U.S. patent application number 15/719084 was filed with the patent office on 2018-04-05 for three-dimensional objects and their formation.
The applicant listed for this patent is Velo3D, Inc.. Invention is credited to Benyamin BULLER, Tasso LAPPAS, Evgeni Levin.
Application Number | 20180093418 15/719084 |
Document ID | / |
Family ID | 61756923 |
Filed Date | 2018-04-05 |
United States Patent
Application |
20180093418 |
Kind Code |
A1 |
LAPPAS; Tasso ; et
al. |
April 5, 2018 |
THREE-DIMENSIONAL OBJECTS AND THEIR FORMATION
Abstract
The present disclosure provides three-dimensional (3D) methods,
apparatuses, software (e.g., non-transitory computer readable
medium), and systems for the formation of at least one desired 3D
object; comprising use of a geometric model, a physics based model,
one or more markers, one or more modes, or any combination thereof.
The disclosure provides reduction of deformation that may be caused
by the forming process of the 3D object.
Inventors: |
LAPPAS; Tasso; (Pasadena,
CA) ; BULLER; Benyamin; (Cupertino, CA) ;
Levin; Evgeni; (Rehovot, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Velo3D, Inc. |
Campbell |
CA |
US |
|
|
Family ID: |
61756923 |
Appl. No.: |
15/719084 |
Filed: |
September 28, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62402634 |
Sep 30, 2016 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B22F 3/1055 20130101;
G06T 2219/2021 20130101; G05B 2219/35134 20130101; B33Y 30/00
20141201; G05B 2219/49007 20130101; B22F 2003/1057 20130101; B28B
17/0081 20130101; Y02P 10/25 20151101; G06F 30/20 20200101; B29C
64/393 20170801; B33Y 50/02 20141201; B28B 1/001 20130101; B29C
64/10 20170801; G06N 20/00 20190101; G06F 30/00 20200101; G06T
19/20 20130101; B33Y 10/00 20141201; G06T 19/00 20130101; G05B
19/4099 20130101 |
International
Class: |
B29C 64/393 20060101
B29C064/393; B33Y 50/02 20060101 B33Y050/02; B29C 64/10 20060101
B29C064/10; G06T 19/00 20060101 G06T019/00 |
Claims
1.-30. (canceled)
31. A method for forming a three-dimensional object, comprising:
(a) forming a test object using a geometric model of the
three-dimensional object, and one or more model markers disposed on
and/or in the geometric model of the three-dimensional object, the
test object having one or more physical markers that correspond to
the one or more model markers; and (b) comparing locations of the
one or more model markers with locations of the one or more
physical markers.
32. The method of claim 31, further comprising (c) generating a
corrected geometric model using the comparing in (b).
33. The method of claim 32, further comprising (d) forming the
three-dimensional object using the corrected geometric model.
34. The method of claim 32, further comprising repeating (a), (b)
and (c) using iteratively adjusted geometric models and a plurality
of test objects until the locations of the one or more model
markers converge with the locations of the one or more physical
markers.
35. The method of claim 31, further comprising generating a physics
model that employs an estimated change of at least one
characteristic of the three-dimensional object present upon
formation of the three-dimensional object.
36. The method of claim 35, further comprising forming a simulated
object employing the physics model.
37. The method of claim 36, further comprising comparing the
simulated object with the test object.
38. The method of claim 36, wherein the physics model employs an
estimated thermo-mechanical change in the three-dimensional object
present upon formation of the three-dimensional object.
39. The method of claim 31, further comprising adding and/or
removing the one or more model markers to the geometric model.
40. The method of claim 31, wherein the one or more model markers
comprise a protrusion, a depression, or a deletion.
41. The method of claim 31, wherein the one or more model markers
comprise tessellation borders, or point clouds.
42. The method of claim 31, wherein the one or more model markers
are positioned on a surface and/or in the geometric model.
43. The method of claim 31, wherein forming comprises printing
using three-dimensional printing.
44. The method of claim 31, wherein the one or more physical
markers comprise a pore, dislocation, crack, microstructure,
crystal structure, or a metallurgical morphology.
45. A system for forming a three-dimensional object, the system
comprising one or more controllers that are collectively or
separately configured to direct: (a) forming a test object using a
geometric model of the three-dimensional object, and one or more
model markers disposed on and/or in the geometric model of the
three-dimensional object, the test object having one or more
physical markers that correspond to the one or more model markers;
and (b) comparing locations of the one or more model markers with
locations of the one or more physical markers.
46. The system of claim 45, wherein forming comprises printing
using three-dimensional printing.
47. The system of claim 45, wherein the system further comprises at
least one sensor configured to sense the one or more physical
markers, wherein the one or more controllers is configured to (i)
control sensing and/or (ii) use sensing data, of the one or more
physical markers.
48. The system of claim 47, wherein the one or more controllers is
configured to (i) control sensing and/or (ii) use sensing data, of
the one or more physical markers after forming of: the
three-dimensional object and/or test object.
49. The system of claim 45, wherein the system further comprises at
least one detector that is operationally coupled to the one or more
controllers, the at least one detector configured to detect as
least one characteristic of the forming.
50. The system of claim 49, wherein the one or more controllers is
configured to control the at least one detector and/or control one
or more process parameters present upon a detection by the at least
one detector.
51. The system of claim 49, wherein the at least one detector is
configured to detect a temperature during the forming, wherein the
one or more controllers is configured to control detection of the
temperature.
52. The system of claim 49, wherein the at least one detector is
configured to detect one or more of cleanliness, pressure,
humidity, and oxygen level of an atmosphere surrounding the
three-dimensional object during the forming.
53. The system of claim 45, wherein the one or more controllers is
configured to direct (c) generating a corrected geometric model
using the comparing in (b).
54. The system of claim 53, wherein the one or more controllers is
configured to direct (d) forming the three-dimensional object using
the corrected geometric model.
55. The system of claim 53, wherein the one or more controllers is
configured to direct repeating (a), (b) and (c) using iteratively
adjusted geometric models and a plurality of test objects, until
locations of the one or more model markers converge with locations
of the one or more physical markers.
56. The system of claim 45, wherein the one or more controllers is
configured to direct generating a physics model that employs an
estimated change of at least one characteristic of the
three-dimensional object present upon formation of the
three-dimensional object.
57. The system of claim 56, further comprising forming a simulated
object employing the physics model.
58. The system of claim 45, wherein the one or more model markers
comprises a protrusion, a depression, or a deletion.
59. The system of claim 45, wherein the one or more model markers
comprise tessellation borders, or point clouds.
60. The system of claim 45, wherein the one or more physical
markers comprise a pore, dislocation, crack, microstructure,
crystal structure, or a metallurgical morphology.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application Ser. No. 62/402,634, filed on Sep. 30, 2016, titled "IN
SITU THREE-DIMENSIONAL OBJECT MARKERS," which is entirely
incorporated herein by reference.
BACKGROUND
[0002] Three-dimensional objects can be made using manufacturing
processes. The manufacturing processes can affect the shape of the
three-dimensional objects in unintended ways. Examples of
manufacturing processes for forming three-dimensional objects
include three-dimensional (3D) printing.
[0003] Three-dimensional (3D) printing (e.g., additive
manufacturing) is a process for making a three-dimensional object
(e.g., of any shape) from a design. The design may be in the form
of a data source such as an electronic data source, or may be in
the form of a hard copy. The hard copy may be a two-dimensional
representation of a 3D object. The data source may be an electronic
3D model. 3D printing may be accomplished through, for example, an
additive process in which successive layers of material are laid
down one on top of another. This process may be controlled (e.g.,
computer controlled, manually controlled, or both). A 3D printer
can be an industrial robot.
[0004] 3D printing can generate custom parts. A variety of
materials can be used in a 3D printing process including elemental
metal, metal alloy, ceramic, elemental carbon, or polymeric
material. In some 3D printing processes (e.g., additive
manufacturing), a first layer of hardened material is formed (e.g.,
by welding powder), and thereafter successive layers of hardened
material are added one by one, wherein each new layer of hardened
material is added on a pre-formed layer of hardened material, until
the entire designed three-dimensional structure (3D object) is
layer-wise materialized.
[0005] 3D models may be created with a computer aided design
package, via 3D scanner, or manually. The manual modeling process
of preparing geometric data for 3D computer graphics may be similar
to plastic arts, such as sculpting or animating. 3D scanning is a
process of analyzing and collecting digital data on the shape and
appearance of a real object (e.g., real-life object). Based on this
data, 3D models of the scanned object can be produced.
[0006] A number of 3D printing processes are currently available.
They may differ in the manner layers are deposited to create the
materialized 3D structure (e.g., hardened 3D structure). They may
vary in the material or materials that are used to materialize the
designed 3D object. Some methods melt, sinter, or soften material
to produce the layers that form the 3D object. Examples for 3D
printing methods include selective laser melting (SLM), selective
laser sintering (SLS), direct metal laser sintering (DMLS) or fused
deposition modeling (FDM). Other methods cure liquid materials
using different technologies such as stereo lithography (SLA). In
the method of laminated object manufacturing (LOM), thin layers
(made inter alia of paper, polymer, or metal) are cut to shape and
joined together.
[0007] Due to the manufacturing (e.g., printing) procedures and/or
materials chosen, some 3D objects may deform during and/or after
their generation. At times it is desirable to print a 3D object
that has a reduced level of deformation. It may be desirable to
form (e.g., print) a 3D object that is substantially similar to the
requested 3D object (e.g., by a client). It may be desirable to
develop a methodology to monitor the forming (e.g., printing) of
the 3D objects.
SUMMARY
[0008] In some embodiments, the present disclosure delineates
methods, systems, apparatuses, and software that allow modeling and
forming of 3D objects with a reduced amount of design constraints
(e.g., no design constraints). The present disclosure delineates
methods, systems, apparatuses, and software that allow
materialization of 3D object and models thereof. Described herein
is also a way of tracking of 3D object formation (e.g., 3D
printing) that may be of assistance in reducing and/or controlling
deformation that occur during formation of a (physical) 3D
object.
[0009] In an aspect is a method for monitoring a three-dimensional
(3D) printing process that comprises (a) generating a prior marked
model (e.g., first marked model) of a requested 3D object by
inserting one or more markers in a model design of the requested 3D
object; (b) forming a prior marked 3D object based on the prior
marked model of the requested 3D object; (c) calculating a
deviation by comparing between: the one or more markers in the
prior marked model of the requested 3D object in (a), and the prior
marked 3D object in (b); and (d) monitoring the 3D printing process
based on the calculating, which one or more markers are
structural.
[0010] In some embodiments, the one or more markers that are
structural comprise depression, protrusion, or deletion as compared
to the requested 3D object. In some embodiments, the deletion is a
hole. In some embodiments, forming comprises using a printing
instruction to form the prior marked 3D object (e.g., first marked
3D object). In some embodiments, the one or more markers are small
such that the printing instruction to form the prior marked 3D
object is substantially similar to a printing instruction to form
the requested 3D object. In some embodiments, substantially is
relative to the intended purpose of the 3D object. In some
embodiments, monitoring comprises adjusting the 3D printing process
based on the calculating. In some embodiments, adjusting comprises:
(i) generating a subsequent marked model (e.g., second marked
model) of a requested 3D object by adjusting the prior marked model
based on the calculating in operation (c); (ii) forming a
subsequent marked 3D object (e.g., second marked 3D object) based
on the subsequent marked model of the requested 3D object; (iii)
calculating a deviation by comparing between: the one or more
markers of the subsequent marked model of the requested 3D object
in (i), and the subsequent marked 3D object in (ii); or (iv)
repeating steps (i) to (iii) based on a deviation value. In some
embodiments, adjusting in (i) is relative to the intended purpose
of the requested 3D object. In some embodiments, adjusting in (i)
comprises corrective adjustment. In some embodiments, adjusting in
(i) comprises geometric adjustment. In some embodiments, adjusting
in (i) comprises structural adjustment. In some embodiments,
adjusting in (i) results in reducing the deviation value. In some
embodiments, the deviation value is (e.g., substantially) based on
the intended purpose of the requested 3D object, and the repeating
in (iv) occurs. In some embodiments, the deviation value is
insubstantial and the repeating in (iv) does not occur. In some
embodiments, the method further comprises forming the requested 3D
object based on the subsequent marked model of the requested 3D
object. Insubstantial can be relative to the intended purpose of
the requested 3D object. In some embodiments, the deviation value
is insubstantial. In some embodiments, the method further comprises
forming the requested 3D object based on the prior marked model of
the requested 3D object. In some embodiments, adjusting results in
a subsequent marked 3D object comprises less auxiliary support as
compared to the prior marked 3D object. In some embodiments, less
is a fewer number of auxiliary support structures. In some
embodiments, less is smaller contact area between the auxiliary
support and the subsequent marked 3D object. In some embodiments,
the method further comprises using the calculating in a simulation.
In some embodiments, the simulation comprises a simulation of the
3D printing process. In some embodiments, the simulation comprises
a simulation of the requested 3D object. In some embodiments, the
simulation comprises a simulation of the marked model of the
requested 3D object. In some embodiments, the simulation comprises
the 3D printing directions. In some embodiments, the simulation
comprises the requested 3D object. In some embodiments, the
simulation comprises the marked model of the requested 3D object.
In some embodiments, the simulation comprises a learning algorithm.
In some embodiments, comparing comprises measuring a fundamental
length scale, shape, or volume of at least one of the one or more
markers of the prior marked 3D object. In some embodiments,
comparing comprises measuring a fundamental length scale, shape, or
volume of at least one of the one or more markers of the prior
marked 3D object and/or of a subsequent marked 3D object (e.g.,
subsequent to the prior marked 3D object). In some embodiments,
comparing comprises metrologically measuring the one or more
markers of the prior marked 3D object. In some embodiments,
comparing comprises metrologically measuring the one or more
markers of the prior marked 3D object and/or of the subsequent
marked 3D object. In some embodiments, metrologically comprises
measuring a distance between at least two markers. In some
embodiments, measuring a distance between at least two markers
comprises measuring a distance between the center of the at least
two markers. In some embodiments, measuring a distance between at
least two markers comprises measuring a distance between the
circumference of the at least two markers. The prior can be
relative to the subsequent. The prior can be first. The subsequent
can be second, third, fourth, etc.
[0011] Another aspect of the present disclosure provides a computer
system comprising one or more computer processors and a
non-transitory computer-readable medium coupled thereto. The
non-transitory computer-readable medium comprises
machine-executable code that, upon execution by the one or more
computer processors, implements any of the methods disclosed
herein.
[0012] In another aspect, a system for monitoring a 3D printing
process, comprises: a first processor that is configured to
generate a prior marked model of a requested 3D object by inserting
one or more markers in a model design of the requested 3D object to
form a marked 3D object; a 3D printer that is configured to print a
prior marked 3D object based on the prior marked model of the
requested 3D object; a second processor that is configured to
calculate a deviation by comparing between: (i) the one or more
markers in the prior marked model of the requested 3D object, and
(ii) the prior marked 3D object; and (d) a third processor that is
configured to monitor the 3D printing process based on the
deviation, which one or more markers are structural, wherein at
least two of the first processor, second processor, third
processor, and 3D printer are operatively coupled.
[0013] In some embodiments, the at least two of the first
processor, second processor, and third processor are the same
processor. In some embodiments, the 3D printer comprises an energy
beam (e.g., laser or electron-beam). In some embodiments, the 3D
printer comprises a layer dispensing mechanism. In some
embodiments, the 3D printer is configured to accommodate a material
bed. In some embodiments, the 3D printing comprises a platform that
is configured to support the 3D object. In some embodiments, the 3D
printer is an additive 3D printer. In some embodiments, the system
further comprises at least one controller that is operatively
coupled to at least one of the 3D printer, first processor, second
processor, and third processor are the same processor. In some
embodiments, the system further comprises a sensor that senses at
least one characteristic of the one or more markers. In some
embodiments, the sensor comprises a temperature or metrology (e.g.,
height) sensor. In some embodiments, the characteristic is a
metrological characteristic.
[0014] In another aspect, an apparatus for printing one or more 3D
objects comprises at least one controller that is programmed to
direct at least one mechanism used in a 3D printing methodology to
implement (e.g., effectuate) any of the method disclosed herein,
wherein one or more of the at least one controller is operatively
coupled to the mechanism.
[0015] In another aspect, at least one controller comprises a
plurality of controllers and wherein at least two of operations
(e.g., at least two of (a), (b), (c) operations) are directed by
the same controller. In some embodiments, at least one controller
comprises a plurality of controllers and wherein at least two
operations (e.g., at least two of (a), (b), (c) operations) are
directed by different controllers. In some embodiments, the at
least two operations may be of a method, a software, and/or
operations programed in a control scheme.
[0016] In another aspect, an apparatus for monitoring a 3D printing
process, comprises: (a) a first controller that is programmed to
direct generating a prior marked model of a requested 3D object by
inserting one or more markers in a model design of the requested 3D
object; (b) a second controller that is programmed to direct
forming a prior marked 3D object based on the prior marked model of
the requested 3D object; (c) a third controller that is programmed
to direct calculating a deviation by comparing between: the one or
more markers in the prior marked model of the requested 3D object
in (a), and the prior marked 3D object in (b); and (d) a fourth
controller that is programmed to direct monitoring the 3D printing
process based on the deviation, which one or more markers are
structural, wherein at least two of the first processor, second
processor, third processor, and 3D printer are operatively
coupled.
[0017] In some embodiments, the at least two of the first
controller, second controller, third controller, and fourth
controller are the same controller. In some embodiments, the at
least two of the first controller, second controller, third
controller, and fourth controller are different controllers. In
some embodiments, the at least one of the first controller, second
controller, third controller, and fourth controller comprises a
proportional-integral-derivative (PID) controller. In some
embodiments, the at least one of the first controller, second
controller, third controller, and fourth controller comprises a
feedback loop. In some embodiments, the at least one of the first
controller, second controller, third controller, and fourth
controller comprises a feed forward loop. In some embodiments, the
at least one of the first controller, second controller, third
controller, and fourth controller comprises a closed loop control
(e.g., based on a sensor signal, e.g., a temperature signal, and/or
a power signal). In some embodiments, the at least one of the first
controller, second controller, third controller, and fourth
controller comprises an open loop control. In some embodiments, the
at least one of the first controller, second controller, third
controller, and fourth controller comprises a real-time controller.
In some embodiments, the at least one of the first controller,
second controller, third controller, and fourth controller
comprises a temperature controller (e.g., controlling the melt pool
temperature, e.g., in real time). In some embodiments, the at least
one of the first controller, second controller, third controller,
and fourth controller comprises a metrology controller (e.g.,
mapping the exposed surface of a material bed and/or 3D object,
e.g., in real time). In some embodiments, the at least one of the
first controller, second controller, third controller, and fourth
controller comprises a power controller (e.g., controlling the
power of the energy source and/or power density of the energy beam,
e.g., in real time).
[0018] In another aspect, a computer software product comprises:
(a) a first non-transitory computer-readable medium in which
program instructions are stored, which instructions, when read by a
first computer, cause the first computer to generate a prior marked
model of a requested 3D object by inserting one or more markers in
a model design of the requested 3D object, wherein the prior marked
model of the requested 3D object is utilized to form a prior marked
3D object based on the prior marked model of the requested 3D
object; and (b) a second non-transitory computer-readable medium in
which program instructions are stored, which instructions, when
read by a second computer, cause the second computer to calculate a
deviation by comparing between: the one or more markers in the
prior marked model of the requested 3D object in (a), and the prior
marked 3D object in (b), wherein the deviation is used to control
(e.g., adjust) the 3D printing process, and wherein one or more
markers are structural.
[0019] In some embodiments, the first non-transitory
computer-readable medium and the second non-transitory
computer-readable medium are the same non-transitory
computer-readable medium. In some embodiments, the first
non-transitory computer-readable medium and the second
non-transitory computer-readable medium are different. In some
embodiments, the first computer and the second computer are the
same. In some embodiments, the first computer and the second
computer are different
[0020] In another aspect, a method for forming a three-dimensional
object comprises comparing one or more model markers with one or
more physical markers, which one or more model markers are disposed
on and/or in a geometric model of the three-dimensional object,
wherein the one or more physical markers are disposed on and/or in
a test object that is formed by employing the geometric model,
which one or more physical markers correspond to the one or more
model markers.
[0021] In another aspect (e.g., that can be related to the one
above), a method for forming a three-dimensional object comprises:
(a) (optionally) forming a test object using a geometric model of
the three-dimensional object, and one or more model markers
disposed on and/or in the geometric model of the three-dimensional
object, the test object having one or more physical markers that
correspond to the one or more model markers; and (b) comparing
(e.g., locations, dimensions, and/or material properties of) the
one or more model markers with (e.g., locations, dimensions, and/or
material properties of) the one or more physical markers.
[0022] In some embodiments, the comparing is of location, shape,
volume, fundamental length scale, and/or a material property. In
some embodiments, the method further comprises operation (c)
generating a corrected geometric model using the comparing in
operation (b). In some embodiments, the method further comprises
operation (d) forming the three-dimensional object using the
corrected geometric model. In some embodiments, the method further
comprises repeating operations (a), (b) and (c) using iteratively
adjusted geometric models and a plurality of test objects until the
locations of the one or more model markers (e.g., substantially)
converge with the locations of the one or more physical markers. In
some embodiments, a predefined location threshold of the physical
markers comprises a vicinity of the one or more physical markers
and the location of the one or more physical markers. In some
embodiments, the locations of the one or more model markers
converge within the predefined location threshold of the one or
more physical markers. In some embodiments, the method further
comprises generating a physics model that employs an estimated
change of at least one characteristic of the three-dimensional
object resulting from the forming. In some embodiments, the method
further comprises forming a simulated object employing the physics
model. In some embodiments, the method further comprises comparing
the simulated object with the test object. In some embodiments,
comparing the simulated object with the test object comprises
comparing one or more dimensions of the simulated object with
respective one or more dimensions of the test object. In some
embodiments, the method further comprises generating a corrected
geometric model employing comparing the simulated object with the
test object. In some embodiments, the method further comprises
forming the three-dimensional object while employing the corrected
geometric model. In some embodiments, the at least one
characteristic of the three-dimensional object comprises a material
property of the three-dimensional object. In some embodiments, the
at least one characteristic of the three-dimensional object
comprises a geometry of the three-dimensional object. In some
embodiments, the physics model employs an estimated thermally
induced change in the three-dimensional object present upon
formation of the three-dimensional object. In some embodiments, the
estimated thermally induced change comprises an estimated
volumetric change in at least a portion of the three-dimensional
object. In some embodiments, the estimated thermally induced change
comprises an estimated expansion or an estimated contraction in at
least a portion of the three-dimensional object. In some
embodiments, the estimated thermally induced change comprises an
estimated change in a microstructure of at least a portion of the
three-dimensional object. In some embodiments, the estimated change
in the microstructure comprises an estimated change in a crystal
structure. In some embodiments, the estimated change in the
microstructure comprises an estimated change in a metallurgical
microstructure. In some embodiments, the physics model employs an
estimated thermo-mechanical change in the three-dimensional object
present upon formation of the three-dimensional object. In some
embodiments, the estimated thermo-mechanical change comprises an
estimated thermoplastic or thermo-elastic change. In some
embodiments, the estimated thermo-mechanical change comprises an
estimated thermo-mechanical deformation. In some embodiments, the
physics model employs an estimated mechanical alteration in the
three-dimensional object present upon formation of the
three-dimensional object. In some embodiments, the estimated
mechanical alteration comprises an estimated inelastic or elastic
change. In some embodiments, inelastic change comprises plastic
change. In some embodiments, the estimated mechanical alteration
comprises mechanical deformation. In some embodiments, the
estimated mechanical alteration comprises a set of modes. In some
embodiments, the method further comprises generating a physics
model employing an estimated alteration in the three-dimensional
object present upon formation of the three-dimensional object. In
some embodiments, the estimated alteration is a deformation. In
some embodiments, the method further comprises comparing a
simulated object with the test object. In some embodiments, the
simulated object is generated using the physics model. In some
embodiments, the method further comprises adding the one or more
model markers to the geometric model. In some embodiments, the
method further comprises removing the one or more model markers
from the geometric model. In some embodiments, the one or more
model markers comprises an induced change to the three-dimensional
object. In some embodiments, the one or more model markers
comprises a protrusion, a depression, or a deletion. In some
embodiments, the one or more model markers comprise tessellation
borders, or point clouds. In some embodiments, the one or more
physical markers comprise a pore, dislocation, crack,
microstructure, crystal structure, or a metallurgical morphology.
In some embodiments, the one or more model markers are positioned
on a surface and/or within a volume of the geometric model. In some
embodiments, (b) comprises performing a data analysis. In some
embodiments, the data analysis comprises at least one of: linear
regression, least squares fit, Gaussian process regression, kernel
regression, nonparametric multiplicative regression (NPMR),
regression trees, local regression, semiparametric regression,
isotonic regression, multivariate adaptive regression splines
(MARS), logistic regression, robust regression, polynomial
regression, stepwise regression, ridge regression, lasso
regression, elasticnet regression, principal component analysis
(PCA), singular value decomposition, fuzzy measure theory, Borel
measure, Han measure, risk-neutral measure, Lebesgue measure, group
method of data handling (GMDH), Naive Bayes classifiers, k-nearest
neighbors algorithm (k-NN), support vector machines (SVMs), neural
networks, support vector machines, classification and regression
trees (CART), random forest, gradient boosting, or generalized
linear model (GLM) technique. In some embodiments, the forming the
three-dimensional object comprises printing the three-dimensional
object using three-dimensional printing. In some embodiments, the
forming the three-dimensional object comprises additively or
substantively forming the three-dimensional object. In some
embodiments, the forming the three-dimensional object comprises
extrusion, molding, or sculpting.
[0023] In another aspect, a system for forming a three-dimensional
object, the system comprising one or more controllers is/are
configured to direct comparing one or more model markers with one
or more physical markers, which one or more model markers are
disposed on and/or in a geometric model of the three-dimensional
object, wherein the one or more physical markers are disposed on
and/or in a test object that is formed by employing the geometric
model, which one or more physical markers correspond to the one or
more model markers.
[0024] In another aspect (e.g., that can be related to the one
above), a system for forming a three-dimensional object, the system
comprising: one or more controllers that are collectively or
separately configured to direct: (a) (optionally) forming a test
object using a geometric model of the three-dimensional object, and
one or more model markers disposed on and/or in the geometric model
of the three-dimensional object, the test object having one or more
physical markers that correspond to the one or more model markers;
and (b) comparing (e.g., locations, dimensions, and/or material
properties of) the one or more model markers with (e.g., locations,
dimensions, and/or material properties of) the one or more physical
markers.
[0025] In some embodiments, the comparing is of location, shape,
volume, fundamental length scale, and/or a material property. In
some embodiments, at least one of the one or more controllers
comprises a feed forward and/or feedback control loop. In some
embodiments, at least one of the one or more controllers comprises
a closed loop and/or open loop control scheme. In some embodiments,
forming the three-dimensional object comprises printing the
three-dimensional object using three-dimensional printing. In some
embodiments, forming the three-dimensional object comprises
additively or substantively forming the three-dimensional object.
In some embodiments, forming the three-dimensional object comprises
extrusion, molding, or sculpting. In some embodiments, the one or
more controllers is further configured to direct operation (c) an
energy beam to transform a pre-transformed material into a
transformed material to form the three-dimensional object. In some
embodiments, operation (c) is during (a). In some embodiments, at
least two of the one or more controllers directing operation (a) to
operation (c) are different controllers. In some embodiments, at
least two of the one or more controllers directing operation (a) to
operation (c) are the same controller. In some embodiments, the one
or more controllers is configured to direct at least one energy
source to generate and direct at least one energy beam at a
pre-transformed material. In some embodiments, the one or more
controllers is further configured to direct operation (d) a
platform to vertically translate, which platform is configured to
support the three-dimensional object. In some embodiments,
operation (d) is during (a). In some embodiments, at least two of
the one or more controllers directing operation (a) to operation
(d) are different controllers. In some embodiments, at least two of
the one or more controllers directing operation (a) to operation
(d) are the same controller. In some embodiments, the system
further comprises a chamber configured to enclose at least a
portion of the three-dimensional object during its formation. In
some embodiments, the one or more controllers is configured to
monitor and/or control a progress of formation of the
three-dimensional object within the chamber. In some embodiments,
the system further comprises at least one sensor configured to
sense the one or more physical markers. In some embodiments, the
one or more controllers is configured to (i) control sensing and/or
(ii) use sensing data, of the one or more physical markers. In some
embodiments, the one or more controllers is configured to (i)
control sensing and/or (ii) use sensing data, of the one or more
physical markers during forming of the three-dimensional object. In
some embodiments, the one or more controllers is configured to (i)
control sensing and/or (ii) use sensing data, of the one or more
physical markers after forming of the three-dimensional object. In
some embodiments, the system further comprises at least one
detector that is operationally coupled to the one or more
controllers, the at least one detector configured to detect as
least one characteristic of the forming. In some embodiments, the
one or more controllers is configured to control the at least one
detector and/or control one or more process parameters present upon
a detection by the at least one detector. In some embodiments, the
at least one detector is configured to detect a temperature during
the forming of the three-dimensional object. In some embodiments,
the one or more controllers is configured to control (e.g.,
monitor) detection of the temperature. In some embodiments, the
temperature corresponds to a temperature of the three-dimensional
object. In some embodiments, the temperature corresponds to a
temperature of a vicinity of the three-dimensional object. In some
embodiments, the vicinity is in a material bed that is configured
to accommodate the three-dimensional object. In some embodiments,
the temperature corresponds to a temperature of an atmosphere
surrounding the three-dimensional object. In some embodiments, the
at least one detector is configured to detect at least one of
cleanliness, pressure, humidity, or oxygen level of an atmosphere
surrounding the three-dimensional object during the forming. In
some embodiments, detecting a cleanliness comprises detecting a
number of particles within at least a processing cone of the
atmosphere. In some embodiments, the one or more controllers
comprise at least two controllers. In some embodiments, the one or
more controllers is one controller. In some embodiments, the one or
more controllers is configured to direct operation (e) generating a
corrected geometric model using the comparing in operation (b). In
some embodiments, the one or more controllers is configured to
direct operation (f) forming the three-dimensional object using the
corrected geometric model. In some embodiments, the one or more
controllers is configured to direct repeating operations (a), (b)
and (e) using iteratively adjusted geometric models and a plurality
of test objects, until locations of the one or more model markers
(e.g., substantially) converge with locations of the one or more
physical markers. In some embodiments, the one or more controllers
is configured to direct generating a physics model that employs an
estimated change of at least one characteristic of the
three-dimensional object resulting from the forming. In some
embodiments, the system further comprises forming a simulated
object employing the physics model. In some embodiments, the
physics model comprises calculating a plurality of modes, each of
the plurality of modes having an associated energy, each of the
plurality of modes representing a plausible alteration component of
the three-dimensional object during a printing operation.
[0026] In another aspect, a computer software product comprising at
least one non-transitory computer-readable medium in which program
instructions are stored, which program instructions, when read by
at least one computer, cause the at least one computer to direct
comparing one or more model markers with one or more physical
markers, which one or more model markers are disposed on and/or in
a geometric model of the three-dimensional object, wherein the one
or more physical markers are disposed on and/or in a test object
that is formed by employing the geometric model, which one or more
physical markers correspond to the one or more model markers.
[0027] In another aspect (e.g., that can be related to the one
above), a computer software product comprising at least one
non-transitory computer-readable medium in which program
instructions are stored, which program instructions, when read by
at least one computer, cause the at least one computer to direct
comparing (i) (e.g., locations, dimensions, and/or material
properties of) one or more model markers of a geometric model that
is used to form a three-dimensional test object with (ii) (e.g.,
locations, dimensions, and/or material properties of) one or more
physical markers of a formed three-dimensional test object, wherein
the one or more model markers are disposed on and/or in the
geometric model of the test three-dimensional object; and the one
or more physical markers correspond to the one or more model
markers.
[0028] In some embodiments, the comparing is of location, shape,
volume, fundamental length scale, and/or a material property. In
some embodiments, the (e.g., successful) test object is a requested
three-dimensional object. In some embodiments, the comparing is
operation (a), and wherein the program instructions further cause
the at least one computer to direct operation (b) forming the
three-dimensional test object using the geometric model of the
three-dimensional test object. In some embodiments, the forming in
(b) further comprises the one or more model markers. In some
embodiments, a non-transitory computer-readable medium causes a
computer to direct operation (a) and operation (b). In some
embodiments, a non-transitory computer-readable medium cause a
first computer to direct operation (a) and a second computer to
direct operation (b). In some embodiments, a first non-transitory
computer-readable medium causes a computer to direct operation (a)
and a second non-transitory computer-readable medium cause the
computer to direct operation (b). In some embodiments, a first
non-transitory computer-readable medium cause a first computer to
direct operation (a) and a second non-transitory computer-readable
medium cause a second computer to direct operation (b). In some
embodiments, the program instructions cause the at least one
computer to direct a feed forward and/or feedback control loop. In
some embodiments, the program instructions cause the at least one
computer to direct a closed loop and/or open loop control scheme.
In some embodiments, operation (b) comprises printing the
three-dimensional test object. In some embodiments, operation (b)
comprises additively or substantively forming the three-dimensional
test object. In some embodiments, operation (b) comprises
extrusion, molding, or sculpting the three-dimensional test object.
In some embodiments, the comparing is operation (a), wherein the
program instructions further cause the at least one computer to
direct: operation (c) forming a requested object while employing
the comparing. In some embodiments, operation (c) comprises
directing an energy beam to transform a pre-transformed material
into a transformed material. In some embodiments, a non-transitory
computer-readable medium cause a computer to direct at least two of
operations (a), (b) and (c). In some embodiments, a non-transitory
computer-readable medium cause each a different computer to direct
at least two of operations (a), (b) and (c). In some embodiments,
different non-transitory computer-readable mediums cause each a
different computer to direct at least two of operations (a), (b)
and (c). In some embodiments, the program instructions cause the at
least one computer to direct: monitoring and/or controlling a
progress of formation of requested object. In some embodiments, the
monitoring and/or controlling comprises directing at least one
sensor to (i) control sensing and/or (ii) use sensing data,
relating to the one or more physical markers. In some embodiments,
the monitoring and/or controlling comprises directing at least one
detector to detect as least one characteristic of forming the
requested object. In some embodiments, the at least one
characteristic of forming the requested object comprises at least
one characteristic of an energy beam. In some embodiments, the
program instructions cause the at least one computer to direct:
repeating operations (a) and (b) using iteratively adjusted
geometric models and a plurality of three-dimensional test objects,
until locations of the one or more model markers (e.g.,
substantially) converge with locations of the one or more physical
markers. In some embodiments, the program instructions cause the at
least one computer to direct: generating a physics model that
employs an estimated change of at least one characteristic of the
three-dimensional object resulting from forming the
three-dimensional test object. In some embodiments, the program
instructions cause the at least one computer to direct: forming a
simulated object employing the physics model. In some embodiments,
the physics model comprises calculating a plurality of modes, each
of the plurality of modes having an associated energy, each of the
plurality of modes representing a plausible alteration component of
the three-dimensional test object during forming of the
three-dimensional test object.
[0029] In another aspect, a method for generating a
three-dimensional object, comprising: (A) generating a physics
model that employs a geometric model of the three-dimensional
object; (B) computing a plurality of modes using the physics model,
each of the plurality of modes having an associated energy, each of
the plurality of modes representing a plausible alteration
component of the three-dimensional object during the generating;
and (C) generating the three-dimensional object while employing a
corrected geometric model that is generated using at least a
fraction of the plurality of modes.
[0030] In another aspect (e.g., that can be related to the one
above), a method for generating a three-dimensional object,
comprising: (a) generating a physics model that employs a geometric
model of the three-dimensional object; (b) computing a plurality of
modes using the physics model, each of the plurality of modes
having an associated energy, each of the plurality of modes
representing a plausible alteration component of the
three-dimensional object during the generating; (c) (optionally)
identifying one or more prominent modes having associated energies
of at most a predetermined threshold; and (d) (optionally)
generating the three-dimensional object while employing a corrected
geometric model that is generated using at least a fraction of the
plurality of modes (e.g., that comprise the one or more prominent
modes).
[0031] In some embodiments, the generating comprises printing the
three-dimensional object using three-dimensional printing. In some
embodiments, the generating comprises additively or substantively
forming the three-dimensional object. In some embodiments, the
generating comprises extrusion, molding, or sculpting. In some
embodiments, computing the plurality of modes comprises using one
or more singular value decomposition calculations. In some
embodiments, the method further comprises generating a virtual
image of a test object that is a generated three-dimensional
object. In some embodiments, the generated three-dimensional object
does not employ the corrected geometric model. In some embodiments,
the generated three-dimensional object employs the corrected
geometric model that is generated using the at least a fraction of
the plurality of modes (e.g., a portion of the plurality of modes).
In some embodiments, the geometric model is generated by comparing
the at least a fraction of the plurality of modes (e.g., the one or
more prominent modes) with the virtual image. In some embodiments,
at least a fraction of the plurality of modes correspond to one or
more thermomechanical modes. In some embodiments, the plurality of
modes are computed employing at least one estimated alteration of
the three-dimensional object. In some embodiments, the at least one
estimated alteration employs an estimated mechanical alteration in
the three-dimensional object. In some embodiments, the estimated
mechanical alteration comprises an estimated inelastic or an
estimated elastic deformation. In some embodiments, the estimated
elastic deformation comprises an estimated nonlinear elastic
alteration of the three-dimensional object. In some embodiments,
identifying the fraction of the plurality of modes (e.g., the one
or more prominent modes) comprises organizing the plurality of
modes while employing their associated (e.g., relative) energies.
In some embodiments, identifying the fraction of the plurality of
modes (e.g., the one or more prominent modes) comprises filtering
out modes having associated energies that are higher than the
predetermined threshold. In some embodiments, the method further
comprises adjusting the physics model employing comparing the at
least a fraction of the plurality of modes with the virtual image.
In some embodiments, generating the virtual image comprises
scanning the test object. In some embodiments, the test object
corresponds to a requested three-dimensional object. In some
embodiments, the geometric model of the three-dimensional object
comprises one or more model markers. In some embodiments, (a), (b),
(c), or any combination thereof, occur during a three-dimensional
object generation operation. In some embodiments, the
three-dimensional object generation operation comprises
three-dimensional printing, molding, extruding, sculpting, or
carving. In some embodiments, the three-dimensional object
generation operation comprises additively or substantively
generating the three-dimensional object. In some embodiments, one
or more of the modes materialize as a result from an elastic
response to inelastic forcing during the generating of the
three-dimensional object. In some embodiments, the physics model
comprises an inelastic strain or elastic strain component. In some
embodiments, the physics model comprises an inelastic stress or
elastic stress component. In some embodiments, the physics model
comprises a nonlinear stress/strain component. In some embodiments,
the physics model comprises calculation of a total stress/strain in
the three-dimensional object following the generating of the
three-dimensional object. In some embodiments, the physics model
comprises calculation of an inelastic stress/strain in the
three-dimensional object following the generating of the
three-dimensional object. In some embodiments, the test object
manifests an inelastic response in the three-dimensional object. In
some embodiments, the test object comprises an inelastic response
to the generating of the three-dimensional object.
[0032] In another aspect, a system for forming a three-dimensional
object, the system comprising at least one controller configured to
direct: (A) generating a physics model that employs a geometric
model of the three-dimensional object; (B) computing a plurality of
modes using the physics model, each of the plurality of modes
having an associated energy, each of the plurality of modes
representing a plausible alteration component of the
three-dimensional object during the forming; and (C) generating the
three-dimensional object while employing a corrected geometric
model that is generated using at least a fraction of the plurality
of modes.
[0033] In another aspect (e.g., that can be related to the one
above), a system for forming a three-dimensional object, the system
comprising at least one controller configured to direct: (a)
generating a physics model that employs a geometric model of the
three-dimensional object; (b) computing a plurality of modes using
the physics model, each of the plurality of modes having an
associated energy, each of the plurality of modes representing a
plausible alteration component of the three-dimensional object
during the forming; (c) (optionally) identifying one or more
prominent modes having associated energies of at most a
predetermined threshold; and (d) (optionally) generating the
three-dimensional object while employing a corrected geometric
model that is generated using at least a fraction of the plurality
of modes (e.g., that comprise the one or more prominent modes).
[0034] In some embodiments, at least one of the at least one
controller comprises a feed forward and/or feedback control loop.
In some embodiments, at least one of the at least one controller
comprises a closed loop and/or open loop control scheme. In some
embodiments, forming the three-dimensional object comprises
printing the three-dimensional object using three-dimensional
printing. In some embodiments, forming the three-dimensional object
comprises additively or substantively forming the three-dimensional
object. In some embodiments, forming the three-dimensional object
comprises extrusion, molding, or sculpting. In some embodiments,
the at least one controller is configured to direct an energy beam
to transform a pre-transformed material into a transformed material
to generate the three-dimensional object. In some embodiments, at
least two of the at least one controller directing (a), (b), (c) or
(d) are different controllers. In some embodiments, at least two of
the at least one controller directing (a), (b), (c) or (d) are the
same controller. In some embodiments, the at least one controller
is configured to direct at least one energy source to generate and
direct at least one energy beam at a pre-transformed material. In
some embodiments, the at least one controller is further configured
to direct (e) a platform to vertically translate, which platform is
configured to support the three-dimensional object. In some
embodiments, (e) is during (a) and/or (d). In some embodiments, at
least two of (a), (b), (c), (d), and (e) are directed by different
controllers. In some embodiments, at least two of (a), (b), (c),
(d), and (e) are directed by the same controller. In some
embodiments, the system further comprises a chamber configured to
enclose at least a portion of the three-dimensional object during
forming. In some embodiments, the at least one controller is
configured to monitor and/or control a progress the forming of the
three-dimensional object in the chamber. In some embodiments, the
system further comprises at least one sensor configured to sense
one or more physical markers of the three-dimensional object. In
some embodiments, the at least one controller is configured to (i)
control sensing and/or (ii) use a sensing data, of the one or more
physical markers, which sensing data is obtained by the at least
one sensor. In some embodiments, the at least one controller is
configured to (i) control sensing and/or (ii) use a sensing data,
of the one or more physical markers during forming of the
three-dimensional object. In some embodiments, the at least one
controller is configured to (i) control sensing and/or (ii) use a
sensing data, of the one or more physical markers after forming of
the three-dimensional object. In some embodiments, the system
further comprises at least one detector that is operationally
coupled to the at least one controller, the at least one detector
configured to detect as least one characteristic of the forming. In
some embodiments, the at least one controller is configured to
control the at least one detector and/or control one or more
process parameters present upon a detecting by the at least one
detector. In some embodiments, the at least one detector is
configured to detect a temperature during forming of the
three-dimensional object. In some embodiments, the at least one
controller is configured to control the detecting. In some
embodiments, the temperature corresponds to a temperature of the
three-dimensional object. In some embodiments, the temperature
corresponds to a temperature of a vicinity of the three-dimensional
object. In some embodiments, the temperature corresponds to a
temperature of an atmosphere surrounding the three-dimensional
object. In some embodiments, the at least one detector is
configured to detect at least one of cleanliness, pressure,
humidity, or oxygen level of an atmosphere surrounding the
three-dimensional object during a forming operation. In some
embodiments, detecting a cleanliness comprises detecting an amount
of particles within at least a processing cone of the atmosphere.
In some embodiments, the at least one controller comprises at least
two controllers. In some embodiments, the at least one controller
is one controller. In some embodiments, identifying the fraction of
the plurality of modes (e.g., one or more prominent modes)
comprises filtering out modes having associated energies that are
higher than the predetermined threshold. In some embodiments,
employing a corrected geometric model that is generated using the
at least a fraction of the plurality of modes comprises adjusting
the physics model employing comparing the at least a fraction of
the plurality of modes with a virtual image of a test object.
[0035] In another aspect, a computer software product comprising at
least one non-transitory computer-readable medium in which program
instructions are stored, which program instructions, when read by
at least one computer, cause the at least one computer to direct:
(A) generating a physics model that employs a geometric model of a
three-dimensional object; and (B) computing a plurality of modes
using the physics model, each of the plurality of modes having an
associated energy, each of the plurality of modes representing a
plausible alteration component of the three-dimensional object
during formation of the three-dimensional object.
[0036] In another aspect (e.g., that can be related to the one
above), a computer software product comprising at least one
non-transitory computer-readable medium in which program
instructions are stored, which program instructions, when read by
at least one computer, cause the at least one computer to direct:
(a) generating a physics model that employs a geometric model of a
three-dimensional object; (b) computing a plurality of modes using
the physics model, each of the plurality of modes having an
associated energy, each of the plurality of modes representing a
plausible alteration component of the three-dimensional object
during formation of the three-dimensional object; and (c)
(optionally) identifying a fraction of the plurality of modes
(e.g., comprising one or more prominent modes) having associated
energies of at most a predetermined threshold.
[0037] In some embodiments, the plurality of modes are computed
employing at least one estimated alteration of the
three-dimensional object. In some embodiments, the at least a
fraction of the plurality of modes correspond to one or more
thermo-mechanical modes. In some embodiments, the at least one
estimated alteration employs an estimated mechanical alteration in
the three-dimensional object. In some embodiments, the estimated
mechanical alteration comprises an estimated inelastic or an
estimated elastic deformation. In some embodiments, the computer
software product of the estimated elastic deformation comprises an
estimated nonlinear elastic alteration of the three-dimensional
object. In some embodiments, identifying the fraction of the
plurality of modes (e.g., comprising the one or more prominent
modes) comprises organizing the plurality of modes while employing
their associated (e.g., relative and/or normalized) energies. In
some embodiments, identifying the fraction of the plurality of
modes (e.g., including the one or more prominent modes) comprises
filtering out modes having associated energies that are higher than
the predetermined threshold. In some embodiments, the computer
software product further comprises adjusting the physics model
employing comparing the at least a fraction of the plurality of
modes (e.g., comprising the one or more prominent modes) with a
virtual image of a test object. In some embodiments, the test
object corresponds to a requested three-dimensional object. In some
embodiments, one or more of the modes materialize as a result from
an elastic response to inelastic forcing during forming of the
three-dimensional object. In some embodiments, the physics model
comprises an inelastic strain or elastic strain component. In some
embodiments, the physics model comprises an inelastic stress or
elastic stress component. In some embodiments, the physics model
comprises a nonlinear stress/strain component. In some embodiments,
the physics model comprises calculation of a total stress/strain in
the three-dimensional object following forming of the
three-dimensional object. In some embodiments, the physics model
comprises calculation of an inelastic stress/strain in the
three-dimensional object following forming of the three-dimensional
object. In some embodiments, the geometric model of the
three-dimensional object comprises one or more model markers. In
some embodiments, a non-transitory computer-readable medium cause a
computer to direct operations (a), (b) and (c). In some
embodiments, a non-transitory computer-readable medium cause a
plurality of computers to direct operations (a), (b) and (c). In
some embodiments, a plurality of non-transitory computer-readable
mediums cause a computer to direct operations (a), (b) and (c). In
some embodiments, a plurality of non-transitory computer-readable
medium cause a plurality of computers to direct operations (a), (b)
and operation (c). In some embodiments, the program instructions
further cause the at least one computer to direct operation (d)
forming the three-dimensional object. In some embodiments,
operation (d) comprises printing the three-dimensional object using
three-dimensional printing. In some embodiments, operation (d)
comprises additively or substantively forming the three-dimensional
object. In some embodiments, operation (d) comprises extrusion,
molding, or sculpting the three-dimensional object. In some
embodiments, operation (d) comprises directing an energy beam to
transform a pre-transformed material into a transformed material.
In some embodiments, forming the three-dimensional object is while
employing a corrected geometric model that is generated using at
least a fraction of the plurality of modes. In some embodiments, a
non-transitory computer-readable medium cause a computer to direct
at least two of operations (a), (b), (c) and (d). In some
embodiments, different non-transitory computer-readable mediums
cause a computer to direct at least two of operations (a), (b), (c)
and (d). In some embodiments, different non transitory
computer-readable mediums cause different computers to direct at
least two of operations (a), (b), (c) and (d). In some embodiments,
different non-transitory computer-readable mediums cause a computer
to direct at least three of operations (a), (b), (c) and (d). In
some embodiments, the program instructions cause the at least one
computer to direct a feed forward and/or feedback control loop. In
some embodiments, the program instructions cause the at least one
computer to direct a closed loop and/or open loop control scheme.
In some embodiments, the program instructions cause the at least
one computer to direct: monitoring and/or controlling a progress of
forming the three-dimensional object. In some embodiments, the
monitoring and/or controlling comprises directing at least one
sensor to (i) control sensing and/or (ii) use sensing data, of one
or more physical markers of the three-dimensional object. In some
embodiments, the monitoring and/or controlling comprises directing
at least one detector to detect as least one characteristic of
forming a requested object.
[0038] In another aspect, a method for forming a three-dimensional
object, comprises adjusting a physics model to form an adjusted
physics model, which physical model employs an estimated alteration
in the three-dimensional object present upon formation of the
three-dimensional object, which adjusting is while employing a
comparison between (i) a simulated object and (ii) an image of a
test object that is formed using programmed (e.g., computer)
instructions, which programmed instructions employ (I) a geometric
model of the three-dimensional object, (II) a material property of
the three-dimensional object, or (III) any combination thereof,
wherein the simulated object is generated using the physics model,
wherein (1) the test object comprises one or more markers, (2) the
physics model comprises a plurality of modes each of which
represents a plausible alteration component of the
three-dimensional object present upon formation of the
three-dimensional object, or (3) any combination of (1) and
(2).
[0039] In another aspect (e.g., that can be related to the one
above), a method for forming a three-dimensional object, comprises:
(a) generating a simulated object using a physics model that
employs an estimated alteration in the three-dimensional object
present upon formation of the three-dimensional object; (b) forming
a test object while employing the physics model, wherein (1) the
test object comprises one or more markers, (2) the physics model
comprises a plurality of modes each of which represents a plausible
alteration component of the three-dimensional object present upon
formation of the three-dimensional object, or (3) any combination
of (1) and (2); and (c) adjusting the physics model to form an
adjusted physics model, which adjusting is while employing a
comparison between (i) the simulated object and (ii) an image of
the test object that is formed using programmed (e.g., computer)
instructions, which programmed instructions employ (I) a geometric
model of the three-dimensional object, (II) a material property of
the three-dimensional object, or (III) any combination thereof.
[0040] In some embodiments, the forming comprises printing (e.g.,
the three-dimensional object) using three-dimensional printing. In
some embodiments, the forming comprises additively or substantively
forming the three-dimensional object. In some embodiments, the
forming comprises extrusion, molding, or sculpting. In some
embodiments, adjusting the physics model is continuous. In some
embodiments, the adjusting in operation (c) is a learning module.
In some embodiments, the learning module comprises an inelastic
response to generating the three-dimensional object. In some
embodiments, the learning module comprises a learning algorithm. In
some embodiments, the method further comprises operation (d)
generating the three-dimensional object using program (e.g.,
computer) instruction employing the adjusted physics model. In some
embodiments, the generated three-dimensional object is a requested
three-dimensional object. In some embodiments, the comparison
employs comparing at least one predicted deformation of the
simulated object with at least one deformation of the test object.
In some embodiments, adjusting the physics model is iterative. In
some embodiments, the method further comprises iteratively
repeating operations (a), (b) and (c). In some embodiments, the
method further comprises iteratively repeating operations (a), (b)
and (c) until one or more dimensions of the test object corresponds
to an acceptable dimensional accuracy range relating to a requested
three-dimensional object. In some embodiments, the method that is
acceptable is determined by industrial standard. In some
embodiments, the industrial standard relates to the
three-dimensional object. In some embodiments, the industrial
standard relates to an industry in which the three-dimensional
object is to be used. In some embodiments, the industrial standard
relates to an intended use of the three-dimensional object. In some
embodiments, the acceptable dimensional accuracy range corresponds
to a predetermined threshold range. In some embodiments, the
comparison employs performing at least one of: linear regression,
least squares fit, Gaussian process regression, kernel regression,
nonparametric multiplicative regression (NPMR), regression trees,
local regression, semiparametric regression, isotonic regression,
multivariate adaptive regression splines (MARS), logistic
regression, robust regression, polynomial regression, stepwise
regression, ridge regression, lasso regression, elasticnet
regression, principal component analysis (PCA), singular value
decomposition, fuzzy measure theory, Borel measure, Harr measure,
risk-neutral measure, Lebesgue measure, group method of data
handling (GMDH), Naive Bayes classifiers, k-nearest neighbors
algorithm (k-NN), support vector machines (SVMs), neural networks,
support vector machines, classification and regression trees
(CART), random forest, gradient boosting, or generalized linear
model (GLM) technique. In some embodiments, the comparison
comprises performing a regression analysis. In some embodiments,
the regression analysis comprises a least squares fit analysis. In
some embodiments, the image of the test object is a virtual
three-dimensional image. In some embodiments, the image of the test
object is a computer generated three-dimensional image. In some
embodiments, the test object is formed using a three-dimensional
printing operation. In some embodiments, the test object is
generated using a molding operation. In some embodiments, the test
object is generated using a machining operation. In some
embodiments, the test object is generated using a sculpting. In
some embodiments, the test object comprises additive generation. In
some embodiments, the test object comprises subtractive generation.
In some embodiments, the image of the test object comprises image
markers corresponding to physical markers of the test object. In
some embodiments, the estimated alteration employs a predicted
change of at least one characteristic of the three-dimensional
object. In some embodiments, the estimated alteration employs at
least one physics-based calculation. In some embodiments, the
estimated alteration employs a thermo-mechanical analysis, the
material property of the three-dimensional object, continuum
mechanics, at least one characteristic of an energy beam, the
geometric model of the three-dimensional object, or any suitable
combination thereof. In some embodiments, the physics model
includes modes. In some embodiments, the modes correspond to
predicted elastic deformation modes of the three-dimensional
object. In some embodiments, the physics model is a reduced physics
model. In some embodiments, the physics model is an expanded
physics model. In some embodiments, at least one of operations (a),
(b) and (c) occurs during the forming. In some embodiments, at
least two of operations (a), (b) and (c) occur during the forming.
In some embodiments, operations (a), (b) and (c) occur during the
forming.
[0041] In another aspect, a system for forming a three-dimensional
object, the system comprising at least one controller configured to
direct adjusting a physics model to form an adjusted physics model,
which physical model employs an estimated alteration in the
three-dimensional object present upon formation of the
three-dimensional object, which adjusting is while employing a
comparison between (i) the simulated object and (ii) an image of
the test object that is formed using instructions, which
instructions employ (I) a geometric model of the three-dimensional
object, (II) a material property of the three-dimensional object,
or (III) any combination thereof, wherein the simulated object is
generated using the physics model, wherein (1) the test object
comprises one or more markers, (2) the physics model comprises a
plurality of modes each of which represents a plausible alteration
component of the three-dimensional object present upon formation of
the three-dimensional object, or (3) any combination of (1) and
(2).
[0042] In another aspect (e.g., that can be related to the one
above), a system for forming a three-dimensional object, the system
comprising at least one controller configured to direct: (a)
generating a simulated object using a physics model employing an
estimated alteration in the three-dimensional object present upon
formation of the three-dimensional object; (b) generating a test
object while employing the physics model, wherein (1) the test
object comprises one or more markers, (2) the physics model
comprises a plurality of modes each of which representing a
plausible alteration component of the three-dimensional object
during the forming, or (3) any combination of (1) and (2); and (c)
adjusting the physics model to form an adjusted physics model,
which adjusting is while employing a comparison between (i) the
simulated object and (ii) an image of the test object that is
formed using instructions, which instructions employ (I) a
geometric model of the three-dimensional object, (II) a material
property of the three-dimensional object, or (III) any combination
thereof.
[0043] In some embodiments, at least one of the at least one
controller comprises a feed forward and/or feedback control loop.
In some embodiments, at least one of the at least one controller
comprises a closed loop and/or open loop control scheme. In some
embodiments, forming the three-dimensional object comprises
printing the three-dimensional object using three-dimensional
printing. In some embodiments, forming the three-dimensional object
comprises additively or substantively forming the three-dimensional
object. In some embodiments, forming the three-dimensional object
comprises extrusion, molding, or sculpting. In some embodiments,
the at least one controller is configured to direct an energy beam
to transform a pre-transformed material into a transformed material
to generate the three-dimensional object. In some embodiments, at
least two of operations (a), (b) and (c) are controlled by at least
two different controllers. In some embodiments, at least two of
operations (a), (b) and (c) are controlled by one controller. In
some embodiments, the at least one controller is configured to
direct at least one energy source to generate and direct at least
one energy beam at a pre-transformed material. In some embodiments,
the at least one controller is further configured to direct a
platform to vertically translate, which platform is configured to
support the three-dimensional object. In some embodiments,
directing the platform is during forming of the three-dimensional
object. In some embodiments, the system further comprises a chamber
configured to enclose at least a portion of the three-dimensional
object during forming. In some embodiments, the at least one
controller is configured to monitor and/or control a progress of
the forming within the chamber. In some embodiments, the system
further comprises at least one sensor configured to sense one or
more physical markers of the three-dimensional object. In some
embodiments, the at least one controller is configured to (i)
control sensing and/or (ii) use sensing data, of the one or more
physical markers. In some embodiments, the at least one controller
is configured to (i) control sensing and/or (ii) use sensing data,
of the one or more physical markers during forming of the
three-dimensional object. In some embodiments, the at least one
controller is configured to (i) control sensing and/or (ii) use
sensing data, of the one or more physical markers after forming of
the three-dimensional object. In some embodiments, the system
further comprises at least one detector that is operationally
coupled to the at least one controller, the at least one detector
configured to detect as least one characteristic of the forming. In
some embodiments, the at least one controller is configured to
control the at least one detector and/or control one or more
process parameters present upon detecting by the at least one
detector. In some embodiments, the at least one detector is
configured to detect a temperature during forming of the
three-dimensional object. In some embodiments, the at least one
controller is configured to control (e.g., monitor) detection of
the temperature. In some embodiments, the temperature corresponds
to a temperature of the three-dimensional object. In some
embodiments, the temperature corresponds to a temperature of an
atmosphere surrounding the three-dimensional object. In some
embodiments, the temperature corresponds to a temperature of a
vicinity of the three-dimensional object. In some embodiments, the
vicinity is in a material bed that is configured to accommodate the
three-dimensional object. In some embodiments, the at least one
detector is configured to detect at least one of cleanliness,
pressure, humidity, or oxygen level of an atmosphere surrounding
the three-dimensional object during the forming. In some
embodiments, detecting a cleanliness comprises detecting an amount
of particles within at least a processing cone of the atmosphere.
In some embodiments, the at least one controller comprises at least
two controllers. In some embodiments, the at least one controller
is one controller. In some embodiments, the at least one controller
is configured to direct iteratively repeating operations (a), (b)
and (c). In some embodiments, the at least one controller is
configured to direct iteratively repeating operations (a), (b) and
(c) until one or more dimensions of the test object corresponds to
an acceptable dimensional accuracy range relating to a requested
three-dimensional object. In some embodiments, the comparison
employs performing at least one of: linear regression, least
squares fit, Gaussian process regression, kernel regression,
nonparametric multiplicative regression (NPMR), regression trees,
local regression, semiparametric regression, isotonic regression,
multivariate adaptive regression splines (MARS), logistic
regression, robust regression, polynomial regression, stepwise
regression, ridge regression, lasso regression, elasticnet
regression, principal component analysis (PCA), singular value
decomposition, fuzzy measure theory, Borel measure, Harr measure,
risk-neutral measure, Lebesgue measure, group method of data
handling (GMDH), Naive Bayes classifiers, k-nearest neighbors
algorithm (k-NN), support vector machines (SVMs), neural networks,
support vector machines, classification and regression trees
(CART), random forest, gradient boosting, or generalized linear
model (GLM) technique.
[0044] In another aspect, a computer software product comprising at
least one non-transitory computer-readable medium in which program
instructions are stored, which program instructions, when read by
at least one computer, cause the at least one computer to direct
adjusting a physics model to form an adjusted physics model, which
physical model employs an estimated alteration in the
three-dimensional object present upon formation of the
three-dimensional object, which adjusting is while employing a
comparison between (i) a simulated object and (ii) an image of a
test object that is formed using programmed (e.g., computer)
instructions, which programmed instructions employ (I) a geometric
model of the three-dimensional object, (II) a material property of
the three-dimensional object, or (III) any combination thereof,
wherein the simulated object is generated using the physics model,
wherein (1) the test object comprises one or more markers, (2) the
physics model comprises a plurality of modes each of which
represents a plausible alteration component of the
three-dimensional object present upon formation of the
three-dimensional object, or (3) any combination of (1) and
(2).
[0045] In another aspect (e.g., that can be related to the one
above), a computer software product comprising at least one
non-transitory computer-readable medium in which program
instructions are stored, which program instructions, when read by
at least one computer, cause the at least one computer to direct:
generating a simulated object using a physics model employing an
estimated alteration in a three-dimensional object present upon
formation of the three-dimensional object; forming an adjusted
physics model by adjusting the physics model, which employs a
comparison between (i) the simulated object and (ii) an image of a
test object formed using forming instructions, which forming
instructions employ (I) a geometric model of the three-dimensional
object, (II) a material property of the three-dimensional object,
or (III) a combination of (I) and (II), wherein (1) the test object
comprises one or more markers, (2) the physics model comprises a
plurality of modes each of which representing a plausible
alteration component of the three-dimensional object during the
forming, or (3) a combination of (1) and (2), wherein the forming
instructions are programed (e.g., computer) instructions.
[0046] In some embodiments, the program instructions further cause
the at least one computer to direct: forming the test object while
employing the physics model. In some embodiments, generating a
simulated object is operation (a), forming an adjusted physics
model is operation (b), and forming the test object is operation
(c), wherein the program instructions further cause the at least
one computer to direct: iteratively repeating operations (a), (b)
and (c) until one or more dimensions of the test object corresponds
to an acceptable dimensional accuracy range relating to a requested
three-dimensional object. In some embodiments, forming the test
object comprises printing the three-dimensional object using
three-dimensional printing. In some embodiments, forming the test
object comprises additively or substantively forming the
three-dimensional object. In some embodiments, forming the test
object comprises extrusion, molding, or sculpting the
three-dimensional object. In some embodiments, the program
instructions cause the at least one computer to direct a feed
forward and/or feedback control loop. In some embodiments, the
program instructions cause the at least one computer to direct a
closed loop and/or open loop control scheme. In some embodiments,
adjusting the physics model is continuous. In some embodiments, the
computer software product is continuous in real time and occurs
during at least a fraction of the formation of the test object
(e.g., is continuous in at least a fraction of the formation). In
some embodiments, the printing comprises using an energy beam. In
some embodiments, the program instructions cause the at least one
computer to direct an energy beam to transform a pre-transformed
material into a transformed material to generate the
three-dimensional object and/or the test object. In some
embodiments, generating a simulated object is operation (a), and
forming an adjusted physics model is operation (b). In some
embodiments, the at least one non-transitory computer-readable
medium causes at least computer to direct operations (a) and (b)
individually or collectively. In some embodiments, a non-transitory
computer-readable medium causes a computer to direct operations (a)
and (b). In some embodiments, a non-transitory computer-readable
medium causes a first computer to direct operation (a) and a second
computer to direct operation (b), wherein the first computer is
different from the second computer. In some embodiments, a first
non-transitory computer-readable medium cause a computer to direct
operation (a) and a second non-transitory computer-readable medium
cause the computer to direct operation (b), wherein the first
non-transitory computer-readable medium is different from the
second non-transitory computer-readable medium. In some
embodiments, a first non-transitory computer-readable medium cause
a first computer to direct operation (a) and a second
non-transitory computer-readable medium cause a second computer to
direct operation (b), wherein the first non-transitory
computer-readable medium is different from the second
non-transitory computer-readable medium, and wherein the first
computer is different from the second computer. In some
embodiments, the program instructions further cause the at least
one computer to direct: iteratively repeating operations (a) and
(b). In some embodiments, the comparison employs performing at
least one of: linear regression, least squares fit, Gaussian
process regression, kernel regression, nonparametric multiplicative
regression (NPMR), regression trees, local regression,
semiparametric regression, isotonic regression, multivariate
adaptive regression splines (MARS), logistic regression, robust
regression, polynomial regression, stepwise regression, ridge
regression, lasso regression, elasticnet regression, principal
component analysis (PCA), singular value decomposition, fuzzy
measure theory, Borel measure, Harr measure, risk-neutral measure,
Lebesgue measure, group method of data handling (GMDH), Naive Bayes
classifiers, k-nearest neighbors algorithm (k-NN), support vector
machines (SVMs), neural networks, support vector machines,
classification and regression trees (CART), random forest, gradient
boosting, or generalized linear model (GLM) technique. In some
embodiments, the estimated alteration employs a thermo-mechanical
analysis, a material property of the three-dimensional object,
continuum mechanics, at least one characteristic of an energy beam
used to form the three-dimensional object, the geometric model of
the three-dimensional object, or any suitable combination
thereof.
[0047] In another aspect, a method for determining a strain and/or
a stress in a three-dimensional object, comprising: (A) computing a
plurality of modes employing a geometric model of a requested
three-dimensional object, the plurality of modes employing an
estimated mechanical alteration of the geometric model generated
during formation of the three-dimensional object, each of the
plurality of modes representing a plausible alteration component of
the three-dimensional object during a forming of the
three-dimensional object; (B) generating image data from the
three-dimensional object; and (C) calculating the strain and/or the
stress in the three-dimensional object by comparing the plurality
of modes with the image data, wherein the three-dimensional object
is formed while employing the geometric model.
[0048] In another aspect (e.g., relating to the above aspect), a
method for determining a strain and/or a stress in a
three-dimensional object, comprising: (a) computing a plurality of
modes employing a geometric model of a requested three-dimensional
object, the plurality of modes employing an estimated mechanical
alteration of the geometric model generated during formation of the
three-dimensional object, each of the plurality of modes
representing a plausible alteration component of the
three-dimensional object during a forming of the three-dimensional
object; (b) (optionally) forming the three-dimensional object while
employing the geometric model; (c) generating image data from the
three-dimensional object; and (d) calculating the strain and/or the
stress in the three-dimensional object by comparing the plurality
of modes with the image data.
[0049] In some embodiments, each of the plurality of modes is
associated with an energy, the method further comprises identifying
a fraction of the plurality of modes (e.g., one or more prominent
modes) from the plurality of modes. In some embodiments,
identifying comprises employing the associated energy of each of
the plurality of modes. In some embodiments, the fraction of the
plurality of modes (e.g., one or more prominent modes) have
associated energies of at most a predetermined threshold. In some
embodiments, the forming comprises printing (e.g., the
three-dimensional object) using three-dimensional printing. In some
embodiments, the forming comprises additively or substantively
forming the three-dimensional object. In some embodiments, the
forming comprises extrusion, molding, or sculpting. In some
embodiments, the estimated mechanical alteration is predicted by
one or more nonlinear mechanical strain modes. In some embodiments,
the one or more nonlinear mechanical strain modes comprise elastic
or inelastic strain modes. In some embodiments, alteration
comprises deformation. In some embodiments, the method further
comprises identifying a fraction of the plurality of modes (e.g.,
one or more prominent modes) from the plurality of modes. In some
embodiments, the fraction of the plurality of modes have associated
energies of at most a predetermined threshold. In some embodiments,
the method further comprises calculating a strain by comparing at
least a fraction of the plurality of modes with image data. In some
embodiments, the image data corresponds to a virtual image. In some
embodiments, the method further comprises forming a requested
three-dimensional object while employing a corrected geometric
model that is generated using the at least a fraction of the
plurality of modes. In some embodiments, computing the plurality of
modes comprises using one or more calculations using at least one
of singular value decomposition, Kosambi-Karhunen-Loeve transform
(KLT), Hotelling transform, proper orthogonal decomposition (POD),
eigenvalue decomposition (EVD), factor analysis, Eckart-Young
theorem, Schmidt-Mirsky theorem, empirical orthogonal functions
(EOF), empirical eigenfunction decomposition, empirical component
analysis, quasiharmonic modes, spectral decomposition, or empirical
modal analysis. In some embodiments, comparing the at least a
fraction of the plurality of modes with the image data comprises
comparing at least one type of characteristic of the one or more
image markers of the image data with corresponding at least one
type of characteristic (e.g., location, shape, volume,
microstructure, or FLS) of the at least a fraction of the plurality
of modes. In some embodiments, comparing the at least a fraction of
the plurality of modes with the image data comprises comparing
locations of one or more image markers of the image data with
corresponding locations of the at least a fraction of the plurality
of modes. In some embodiments, the method further comprises
obtaining the image data by scanning the three-dimensional object
using a scanner. In some embodiments, calculating the strain
comprises calculating an inelastic strain, an elastic strain, or a
total strain. In some embodiments, the calculating comprises using
a distribution of the plurality of modes. In some embodiments,
calculating the strain comprises calculating the inelastic strain.
In some embodiments, calculating the stress comprises calculating
an inelastic stress, an elastic stress, or a total stress. In some
embodiments, the calculating the stress comprises using a
distribution of the plurality of modes. In some embodiments,
calculating the stress comprises calculating the inelastic
stress.
[0050] In another aspect, a system for forming a three-dimensional
object, the system comprising at least one controller configured to
direct: (A) computing a plurality of modes employing a geometric
model of a requested three-dimensional object, the plurality of
modes employing estimated mechanical alteration of the geometric
model generated during formation of the three-dimensional object,
each of the plurality of modes representing a plausible alteration
component of the three-dimensional object during a forming
operation; (B) generating image data from the three-dimensional
object; and (C) calculating a strain and/or a stress in the
three-dimensional object by comparing the plurality of modes with
the image data, wherein the three-dimensional object is formed
while employing the geometric model.
[0051] In another aspect (e.g., that can be related to the above
aspect), a system for forming a three-dimensional object, the
system comprising at least one controller configured to direct: (a)
computing a plurality of modes employing a geometric model of a
requested three-dimensional object, the plurality of modes
employing estimated mechanical alteration of the geometric model
generated during formation of the three-dimensional object, each of
the plurality of modes representing a plausible alteration
component of the three-dimensional object during a forming
operation; (b) (optionally) forming the three-dimensional object
while employing the geometric model; (c) generating image data from
the three-dimensional object; and (d) calculating a strain and/or a
stress in the three-dimensional object by comparing the plurality
of modes with the image data.
[0052] In some embodiments, at least one of the at least one
controller comprises a feed forward and/or feedback control loop.
In some embodiments, at least one of the at least one controller
comprises a closed loop and/or open loop control scheme. In some
embodiments, forming the three-dimensional object comprises
printing the three-dimensional object using three-dimensional
printing. In some embodiments, forming the three-dimensional object
comprises additively or substantively forming the three-dimensional
object. In some embodiments, forming the three-dimensional object
comprises extrusion, molding, or sculpting. In some embodiments,
the at least one controller is configured to direct an energy beam
to transform a pre-transformed material into a transformed material
to generate the three-dimensional object. In some embodiments, at
least two of operations (a), (b), (c) and (d) are controlled by at
least two different controllers. In some embodiments, at least two
of operations (a), (b), (c) and (d) are controlled by one
controller. In some embodiments, the at least one controller is
configured to direct at least one energy source to generate and
direct at least one energy beam at a pre-transformed material. In
some embodiments, the at least one controller is further configured
to direct operation (k) a platform to vertically translate, which
platform is configured to support the three-dimensional object. In
some embodiments, operation (k) is during forming of the
three-dimensional object. In some embodiments, the system further
comprises a chamber configured to enclose at least a portion of the
three-dimensional object during forming. In some embodiments, the
at least one controller is configured to monitor and/or control a
progress of the forming within the chamber. In some embodiments,
the system further comprises at least one sensor configured to
sense one or more physical markers of the three-dimensional object.
In some embodiments, the at least one controller is configured to
(i) control sensing and/or (ii) use sensing data, of the one or
more physical markers. In some embodiments, the at least one
controller is configured to (i) control sensing and/or (ii) use
sensing data, of the one or more physical markers during forming of
the three-dimensional object. In some embodiments, the at least one
controller is configured to (i) control sensing and/or (ii) use
sensing data, of the one or more physical markers after forming of
the three-dimensional object. In some embodiments, the system
further comprises at least one detector that is operationally
coupled to the at least one controller, the at least one detector
configured to detect as least one characteristic of the forming. In
some embodiments, the at least one controller is configured to
control the at least one detector and/or control one or more
process parameters present upon detecting by the at least one
detector. In some embodiments, the at least one detector is
configured to detect a temperature during forming of the
three-dimensional object. In some embodiments, the at least one
controller is configured to control (e.g., monitor) detection of
the temperature. In some embodiments, the temperature corresponds
to a temperature of the three-dimensional object. In some
embodiments, the temperature corresponds to a temperature of a
vicinity of the three-dimensional object. In some embodiments, the
vicinity is in a material bed that is configured to accommodate the
three-dimensional object. In some embodiments, the temperature
corresponds to a temperature of an atmosphere surrounding the
three-dimensional object. In some embodiments, the at least one
detector is configured to detect at least one of cleanliness,
pressure, humidity, or oxygen level of an atmosphere surrounding
the three-dimensional object during forming. In some embodiments,
detecting a cleanliness comprises detecting an amount of particles
within at least a processing cone of the atmosphere. In some
embodiments, the at least one controller comprises at least two
controllers. In some embodiments, the at least one controller is
one controller. In some embodiments, each of the plurality of modes
is associated with an energy, the method further comprises
identifying a fraction of the plurality of modes (e.g., comprising
one or more prominent modes) from the plurality of modes. In some
embodiments, identifying comprises employing the associated energy
of each of the plurality of modes. In some embodiments, the at
least a fraction of the plurality of modes (e.g., comprising the
one or more prominent modes) have associated energies. In some
embodiments, the fraction of the plurality of modes have associated
energies of at most a predetermined threshold. In some embodiments,
computing the plurality of modes comprises using one or more
calculations using at least one of singular value decomposition,
Kosambi-Karhunen-Loeve transform (KLT), Hotelling transform, proper
orthogonal decomposition (POD), eigenvalue decomposition (EVD),
factor analysis, Eckart-Young theorem, Schmidt-Mirsky theorem,
empirical orthogonal functions (EOF), empirical eigenfunction
decomposition, empirical component analysis, quasiharmonic modes,
spectral decomposition, or empirical modal analysis. In some
embodiments, calculating the strain comprises calculating an
inelastic strain, an elastic strain, or a total strain. In some
embodiments, calculating the strain comprises calculating the
inelastic strain. In some embodiments, calculating the stress
comprises calculating an inelastic stress, an elastic stress, or a
total stress. In some embodiments, calculating the stress comprises
calculating the inelastic stress.
[0053] In another aspect, a computer software product comprising at
least one non-transitory computer-readable medium in which program
instructions are stored, which program instructions, when read by
at least one computer, cause the at least one computer to direct:
computing a plurality of modes employing a geometric model of a
requested three-dimensional object, the plurality of modes
employing an estimated mechanical alteration of the geometric model
generated during formation of a three-dimensional object, each of
the plurality of modes representing a plausible alteration
component of the three-dimensional object during a forming
operation; and calculating a strain and/or a stress in a
three-dimensional object formed while employing the geometric model
of the requested three-dimensional object, wherein the calculating
comprises comparing the plurality of modes with image data
associated with the three-dimensional object.
[0054] In some embodiments, computing the plurality of modes is
operation (a) and calculating the strain and/or the stress is
operation (b). In some embodiments, a non-transitory
computer-readable medium cause a computer to direct (a) and (b). In
some embodiments, a non-transitory computer-readable medium cause a
computer to direct (a) and (b). In some embodiments, a
non-transitory computer-readable medium cause a first computer to
direct (a) and a second computer to direct (b). In some
embodiments, a first non-transitory computer-readable medium causes
a computer to direct (a) and a second non-transitory
computer-readable medium causes the computer to direct (b). In some
embodiments, a first non-transitory computer-readable medium causes
a first computer to direct (a) and a second non-transitory
computer-readable medium causes a second computer to direct (b),
wherein the first computer is different from the second computer,
wherein the first non-transitory computer-readable medium is
different from the second non-transitory computer-readable medium.
In some embodiments, the program instructions further cause the at
least one computer to direct forming the three-dimensional object
while employing the geometric model. In some embodiments, computing
the plurality of modes is operation (a), and calculating the strain
and/or the stress is (b), and forming the three-dimensional object
is operation (c). In some embodiments, a non-transitory
computer-readable medium causes a computer to direct at least two
of (a), (b) and (c). In some embodiments, a non-transitory
computer-readable medium causes different computers to direct at
least two of (a), (b) and (c). In some embodiments, a plurality of
non-transitory computer-readable mediums cause a computer to direct
at least two of (a), (b) and (c). In some embodiments, a plurality
of non-transitory computer-readable medium cause a plurality of
computers, respectively, to direct at least two of (a), (b) and
(c). In some embodiments, the first computer is different from the
second computer, wherein the first non-transitory computer-readable
medium is different from the second non-transitory
computer-readable medium. In some embodiments, the program
instructions further cause the at least one computer to direct
generating the image data from the three-dimensional object. In
some embodiments, the program instructions further cause the at
least one computer to direct a feed forward and/or feedback control
loop. In some embodiments, the program instructions further cause
the at least one computer to direct a closed loop and/or open loop
control scheme. In some embodiments, forming the three-dimensional
object comprises printing the three-dimensional object using
three-dimensional printing. In some embodiments, forming the
three-dimensional object comprises additively or substantively
forming the three-dimensional object. In some embodiments, forming
the three-dimensional object comprises extrusion, molding, or
sculpting. In some embodiments, the estimated mechanical alteration
is predicted by one or more nonlinear mechanical strain modes. In
some embodiments, the one or more nonlinear mechanical strain modes
comprise elastic or inelastic strain modes. In some embodiments,
alteration comprises deformation. In some embodiments, the computer
software product further comprises identifying a fraction of the
plurality of modes (e.g., one or more prominent modes) from the
plurality of modes. In some embodiments, the fraction of the
plurality of modes have associated energies of at most a
predetermined threshold. In some embodiments, computing the
plurality of modes comprises using one or more calculations using
at least one of singular value decomposition,
Kosambi-Karhunen-Loeve transform (KLT), Hotelling transform, proper
orthogonal decomposition (POD), eigenvalue decomposition (EVD),
factor analysis, Eckart-Young theorem, Schmidt-Mirsky theorem,
empirical orthogonal functions (EOF), empirical eigenfunction
decomposition, empirical component analysis, quasiharmonic modes,
spectral decomposition, or empirical modal analysis. In some
embodiments, calculating the strain comprises calculating an
inelastic strain, an elastic strain, or a total strain. In some
embodiments, calculating the strain comprises calculating the
inelastic strain. In some embodiments, calculating the stress
comprises calculating an inelastic stress, an elastic stress, or a
total stress. In some embodiments, calculating the stress comprises
calculating the inelastic stress.
[0055] In another aspect, a method for calibrating a system for
forming a three-dimensional object, comprising: (A) comparing one
or more dimensions of a first three-dimensional object with one or
more dimensions of a second three-dimensional object respectively,
wherein the first three-dimensional object is formed using a first
system by employing a first set of forming instructions comprising
a first physics model, and a first geometric model, wherein the
second three-dimensional object is formed employing a second set of
forming instructions comprising a second physics model, and a
second geometric model, wherein the first geometric model is
similar to the second geometric model; and (B) based on the
comparing, (i) adjusting the second physics model to differentiate
the second set of forming instructions from the first set of
forming instructions, (ii) adjusting the second geometric model to
differentiate the second set of forming instructions from the first
set of forming instructions, (iii) adjusting at least one hardware
component of a system that forming the second three-dimensional
object, or any combination of (i), (ii), and (iii), such that the
first three-dimensional object is (e.g., substantially) identical
to the second three-dimensional object, wherein the first and/or
second set of forming instructions are programmed (e.g., computer)
instructions.
[0056] In another aspect (e.g., that can be related to the one
above), a method for calibrating a system for forming a
three-dimensional object, comprising: (a) (optionally) using a
first system for forming a first three-dimensional object using a
first set of forming instructions comprising a first physics model,
and a first geometric model; (b) (optionally) forming a second
three-dimensional object using a second set of forming instructions
comprising a second physics model, and a second geometric model,
wherein the first geometric model is equal to the second geometric
model; (c) comparing one or more dimensions of the first
three-dimensional object with the second three-dimensional object
respectively; and (d) based on the comparing, (i) adjusting the
second physics model to differentiate the second set of forming
instructions from the first set of forming instructions, (ii)
adjusting the second geometric model to differentiate the second
set of forming instructions from the first set of forming
instructions, (iii) adjusting at least one hardware component of a
system that forming the second three-dimensional object, or any
combination of (i), (ii), and (iii), such that the first
three-dimensional object is (e.g., substantially) identical to the
second three-dimensional object, wherein the first and/or second
set of forming instructions are programmed (e.g., computer)
instructions.
[0057] In some embodiments, the first and/or second physics model
includes physics-based calculations related to a plurality of
modes. In some embodiments, the plurality of modes correspond to
estimated mechanical alteration in the first and/or the second
three-dimensional object that are brought about during their
respective forming. In some embodiments, the plurality of modes
correspond to an estimated elastic alteration, an inelastic
alteration, or an elastic and an inelastic alteration in the first
and/or the second three-dimensional object brought about during
their respective forming. In some embodiments, at least one of the
plurality of modes corresponds to Eigenstrain modes. In some
embodiments, at least one of the plurality of modes corresponds to
at least one prominent modes. In some embodiments, the first set of
forming instructions comprise a first set of non-transitory
computer readable instructions, and wherein the second set of
forming instruction comprise a second set of non-transitory
computer readable instructions. In some embodiments, the method
further comprises adjusting the second non-transitory computer
readable instructions to differentiate the second set from the
first set of forming instructions. In some embodiments, adjusting
the second physics model comprises adjusting one or more parameters
of a physics-based calculation. In some embodiments, the
physics-based calculation comprises thermo-mechanical related
calculations. In some embodiments, the physics-based calculation
comprises thermo-elastic, thermo-plastic, or flow-dynamics related
calculations. In some embodiments, the first set of forming
instructions comprises a first corrected geometric model. In some
embodiments, the first geometric model is a first corrected
geometric model with respect to a requested three-dimensional
object; and wherein the first three-dimensional object is (e.g.,
substantially) similar to the requested three-dimensional object.
In some embodiments, the second set of forming instructions
comprises a second corrected geometric model. In some embodiments,
the second geometric model is a second corrected geometric model
with respect to a requested three-dimensional object; and wherein
the second three-dimensional object is (e.g., substantially)
similar to the requested three-dimensional object. In some
embodiments, comparing one or more dimensions of the first
three-dimensional object with respective one or more dimensions of
the second three-dimensional object comprises comparing a first
image of the first three-dimensional object with a second image of
the second three-dimensional object. In some embodiments, the first
image and/or second image is a virtual image. In some embodiments,
the forming comprises printing (e.g., the first and/or second
three-dimensional object) using three-dimensional printing. In some
embodiments, the forming comprises additively or substantively
forming the three-dimensional object. In some embodiments, the
forming comprises extrusion, molding, or sculpting.
[0058] In another aspect, a system for forming a three-dimensional
object, the system comprising at least one controller configured to
collectively or separately direct: (a) comparing one or more
dimensions of a first three-dimensional object with respective ones
of a second three-dimensional object; and wherein the first
three-dimensional object is formed using a first set of forming
instructions comprising a first physics model, and a first
geometric model; wherein the second three-dimensional object is
formed using a second set of forming instructions comprising a
second physics model, and a second geometric model, wherein the
first geometric model is equal to the second geometric model; (b)
based on the comparing, (i) adjusting the second physics model to
differentiate the second set of forming instructions from the first
set of forming instructions, (ii) adjusting the second geometric
model to differentiate the second set of forming instructions from
the first set of forming instructions, (iii) adjusting at least one
hardware component of a system that forms the second
three-dimensional object, or any combination of (i), (ii), and
(iii), such that the first three-dimensional object is (e.g.,
substantially) identical to the second three-dimensional object,
wherein the first and/or second set of forming instructions are
programmed (e.g., computer) instructions.
[0059] In some embodiments, at least one of the at least one
controller comprises a feed forward and/or feedback control loop.
In some embodiments, at least one of the at least one controller
comprises a closed loop and/or open loop control scheme. In some
embodiments, the first set of forming instructions comprise a first
set of non-transitory computer readable instructions, and wherein
the second set of forming instruction comprise a second set of
non-transitory computer readable instructions. In some embodiments,
the system further comprises adjusting the second non-transitory
computer readable instructions to differentiate the second set from
the first set of forming instructions. In some embodiments, the
first three-dimensional object and the second three-dimensional
object are formed using three-dimensional printing at least in
part. In some embodiments, the first three-dimensional object and
the second three-dimensional object are formed using additive
and/or substantive formation of the three-dimensional object. In
some embodiments, the first three-dimensional object and the second
three-dimensional object are formed using extrusion, molding,
and/or sculpting. In some embodiments, the at least one controller
is configured to direct an energy beam to transform a
pre-transformed material into a transformed material to generate
the three-dimensional object. In some embodiments, operations (a),
and (b) are controlled by at least two different controllers. In
some embodiments, operations (a), and (b) are controlled by one
controller. In some embodiments, the at least one controller is
configured to direct at least one energy source to generate and
direct at least one energy beam at a pre-transformed material. In
some embodiments, the at least one controller is configured to
direct a first platform to vertically translate, which first
platform is configured to support the first three-dimensional
object. In some embodiments, directing the first platform is during
formation of the first three-dimensional object. In some
embodiments, the at least one controller is further configured to
direct a second platform to vertically translate, which second
platform is configured to support the second three-dimensional
object. In some embodiments, directing the second platform is
during formation of the second three-dimensional object. In some
embodiments, the system further comprises a chamber configured to
enclose at least a portion of the first and/or second
three-dimensional object during its formation. In some embodiments,
the at least one controller is configured to monitor and/or control
a progress of the forming within the chamber. In some embodiments,
the system further comprises at least one sensor configured to
sense one or more physical markers of the first and/or second
three-dimensional object. In some embodiments, the at least one
controller is configured to (i) control sensing and/or (ii) use
sensing data, of the one or more physical markers. In some
embodiments, the at least one controller is configured to (i)
control sensing and/or (ii) use sensing data, of one or more
physical markers of the first and/or second three-dimensional
object during formation of the first and/or second
three-dimensional object respectively. In some embodiments, the at
least one controller is configured to (i) control sensing and/or
(ii) use sensing data, of one or more physical markers of the first
and/or second three-dimensional object after forming of the first
and/or second three-dimensional object, respectively. In some
embodiments, the system further comprises at least one detector
that is operationally coupled to the at least one controller, the
at least one detector configured to detect as least one
characteristic of the formation of the first and/or second
three-dimensional object. In some embodiments, the at least one
controller is configured to control the at least one detector
and/or control one or more process parameters present upon
detecting by the at least one detector. In some embodiments, the at
least one detector is configured to detect a temperature during
forming of the first and/or second three-dimensional object. In
some embodiments, the at least one controller is configured to
control (e.g., monitor) the detecting. In some embodiments, the
temperature corresponds to a temperature of the first and/or second
three-dimensional object. In some embodiments, the temperature
corresponds to a temperature of a vicinity of the first and/or
second three-dimensional object. In some embodiments, the vicinity
is in a material bed that is configured to accommodate the first
and/or second three-dimensional object. In some embodiments, the
temperature corresponds to a temperature of an atmosphere
surrounding the first and/or second three-dimensional object. In
some embodiments, the at least one detector is configured to detect
at least one of cleanliness, pressure, humidity, or oxygen level of
an atmosphere surrounding the first and/or second three-dimensional
object during a forming operation. In some embodiments, detecting a
cleanliness comprises detecting a number and/or density of
particles within at least a processing cone of the atmosphere. In
some embodiments, the at least one controller comprises at least
two controllers. In some embodiments, the at least one controller
is one controller. In some embodiments, the physics model includes
physics-based calculations related to a plurality of modes. In some
embodiments, the plurality of modes correspond to estimated
mechanical alteration in the first and/or the second
three-dimensional object that are brought about during formation of
the first and/or second three-dimensional object. In some
embodiments, the plurality of modes correspond to estimated elastic
alteration, inelastic alteration, or elastic and inelastic
alteration in the first and/or the second three-dimensional object
brought about during formation of the first and/or second
three-dimensional object.
[0060] In another aspect, a computer software product comprising at
least one non-transitory computer-readable medium in which program
instructions are stored, which program instructions, when read by
at least one computer, cause the at least one computer to direct:
(a) comparing dimensions of a first three-dimensional object with
dimensions of a second three-dimensional object, the first and
second three-dimensional objects formed using a first and second
set of forming instructions respectively while employing a first
and a second physics model respectively, and a first and a second
geometric model respectively; and (b) using results from the
comparing, (i) adjusting the second physics model to differentiate
the second set of forming instructions from the first set of
forming instructions, (ii) adjusting the second geometric model to
differentiate the second set of forming instructions from the first
set of forming instructions, (iii) adjusting at least one hardware
component of a system that forms the second three-dimensional
object, or any combination of (i), (ii), and (iii), such that the
dimensions of the first three-dimensional object is (e.g.,
substantially) identical to the dimensions of the second
three-dimensional object, wherein the first and/or second set of
forming instructions are programmed (e.g., computer)
instructions.
[0061] In some embodiments, the at least one non-transitory
computer-readable medium causes the at least one computer to direct
operations (a) and (b) collectively or separately. In some
embodiments, a non-transitory computer-readable medium cause a
computer to direct operations (a) and (b). In some embodiments, a
non-transitory computer-readable medium cause a first computer to
direct operation (a) and a second computer to direct operation (b).
In some embodiments, a first non-transitory computer-readable
medium causes a computer to direct operation (a) and a second
non-transitory computer-readable medium causes the computer to
direct operation (b). In some embodiments, a first non-transitory
computer-readable medium causes a first computer to direct
operation (a) and a second non-transitory computer-readable medium
causes a second computer to direct operation (b). In some
embodiments, the first set of forming instructions comprise a first
set of non-transitory computer readable instructions, and wherein
the second set of forming instruction comprise a second set of
non-transitory computer readable instructions. In some embodiments,
further comprises adjusting the second non-transitory computer
readable instructions to differentiate the second set from the
first set of forming instructions. In some embodiments, the system
comprises a three-dimensional printer. In some embodiments, the
system is configured to additively, substantively, or both
additively and substantively, form the first and second
three-dimensional objects. In some embodiments, the system is
configured to perform extrusion, molding, sculpting, or any
combination thereof. In some embodiments, (I) the first set of
non-transitory computer readable instructions cause a first
computer to direct forming the first three-dimensional objects, and
(II) the second set of non-transitory computer readable
instructions cause a second computer to direct forming the second
three-dimensional objects. In some embodiments, the first computer
is different from the second computer. In some embodiments, the
first computer and the second computer are the same computer. In
some embodiments, the computer software product further comprises
operation (c) forming the first and second three-dimensional
object. In some embodiments, forming the first and second
three-dimensional objects comprises printing the first and second
three-dimensional objects using at least one three-dimensional
printing methodology. In some embodiments, forming the first and
second three-dimensional objects comprises additively or
substantively forming the first and second three-dimensional
objects. In some embodiments, forming the first and second
three-dimensional objects comprises extrusion, molding, or
sculpting the first and second three-dimensional objects. In some
embodiments, the program instructions cause the at least one
computer to direct a feed forward and/or feedback control loop. In
some embodiments, the program instructions cause the at least one
computer to direct a closed loop and/or open loop control scheme.
In some embodiments, the physics model includes physics-based
calculations related to a plurality of modes. In some embodiments,
the computer software product further comprises identifying a
fraction of the plurality of modes (e.g., one or more prominent
modes) from the plurality of modes. In some embodiments, the
fraction of the plurality of modes have associated energies of at
most a predetermined threshold. In some embodiments, the
physics-based calculations comprise using at least one of singular
value decomposition, Kosambi-Karhunen-Loeve transform (KLT),
Hotelling transform, proper orthogonal decomposition (POD),
eigenvalue decomposition (EVD), factor analysis, Eckart-Young
theorem, Schmidt-Mirsky theorem, empirical orthogonal functions
(EOF), empirical eigenfunction decomposition, empirical component
analysis, quasiharmonic modes, spectral decomposition, or empirical
modal analysis. In some embodiments, the plurality of modes
correspond to estimated mechanical alterations in the first and/or
second three-dimensional objects that are brought about during
forming of the first and/or second three-dimensional objects. In
some embodiments, the plurality of modes correspond to an estimated
elastic alteration, an inelastic alteration, or an elastic and an
inelastic alteration in the first and/or second three-dimensional
objects brought about during forming of the first and/or second
three-dimensional objects. In some embodiments, at least one of the
plurality of modes corresponds to Eigenstrain modes. In some
embodiments, at least one of the plurality of modes corresponds to
at least one prominent mode. In some embodiments, adjusting the
physics model comprises adjusting one or more parameters of a
physics-based calculation. In some embodiments, the physics-based
calculation comprises thermo-mechanical related calculations. In
some embodiments, the physics-based calculation comprises
thermo-elastic, thermo-plastic, or flow-dynamics related
calculations. In some embodiments, the physics-based calculation
comprises thermo-mechanics, continuum mechanics, material
properties, geometric dimensions of the first and/or second
three-dimensional object, or at least one characteristic of an
energy beam.
[0062] In another aspect, a method for determining a strain and/or
a stress in a three-dimensional object, comprising: (A) generating
a simulated object of the three-dimensional object using a physics
model that employs an estimated thermally induced change in a
geometric model of a requested three-dimensional object present
upon formation of the three-dimensional object; and (B) calculating
the strain and/or the stress in the three-dimensional object by
comparing the simulated object with image data that is generated
from a three-dimensional object, the three-dimensional object
formed using the geometric model of the requested three-dimensional
object.
[0063] In another aspect (e.g., that can be related to the above
aspect), a method for determining a strain and/or a stress in a
three-dimensional object, comprising: (a) generating a simulated
object of the three-dimensional object using a physics model that
employs an estimated thermally induced change in a geometric model
of a requested three-dimensional object present upon formation of
the three-dimensional object; (b) (optionally) forming the
three-dimensional object using the geometric model of the requested
three-dimensional object; (c) (optionally) generating image data
from the three-dimensional object; and (d) calculating the strain
and/or the stress in the three-dimensional object by comparing the
simulated object with the image data.
[0064] In some embodiments, the physics model employs a plurality
of modes, each of the plurality of modes representing a plausible
alteration component of the three-dimensional object as the present
upon formation of the three-dimensional object. In some
embodiments, each of the plurality of modes employs estimated
mechanical alterations of the geometric model. In some embodiments,
calculating the strain comprises: calculating an inelastic strain
using the image data of the three-dimensional object; and
calculating a total strain using the simulated object. In some
embodiments, the method further comprises calculating an elastic
strain using the inelastic strain and the total strain. In some
embodiments, the physics model employs a thermo-mechanical
analysis, a material property of the three-dimensional object,
continuum mechanics, at least one characteristic of an energy beam,
geometric dimensions of the three-dimensional object, or any
suitable combination thereof. In some embodiments, the
thermo-mechanical analysis comprises at least one of a thermal
expansion of the three-dimensional object, a thermal conductivity
of the three-dimensional object, an estimated thermo-plastic
deformation of the three-dimensional object, an estimated inelastic
deformation of the three-dimensional object, an estimated plastic
deformation of the three-dimensional object, an estimated elastic
deformation of the three-dimensional object, an estimated thermal
deformation of the three-dimensional object, or pressure gradients
related to the stress of the three-dimensional object. In some
embodiments, the material property of the three-dimensional object
comprises at least one of a type of material of the
three-dimensional object, a state of the material of the
three-dimensional object, a phase of the material of the
three-dimensional object, a density of the three-dimensional
object, or a surface tension of the material of the
three-dimensional object. In some embodiments, the continuum
mechanics comprises at least one of fluid dynamics during the
forming process, or stacking characteristics of the forming
process. In some embodiments, at least one characteristic of the
energy beam comprises at least one of a type of the energy beam, a
power density of the energy beam, a path of the energy beam, a
pulse width of the energy beam, or a dwell time of the energy beam.
In some embodiments, the geometric dimensions comprise at least one
of an overall shape of the three-dimensional object, or geometric
features of the three-dimensional object.
[0065] In another aspect, a system for forming a three-dimensional
object, the system comprising at least one controller configured to
direct: (A) generating a simulated object of the three-dimensional
object using a physics model that employs an estimated thermally
induced change in a geometric model of a requested
three-dimensional object present upon formation of the
three-dimensional object; and (B) calculating a strain and/or a
stress in the three-dimensional object by comparing the simulated
object with the image data that is generated from a
three-dimensional object, the three-dimensional object formed using
the geometric model of the requested three-dimensional object.
[0066] In another aspect (e.g., that can be related to the one
above), a system for forming a three-dimensional object, the system
comprising at least one controller configured to direct: (a)
generating a simulated object of the three-dimensional object using
a physics model that employs an estimated thermally induced change
in a geometric model of a requested three-dimensional object
present upon formation of the three-dimensional object; (b)
(optionally) forming the three-dimensional object using the
geometric model of the requested three-dimensional object; (c)
(optionally) generating image data from the three-dimensional
object; and (d) calculating a strain and/or a stress in the
three-dimensional object by comparing the simulated object with the
image data.
[0067] In some embodiments, at least one of the at least one
controller comprises a feed forward and/or feedback control loop.
In some embodiments, at least one of the at least one controller
comprises a closed loop and/or open loop control scheme. In some
embodiments, forming the three-dimensional object comprises
printing the three-dimensional object using three-dimensional
printing. In some embodiments, forming the three-dimensional object
comprises additively or substantively forming the three-dimensional
object. In some embodiments, forming the three-dimensional object
comprises extrusion, molding, or sculpting. In some embodiments,
the at least one controller is configured to direct an energy beam
to transform a pre-transformed material into a transformed material
to generate the three-dimensional object. In some embodiments, at
least two of operations (a), (b), (c) and (d) are controlled by at
least two different controllers. In some embodiments, at least two
of operations (a), (b), (c) and (d) are controlled by one
controller. In some embodiments, the at least one controller is
configured to direct at least one energy source to generate and
direct at least one energy beam at a pre-transformed material. In
some embodiments, the at least one controller is further configured
to direct operation (m) a platform to vertically translate, which
platform is configured to support the three-dimensional object. In
some embodiments, operation (m) is during forming of the
three-dimensional object. In some embodiments, the system further
comprises a chamber configured to enclose at least a portion of the
three-dimensional object during forming. In some embodiments, the
at least one controller is configured to monitor and/or control a
progress of the forming within the chamber. In some embodiments,
the system further comprises at least one sensor configured to
sense one or more physical markers of the three-dimensional object.
In some embodiments, the at least one controller is configured to
(i) control sensing and/or (ii) use sensing data, of the one or
more physical markers. In some embodiments, the at least one
controller is configured to (i) control sensing and/or (ii) use
sensing data, of one or more physical markers of the
three-dimensional object during forming of the three-dimensional
object. In some embodiments, the at least one controller is
configured to (i) control sensing and/or (ii) use sensing data, of
one or more physical markers of the three-dimensional object after
forming of the three-dimensional object. In some embodiments, the
system further comprises at least one detector that is
operationally coupled to the at least one controller, the at least
one detector configured to detect as least one characteristic of
the forming. In some embodiments, the at least one controller is
configured to control the at least one detector and/or control one
or more process parameters present upon detecting by the at least
one detector. In some embodiments, the at least one detector is
configured to detect a temperature during forming of the
three-dimensional object. In some embodiments, the at least one
controller is configured to control (e.g., monitor) detection of
the temperature. In some embodiments, the temperature corresponds
to a temperature of the three-dimensional object. In some
embodiments, the temperature corresponds to a temperature of a
vicinity of the three-dimensional object. In some embodiments, the
vicinity is in a material bed that is configured to accommodate the
three-dimensional object. In some embodiments, the temperature
corresponds to a temperature of an atmosphere surrounding the
three-dimensional object. In some embodiments, the at least one
detector is configured to detect at least one of cleanliness,
pressure, humidity, or oxygen level of an atmosphere surrounding
the three-dimensional object during the forming. In some
embodiments, detecting a cleanliness comprises detecting an amount
of particles within at least a processing cone of the atmosphere.
In some embodiments, the at least one controller comprises at least
two controllers. In some embodiments, the at least one controller
is one controller. In some embodiments, the physics model employs a
thermo-mechanical analysis, a material property of the
three-dimensional object, continuum mechanics, at least one
characteristic of an energy beam, geometric dimensions of the
three-dimensional object, or any suitable combination thereof. In
some embodiments, the thermo-mechanical analysis comprises at least
one of a thermal expansion of the three-dimensional object, a
thermal conductivity of the three-dimensional object, an estimated
thermo-plastic deformation of the three-dimensional object, an
estimated inelastic deformation of the three-dimensional object, an
estimated plastic deformation of the three-dimensional object, an
estimated elastic deformation of the three-dimensional object, an
estimated thermal deformation of the three-dimensional object, or
pressure gradients related to the stress of the three-dimensional
object. In some embodiments, the material property of the
three-dimensional object comprises at least one of a type of
material of the three-dimensional object, a state of a material of
the three-dimensional object, a phase of the material of the
three-dimensional object, a density of the three-dimensional
object, or a surface tension of the material of the
three-dimensional object. In some embodiments, the continuum
mechanics comprises at least one of fluid dynamics during the
forming process, or stacking characteristics of the forming
process. In some embodiments, the at least one characteristic of
the energy beam comprises at least one of a type of the energy
beam, a power density of the energy beam, a path of the energy
beam, a pulse width of the energy beam, or a dwell time of the
energy beam. In some embodiments, geometric dimensions comprise at
least one of an overall shape of the three-dimensional object, or
geometric features of the three-dimensional object.
[0068] In another aspect, A computer software product comprising at
least one non-transitory computer-readable medium in which program
instructions are stored, which program instructions, when read by
at least one computer, cause the at least one computer to direct:
(a) generating a simulated object using a physics model that
employs an estimated thermally induced change of a requested
three-dimensional object present upon formation of the
three-dimensional object; and (b) calculating a strain and/or a
stress in the three-dimensional object formed by using a geometric
model of the requested three-dimensional object, wherein
calculating the strain and/or stress comprises comparing the
simulated object with the three-dimensional object that is
formed.
[0069] In some embodiments, the estimated thermally induced change
comprises an estimated thermo-mechanically induced change. In some
embodiments, comparing the simulated object with the
three-dimensional object comprises comparing the simulated object
with image data associated with the three-dimensional object. In
some embodiments, the physics model employs a plurality of modes,
each of the plurality of modes representing a plausible alteration
component of the three-dimensional object present upon formation of
the three-dimensional object. In some embodiments, each of the
plurality of modes employs estimated mechanical alterations of the
geometric model. In some embodiments, the computer software product
further comprises identifying a fraction of the plurality of modes
(e.g., comprising one or more prominent modes) from the plurality
of modes. In some embodiments, the fraction of the plurality of
modes (e.g., the one or more prominent modes) have associated
energies of at most a predetermined threshold. In some embodiments,
calculating the strain comprises (i) calculating an inelastic
strain using image data of the three-dimensional object; and (ii)
calculating a total strain using the simulated object. In some
embodiments, the computer software product further comprises
calculating an elastic strain using the inelastic strain and the
total strain. In some embodiments, the physics model employs a
thermo-mechanical analysis, a material property of the
three-dimensional object, continuum mechanics, at least one
characteristic of the energy beam, geometric dimensions of the
three-dimensional object, or any suitable combination thereof. In
some embodiments, the thermo-mechanical analysis comprises at least
one of a thermal expansion of the three-dimensional object, a
thermal conductivity of the three-dimensional object, an estimated
thermo-plastic deformation of the three-dimensional object, an
estimated inelastic deformation of the three-dimensional object, an
estimated plastic deformation of the three-dimensional object, an
estimated elastic deformation of the three-dimensional object, an
estimated thermal deformation of the three-dimensional object, or
pressure gradients related to stress of the three-dimensional
object. In some embodiments, the material property of the
three-dimensional object comprises at least one of a type of
material of the three-dimensional object, a state of a material of
the three-dimensional object, a phase of the material of the
three-dimensional object, a density of the three-dimensional
object, or a surface tension of the material of the
three-dimensional object. In some embodiments, the continuum
mechanics comprises at least one of fluid dynamics during the
forming process, or stacking characteristics of the forming
process. In some embodiments, the at least one characteristic of
the energy beam comprises at least one of a type of the energy
beam, a power density of the energy beam, a path of the energy
beam, a pulse width of the energy beam, or a dwell time of the
energy beam. In some embodiments, the geometric dimensions
comprises at least one of an overall shape of the three-dimensional
object, or geometric features of the three-dimensional object. In
some embodiments, the physics model employs physics-based
calculations using at least one of singular value decomposition,
Kosambi-Karhunen-Loeve transform (KLT), Hotelling transform, proper
orthogonal decomposition (POD), eigenvalue decomposition (EVD),
factor analysis, Eckart-Young theorem, Schmidt-Mirsky theorem,
empirical orthogonal functions (EOF), empirical eigenfunction
decomposition, empirical component analysis, quasiharmonic modes,
spectral decomposition, or empirical modal analysis. In some
embodiments, the at least one non-transitory computer-readable
medium, cause the at least one computer to direct (a) and (b)
separately or collectively. In some embodiments, the program
instructions cause the at least one computer to direct (a) and (b).
In some embodiments, the program instructions cause a first
computer to direct (a) and a second computer to direct (b). In some
embodiments, a first non-transitory computer-readable medium causes
a computer to direct (a) and a second non-transitory
computer-readable medium causes the computer to direct (b). In some
embodiments, a first non-transitory computer-readable medium causes
a first computer to direct (a) and a second non-transitory
computer-readable medium causes a second computer to direct (b). In
some embodiments, the program instructions further cause the at
least one computer to direct (c) forming the first and/or second
three-dimensional object using the geometric model of the requested
three-dimensional object. In some embodiments, (c) is before (b).
In some embodiments, (c) is before (a). In some embodiments, a
non-transitory computer-readable medium causes a computer to direct
at least two of (a), (b) and (c). In some embodiments, plurality of
non-transitory computer-readable medium cause a computer to direct
at least two of (a), (b) and (c). In some embodiments, a
non-transitory computer-readable medium causes a plurality computer
to direct at least two of (a), (b) and (c). In some embodiments, a
plurality of non-transitory computer-readable medium cause a
plurality computers to direct at least two of (a), (b) and (c),
respectively. In some embodiments, forming the three-dimensional
object comprises printing the three-dimensional object using
three-dimensional printing. In some embodiments, forming the
three-dimensional object comprises additively or substantively
forming the three-dimensional object. In some embodiments, forming
the three-dimensional object comprises extrusion, molding, or
sculpting. In some embodiments, the program instructions further
cause the at least one computer to direct a feed forward and/or
feedback control loop. In some embodiments, the program
instructions further cause the at least one computer to direct a
closed loop and/or open loop control scheme. Another aspect of the
present disclosure provides systems, apparatuses, controllers,
and/or non-transitory computer-readable media (e.g., software) that
implement any of the methods disclosed herein.
[0070] In another aspect, a system used in forming (e.g., printing,
molding, welding, machining, casting) at least one 3D object
comprises any combination of the apparatuses disclosed herein.
[0071] In another aspect, a system used in forming of at least one
3D object comprises any combination of the apparatuses and the
computer software disclosed herein.
[0072] In another aspect, a computer software product for forming
at least one 3D object, comprising at least one non-transitory
computer-readable medium in which program instructions are stored,
which instructions, when read by at least one computer, cause the
at least one computer to perform any of the methods disclosed
herein.
[0073] In another aspect, a computer software product, comprising
at least one non-transitory computer-readable medium in which
program instructions are stored, which instructions, when read by
at least one computer, cause the computer to direct a mechanism
used in the forming processes to implement (e.g., effectuate) any
of the method disclosed herein, wherein the at least one
non-transitory computer-readable medium is operatively coupled to
the mechanism.
[0074] Another aspect of the present disclosure provides systems,
apparatuses, controllers, and/or non-transitory computer-readable
medium (e.g., software) that implement any of the methods disclosed
herein.
[0075] In another aspect, an apparatus for forming one or more 3D
objects comprises one or more controllers that is programmed to
direct a mechanism used in a forming methodology to implement
(e.g., effectuate) any of the method disclosed herein, wherein the
one or more controllers is operatively coupled to the
mechanism.
[0076] In another aspect, the one or more controllers disclosed
herein comprise a computer software product, e.g., as disclosed
herein.
[0077] Another aspect of the present disclosure provides a computer
system comprising one or more computer processors and a
non-transitory computer-readable medium coupled thereto. The
non-transitory computer-readable medium comprises
machine-executable code that, upon execution by the one or more
computer processors, implements any of the methods and/or
controller directions disclosed herein.
[0078] In another aspect, a computer software product comprises a
non-transitory computer-readable medium that causes a computer to
direct one or more of the methods described herein.
[0079] In another aspect, a computer software product comprises a
non-transitory computer-readable medium that causes a first
computer to direct one or more methods described herein and a
second computer to direct another one or more methods described
herein.
[0080] In another aspect, a computer software product comprises a
first non-transitory computer-readable medium that causes a
computer to direct one or more methods described herein and a
second non-transitory computer-readable medium that cause the
computer to direct another one or more methods described
herein.
[0081] In another aspect, a computer software product comprises a
first non-transitory computer-readable medium cause a first
computer to direct one or more methods described herein and a
second non-transitory computer-readable medium cause a second
computer to direct another one or more methods described
herein.
[0082] In another aspect, a computer software product comprises a
non-transitory computer-readable medium that causes a plurality of
computers to direct one or more methods described herein.
[0083] In another aspect, a computer software product comprises a
plurality of non-transitory computer-readable mediums cause a
computer to direct one or more methods described herein.
[0084] In another aspect, a computer software product comprises a
plurality of non-transitory computer-readable medium cause a
plurality of computers to direct one or more methods described
herein.
[0085] In some embodiments, the term "3D object" may refer to one
or more 3D objects.
[0086] Another aspect of the present disclosure provides a
non-transitory computer-readable medium comprising
machine-executable code that, upon execution by one or more
computer processors, implements any of the methods disclosed
herein.
[0087] Additional aspects and advantages of the present disclosure
will become readily apparent to those skilled in this art from the
following detailed description, wherein only illustrative
embodiments of the present disclosure are shown and described. As
will be realized, the present disclosure is capable of other and
different embodiments, and its several details are capable of
modifications in various obvious respects, all without departing
from the disclosure. Accordingly, the drawings and description are
to be regarded as illustrative in nature, and not as
restrictive.
INCORPORATION BY REFERENCE
[0088] All publications, patents, and patent applications mentioned
in this specification are herein incorporated by reference to the
same extent as if each individual publication, patent, or patent
application was specifically and individually indicated to be
incorporated by reference.
BRIEF DESCRIPTION OF DRAWINGS
[0089] The novel features of the invention are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the present invention will be obtained
by reference to the following detailed description that sets forth
illustrative embodiments, in which the principles of the invention
are utilized, and the accompanying drawings or figures (also "Fig."
and "Figs." herein), of which:
[0090] FIG. 1 schematically illustrates a model of a
three-dimensional (3D) object and a respective 3D object;
[0091] FIG. 2 schematically illustrates a vertical cross section of
a 3D printing system and its components;
[0092] FIG. 3 schematically illustrates energy beam path;
[0093] FIG. 4 schematically illustrates various energy beam
paths;
[0094] FIG. 5 schematically illustrates a vertical cross section 3D
objects;
[0095] FIG. 6 schematically illustrates vertical cross sections of
a model of a 3D object and a respective 3D object;
[0096] FIG. 7 schematically illustrates a vertical cross section of
a 3D printing system and its components;
[0097] FIG. 8 schematically illustrates models of 3D objects and
respective 3D objects;
[0098] FIG. 9 schematically illustrates a flow diagram used in the
printing of one or more 3D objects;
[0099] FIG. 10 schematically illustrates various 3D objects;
[0100] FIG. 11 schematically illustrates a processing (e.g.,
computer) system;
[0101] FIG. 12 schematically illustrates a vertical cross section
of a detection system;
[0102] FIG. 13 schematically illustrates a flow diagram used in the
forming of one or more 3D objects;
[0103] FIG. 14 schematically illustrates a flow diagram used in the
forming of one or more 3D objects;
[0104] FIGS. 15A-15D illustrate a geometric model and modes for a
3D object;
[0105] FIG. 16 illustrates a spectrum of modes of a 3D object;
[0106] FIG. 17 schematically illustrates a flow diagram used in the
forming of one or more 3D objects;
[0107] FIG. 18 schematically illustrates a flow diagram used in the
forming of one or more 3D objects;
[0108] FIGS. 19A-19C illustrate models of a 3D object;
[0109] FIG. 20A shows a 3D object; FIG. 20B illustrates a model of
a 3D object;
[0110] FIG. 21 illustrates models of a 3D object;
[0111] FIGS. 22A-22G illustrate modes for a 3D object;
[0112] FIG. 23 illustrates a spectrum of modes of a 3D object;
[0113] FIG. 24A illustrates a model of a 3D object; FIGS. 24B-24E
illustrate modes for a 3D object;
[0114] FIG. 25 illustrates a spectrum of modes of a 3D object;
[0115] FIGS. 26A-26D illustrate modes for a 3D object;
[0116] FIG. 27 illustrates a spectrum of modes of a 3D object;
[0117] FIG. 28 schematically illustrates a flow diagram used in the
forming of one or more 3D objects;
[0118] FIG. 29 schematically illustrates a flow diagram used in the
forming of one or more 3D objects;
[0119] FIG. 30 schematically illustrates a flow diagram used in the
forming of one or more 3D objects;
[0120] FIG. 31 schematically illustrates a flow diagram used in the
forming of one or more 3D objects;
[0121] FIG. 32 schematically illustrates a flow diagram used in the
forming of one or more 3D objects; and
[0122] FIG. 33A illustrates a geometric model of a requested 3D
object; FIG. 33B illustrates an image of a 3D object; and FIG. 33C
shows a 3D object.
[0123] The figures and components therein may not be drawn to
scale. Various components of the figures described herein may not
be drawn to scale.
DETAILED DESCRIPTION
[0124] While various embodiments of the invention have been shown
and described herein, it will be obvious to those skilled in the
art that such embodiments are provided by way of example only.
Numerous variations, changes, and substitutions may occur to those
skilled in the art without departing from the invention. It should
be understood that various alternatives to the embodiments of the
invention described herein might be employed.
[0125] The process of generating a three-dimensional object (e.g.,
three-dimensional printing processes) may cause certain alterations
(e.g., deformations) to occur in the three-dimensional (3D) object.
The alterations may be structural alterations in the overall shape
of at least a portion of the 3D object and/or in the microstructure
of at least a portion of the 3D object. For example, the
alterations can cause geometric dimensions (shape) of the 3D object
to vary from a requested geometric dimension (e.g., and shape). An
alteration can occur due to, warping (e.g., bending or twisting) of
the 3D object. An alteration can occur due to thermal expansion of
the 3D object, issues related to tool offset, and other mechanisms
related to the generation process. The tool can be a 3D printer, a
mold, an extrusion mechanism, a welding mechanism, or any other
tool related to the process of generating the 3D object. Methods,
software, apparatus, and systems described herein can be used to
quantify an alteration caused by the generating process, predict
the alteration induced by the generating process, create one or
more computer-based models that compensate for the alteration
(e.g., deformation), generate 3D objects having improved
dimensional accuracy, or any combination thereof.
[0126] Terms such as "a", "an" and "the" are not intended to refer
to only a singular entity, but include the general class of which a
specific example may be used for illustration. The terminology
herein is used to describe specific embodiments of the
invention(s), but their usage does not delimit the
invention(s).
[0127] When ranges are mentioned, the ranges are meant to be
inclusive, unless otherwise specified. For example, a range between
value 1 and value 2 is meant to be inclusive and include value 1
and value 2. The inclusive range will span any value from about
value 1 to about value 2. The term "adjacent" or "adjacent to," as
used herein, includes `next to`, `adjoining`, `in contact with`,
and `in proximity to.` When "and/or" is used in a sentence such as
X and/or Y, the phrase means: X, Y, or any combination thereof.
[0128] As used herein, the term "operatively coupled" or
"operatively connected" refers to a first mechanism that is coupled
(or connected) to a second mechanism to allow the intended
operation of the second and/or first mechanism.
[0129] As used herein, the terms "object", "3D part", and "3D
object" may be used interchangeably, unless otherwise
indicated.
[0130] Fundamental length scale (abbreviated herein as "FLS") can
be refer herein as to any suitable scale (e.g., dimension) of an
object. For example, a FLS of an object may comprise a length, a
width, a height, a diameter, a spherical equivalent diameter, or a
diameter of a bounding sphere. In some cases, FLS may refer to an
area, a volume, a shape, or a density.
[0131] As used herein, the term "based on" is not meant to be
restrictive. That is, "based on" does not necessarily mean
"exclusively based on" or "primarily based on". For example, "based
on" can be synonymous to "using" or "considering."
[0132] In some embodiments, a 3D object is marked with one or more
markers. A marked three-dimensional (3D) object may comprise the
one or more markers. The markers can be embedded on at least one
surface and/or interior portion of a desired 3D object. The markers
may comprise a depression (e.g., embossing, degradation, or
intrusion), protrusion (e.g., extrusion, swelling, elevation, or
projection), or deletion (e.g., omission, or hole) in at least one
portion of the desired 3D object. The marker can correspond to a
feature (e.g., two dimensional and/or three dimensional) that is
located in pre-determined locations of a 3D object. In some
embodiments, the markers reside on a surface of the 3D object. In
some embodiments, the markers reside within a volume of the 3D
object. In some embodiments, the markers are discrete features. The
markers may decorate the 3D object. The markers may be a part of
the 3D object geometry (e.g., a tessellation border, or an edge of
the 3D object). The markers may be geometrical markers. The marker
may be a physical (e.g., comprising material) addition and/or
omission to the 3D object. The markers may be metrological markers.
The marker may be a material property of the 3D object (e.g., a
mark within the material which the 3D object consists of, e.g., a
microstructure). The marker may be a pore, dislocation, or crack.
The marker may comprise a metallurgical or crystalline feature.
FIG. 1 shows an example of a model of a 3D object 100 that
comprises markers (e.g., 101) in the form of (e.g., substantially)
circular holes.
[0133] The position, and/or geometry (e.g., shape and/or size) of
the markers may be chosen such the markers may be monitored during
and/or after a forming process (e.g., 3D printing) of a 3D object.
The position, and/or geometry (e.g., shape and/or size) of the
markers may be chosen such that two subsequent markers may not
merge during and/or after the forming process (e.g., 3D printing)
(e.g., based on an estimated deformation maximum). The position
and/or geometry of the markers may be chosen such that two
subsequent markers may not cause alteration (e.g., deformation) in
the 3D object that will prevent the forming process (e.g., printing
in the 3D printer). Prevent the forming process may be due to
hardware constraints. The estimate may be a crude estimate. The
position, and/or geometry (e.g., shape and/or size) of the markers
may be chosen such that the markers will not (e.g., substantially)
affect the overall behavior of the 3D object during and/or after
the forming process (e.g., 3D printing).
[0134] The one or more markers may serve as a tracking device of
the forming process (e.g., 3D printing process). FIG. 1 shows an
example of a model of a 3D object that comprises markers (e.g.,
101, 102, 103, 104 and 105); and a 3D object 110 that was formed
(e.g., printed) 120 based on the model 100, which formed (e.g.,
printed) 3D object comprises respective markers (e.g., 111, 112,
113, 114 and 115), wherein respective is to the model 100. The
tracking may be of (i) the entire 3D object after its formation,
(ii) various stages of the 3D object during its formation (e.g., 3D
printing) process and/or (iii) of various portions of the formed
(e.g., printed) 3D object.
[0135] In the forming process (e.g., 3D printing), a requested 3D
object can be formed (e.g., printed) according to (e.g., printing)
instructions, which are based at least in part on a model of a
desired 3D object. The model may comprise a computer model,
geometric model, corrected geometric model, test model, marked
model, or a marked geometric model. The geometric model may
comprise a CAD model. The geometric model may be a virtual model,
e.g., a computer-generated model (of the 3D object). The geometric
model may be a virtual representation of the geometry of the 3D
object, e.g., in the form of 3D imagery. In some cases, a geometric
model corresponds to an image (e.g., scan) of an object (e.g., a
test object). The model of the desired 3D object can be manipulated
to incorporate the one or more model markers to form a model of the
marked 3D object (also referred herein as a "test model"). The
model of the marked 3D object (i.e., the "test model") may be
incorporated in (e.g., printing) instruction to generated a
physically (e.g., structurally) marked 3D object (also referred
herein as the "test 3D object", "test object" or "test part") that
incorporates physical one or more markers (also referred to herein
as a "physical markers", "structural markers" or "test markers",
e.g., depending on the type of object). The structural marker may
be a geometric marker. A model of the object can have one or more
markers (also referred to herein as "model markers", "image
markers", "virtual markers" or "test markers", depending on the
type of model) corresponding to the one or more physical
markers.
[0136] The one or more model markers (also referred to herein as
"test markers") that are embedded in the model of the 3D object,
may be embedded at one or more positions respectively. FIG. 1 shows
an example of two markers 102 and 103 that are embedded in the
model 100 of the 3D object, which markers are separated by a
distance d.sub.1. Model 100 also comprises model markers (e.g.,
101, 104 and 105). The one or more positions of the markers (e.g.,
101, 102, 103, 104 and 105) may comprise random, or specific
positions. The one or more positions can form an array. The array
may be an organized array. The one or more positions may be
predetermined positions (e.g., on the model of the 3D object). For
example, the one or more positions may be on a portion of the
requested 3D object that is susceptible to alteration (e.g.,
deformation). The alteration (e.g., deformation) may comprise
warping, buckling, bending, balling, or twisting. The alteration
(e.g., deformation) may comprise squeezing and/or stretching the
material of the 3D object. The deformation may be due to material
stress and/or strain. The deformation may occur during and/or after
forming the 3D object, e.g., during and/or after the formation of a
hardened material. The deformation may occur due to the forming
(e.g., 3D printing) process and/or properties of the particular
material(s) used in the forming (e.g., 3D printing).
[0137] In some embodiments, the 3D object(s) is/are formed using
one or more 3D printing processes. In one embodiment, the process
of 3D printing comprises additive manufacturing. Three-dimensional
printing may comprise depositing a first (e.g., substantially
planar (e.g., planar)) layer of pre-transformed material to form a
material bed; directing an energy beam towards a first portion of
the first layer of pre-transformed material to form a first
transformed material according to a first slice in a model (e.g., a
computer model (e.g., geometric model)) of a three-dimensional
object. In some embodiments, the three-dimensional printing
comprises using one or more laser engineered net shaping, direct
metal deposition, and laser consolidation techniques. The
transformed material may be a portion of the 3D object. The
transformed material may be hardened into a hardened (e.g.,
substantially solid (e.g., solid)) material as part of the 3D
object. Optionally, this process may be repeated layer by layer.
For example, by adding a second (e.g., substantially planar (e.g.,
planar)) layer of pre-transformed material, directing the energy
beam towards a second portion of the second layer of
pre-transformed material to form a second transformed material
according to a second slice in a (geometric) model of a 3D object.
In some embodiments, the 3D object is formed using a material bed.
The material bed may be at a (e.g., substantially) constant
pressure during the forming process. For example, the material bed
may be devoid of a pressure gradient during the forming process.
The material bed (e.g., powder bed) may comprise flowable material
(e.g., powder) during the forming process. In some example, the 3D
object (or a portion thereof) may be formed in the material bed
without being anchored (e.g., to the platform). For example, the 3D
object may be formed without auxiliary supports. The 3D object may
be formed without any externally applied pressure gradient(s). For
example, the material bed can be under (e.g., substantially)
constant pressure (e.g., having (e.g., substantially) no pressure
gradients). For example, the material bed can remain in a flowable
(e.g., not fixed) state during a transformation process. 3D
printing processes; various materials; and 3D printing methods,
systems, apparatuses, controller (e.g., including the processor)
and software (e.g., including energy beams), are described in PCT
Patent Applications serial numbers PCT/US2015/065297,
PCT/US16/34857, and PCT/US17/18191; European patent application
serial number EP17156707.6; U.S. patent application Ser. No.
15/435,065; and in U.S. provisional patent application Ser. No.
62/401,534, each of which is incorporated herein in its
entirety.
[0138] FIG. 2 shows an example of a 3D printer 200 comprising a
chamber 207 (also referred to herein as processing chamber) having
an inner atmosphere 226 enclosed in an inner volume, which
atmosphere comprises one or more gasses; an energy source 221
generating an energy beam 201; a scanner 220 that aids in
translation of the energy beam (e.g., according to a pattern); an
optical window 215; a material dispenser 216; a material leveling
member 217; a material removal member 218; an optional cooling
member (e.g., heat sink) 213; a material bed 204 comprising an
exposed surface 219, a (e.g., forming) 3D object 206; a platform
comprising a base 202 and a substrate 209, which platform is
configured to support the 3D object, which platform is separated
from the enclosure by a barrier (e.g., 203), which platform is
disposed on an actuator (e.g., elevator) 205 that is vertically
translatable 212, which chamber has a bottom portion 211, which
platform has a bottom portion 210, which scanner and energy source
are disposed outside of the enclosure 207. The processing chamber
can enclose at least a portion of the 3D object during its
formation. The energy beam can translate (e.g., travel) through a
region (sometime referred to as a processing cone) within the
processing chamber during the 3D printing. Sometimes it is
desirable for the processing cone to be (e.g., substantially) free
of particles (e.g., debris) during the 3D printing. One or more
controller can be configured to vertically translate the platform.
In some embodiments, at least one of the material removal member,
the material leveling member, the cooling member, the base, and the
optical window are optional components. At times, the energy source
and/or the scanner 220 are disposed within the enclosure. The
enclosure may be open or closed to the ambient environment. The
enclosure may comprise one or more openings (e.g., doors and/or
windows). The enclosure may comprise a load lock. The actuator
and/or the building platform may be an integral part of the
enclosure, or separate part of the enclosure that may be reversibly
connected to the enclosure.
[0139] At times, a formed (e.g., printed) portion of the 3D object
may (e.g., substantially) deviate from the model of the 3D object
during and/or after the forming (e.g., 3D printing), e.g., during
and/or after the formation of the hardened material. Substantially
deviate may be in relation to the intended purpose of the 3D
object. For example, manufacturing requirements may dictate that
particular dimensions of the 3D object are within a specified
threshold (e.g., tolerance). Such deviation may comprise
deformation. FIG. 1 shows an example of a structural deviation in a
general sense. FIG. 1 shows an example of a model of a 3D object
comprising a bent structure 100, and its respective formed (e.g.,
printed) 3D object comprising a planar structure 110 that deviates
from the bent structure 100. Inclusion of one or more markers in
the model of the 3D object, and subsequently in the generated 3D
object, may provide information on the extent, location, and/or
type of alteration (e.g., deformation) that results from forming
the 3D object. At times, the inclusion of the one or more markers
may shed light on the process that leads to the alteration (e.g.,
deformation). The markers may be structural (e.g., geometrical)
markers. The markers may be physical markers (e.g., structural
markers). The markers may be metrological markers. The markers may
provide metrological information (measurable information) regarding
the generation process of the 3D object. The markers may be
material markers.
[0140] In some embodiments, the positions (also referred to herein
as "locations", "physical positions" or "physical locations")
and/or form of the one or more markers of the test 3D object (also
herein "physical positions") and the position of the one or more
markers of the test model (also herein "locations", "model
positions" or "model locations") may be compared. In some
embodiments, (i) the positions and/or form (e.g., structure) of the
one or more markers (that are physically marked (e.g., structurally
marked)) of the 3D object, and (ii) the position of the one or more
markers of the model of the marked 3D object, may be compared. At
times the physical marker positions may deviate from the model
marker positions. At times, the physical marker positions may
(e.g., substantially) coincide with the model marker positions. At
times the physical marker shape may deviate from the model marker
shape. At times, the physical marker shape may (e.g.,
substantially) coincide with the model marker shape. The physical
markers may be referred herein as "test markers." In some
embodiments, substantially coincide is in relation to (e.g.,
within) a predetermined threshold or limit. In some embodiments,
comparing locations of markers (e.g., model markers and test
markers) and/or determining whether they substantially coincide,
involves performing one or more data analysis techniques. In some
embodiments, data analysis techniques described herein involves one
or more regression analys(es) and/or calculation(s). The regression
analysis and/or calculation may comprise linear regression, least
squares fit, Gaussian process regression, kernel regression,
nonparametric multiplicative regression (NPMR), regression trees,
local regression, semiparametric regression, isotonic regression,
multivariate adaptive regression splines (MARS), logistic
regression, robust regression, polynomial regression, stepwise
regression, ridge regression, lasso regression, elasticnet
regression, principal component analysis (PCA), singular value
decomposition (SVD)), probability measure techniques (e.g., fuzzy
measure theory, Borel measure, Harr measure, risk-neutral measure,
Lebesgue measure), predictive modeling techniques (e.g., group
method of data handling (GMDH), Naive Bayes classifiers, k-nearest
neighbors algorithm (k-NN), support vector machines (SVMs), neural
networks, support vector machines, classification and regression
trees (CART), random forest, gradient boosting, generalized linear
model (GLM)), or any other suitable probability and/or statistical
analys(es). In some cases, the comparison involves comparing
relative locations of the markers (e.g., model markers) with
respect to each other and/or to markers on another object (e.g., a
test object). In some cases, some of the markers are removed
(redacted). The markers may include edges, kinks, or rims of an
object. The markers may comprise borders of geometric model
components that are manifested on the physical 3D object. For
example, the markers may comprise tessellation borders of the
geometric model that are manifested on the physical 3D object. In
some cases, certain portions of the object will experience more
alteration (e.g., deformation) as a result of the forming process,
as compared to other portions of the object. The portions that
experience more alteration may result in more deviation between
physical positions and model positions of the markers. The portions
of the (physical) 3D object and/or (virtual) model of the 3D object
at which deviation is detected, may be positions susceptible to
alteration (e.g., deformation). The portions of the 3D object
and/or model at which deviation is not detected, may be positions
(e.g., substantially) free of alteration (e.g., deformation). FIG.
1 shows an example where the markers 114 and 115 in the formed
(e.g., printed) 3D object 110 moved as compared to their respective
positions of markers 102 and 103 in the model 100 of that 3D
object, as can be detected inter alia from the difference in their
respective distances d.sub.2 as compared to d.sub.1. FIG. 1 shows
an example where the markers 111 and 113 in the formed (e.g.,
printed) 3D object changed in shape and density as compared to
their respective markers 104 and 105 in the model of that 3D
object: round marker 104 of the model, became elongated marker 111,
markers in the area of 105 of the test model became denser in the
test 3D object in the respective area of 113. These types of shape
changes of the markers may or may not be of significance. For
example, in some embodiments, such shape changes are treated as
permissible variation (e.g., within a tolerance). In some
embodiments, the shape changes are measurable and included within
the data analys(es). In some embodiments, the shape of the markers
does not (e.g., substantially) change as a result of the forming
process. For example, FIG. 1 shows model marker 101 having a
symmetrically round cross-section shape, resulting in physical
marker 112 having a corresponding symmetrically round cross-section
shape.
[0141] The comparison between the test model (e.g., 100) and the
test object (e.g., test 3D object, 110) may allow for empirical
estimation and/or (simulated) prediction of deformation. An
estimated alteration (e.g., deformation) based on empirical
evidence (referred to herein as "empirical process", "empirical
method" or "empirical estimation") can involve deriving results
from one or more formed (e.g., printed) objects. For example,
dimensions of one or more formed objects can be measured (using any
suitable technique) and compared to corresponding dimensions of a
geometric model (from which the forming (e.g., printing)
instructions are derived). Differences between the dimensions can
then be used to predict what portions of an object are most likely
to deform (and/or an overall deformation of the object) due to the
forming process. In some cases, the differences can include
differences in an expected density (e.g., porosity), material
consistency, metallurgical shape (e.g., and their distribution),
and/or other aspects of an object. As described above, in some
embodiments, the geometric model includes one or more model markers
(e.g., protrusions, recesses and/or deletions) that result in
corresponding physical markers of the formed object. Spacing
(distances) between the physical markers can be compared to
respective spacing (distances) between corresponding model markers,
to determine regions of the object that experience more deformation
than other regions. The comparison between the test model
(geometric model) and a test object (e.g., test 3D object) may
allow the design of forming (e.g., printing) instructions (e.g., 3D
printing instructions) that can result in reduction of deformation.
The comparison between the test model and the test object (e.g.,
test 3D object) may allow the design of forming (e.g., printing)
instructions (e.g., 3D printing instructions) that result high
fidelity forming (e.g., printing) of the 3D object. The comparison
between the (virtual) test model and the (physical) test object may
aid an understanding and/or differentiation between various
mechanism that cause alteration (e.g., deformation and/or addition)
to at least a portion of the 3D object. For example,
differentiation between expansion and extension mechanisms. For
example, various mechanisms leading to dimensional inaccuracy. The
comparison between the test model (e.g., 100) and the test object
(e.g., test 3D object, 110) may comprise comparing their respective
markers (e.g., in terms of relative distances, FLS, volume, and/or
shape). The result may aid in experimental calculation of
(internal) stresses and/or strains of at least a portion of the 3D
object. The experimental calculation(s) may allow for an
understanding of the material behavior during the forming process
(e.g., the material from which the 3D object is built, or the
desired material for the 3D object). In some embodiments, the
comparison and/or strategic placement of the one or more markers
may facilitate formation of functionally graded materials (e.g.,
comprising various microstructures at different portions of the 3D
object). FIG. 10 shows an example of a requested 3D object 1020,
deformed 3D object 1000 respective to the requested 3D object 1020,
and a 3D object 1012 that comprises additions 1010 (e.g., in the
form of stalactites, which can extend beyond height H of the
requested object 1020) with respect to the requested 3D object
1020.
[0142] High fidelity forming (e.g., printing) may refer to the
degree of deviation of the formed (e.g., printed) 3D object from a
model of that 3D object. The 3D object (e.g., solidified material)
that is generated (e.g., for a customer) can have an average
deviation value from its intended dimensions (e.g., as specified by
its respective 3D model) of at most about 0.5 microns (.mu.m), 1
.mu.m, 3 .mu.m, 10 .mu.m, 30 .mu.m, 100 .mu.m, 300 .mu.m
afore-mentioned values (e.g., from about 0.5 .mu.m to about 300
.mu.m, from about 10 .mu.m to about 50 .mu.m, from about 15 .mu.m
to about 85 .mu.m, from about 5 .mu.m to about 45 .mu.m, or from
about 15 .mu.m to about 35 .mu.m). The 3D object can have a
deviation from the intended dimensions (e.g., model dimensions) in
at least one specific direction. The deviation in at least one
specific direction can follow the formula Dv+L/K.sub.dv, wherein Dv
is a deviation value, L is the length of the 3D object in a
specific direction, and K.sub.dv is a constant. Dv can have a value
of at most about 300 .mu.m, 200 .mu.m, 100 .mu.m, 50 .mu.m, 40
.mu.m, 30 .mu.m, 20 .mu.m, 10 .mu.m, 5 .mu.m, 1 .mu.m, or 0.5
.mu.m. Dv can have any value between the afore-mentioned values
(e.g., from about 0.5 .mu.m to about 300 .mu.m, from about 10 .mu.m
to about 50 .mu.m, from about 15 .mu.m to about 85 .mu.m, from
about 5 .mu.m to about 45 .mu.m, or from about 15 .mu.m to about 35
.mu.m). K.sub.dv can have a value of at most about 3000, 2500,
2000, 1500, 1000, or 500. K.sub.dv can have a value of at least
about 500, 1000, 1500, 2000, 2500, or 3000. K.sub.dv can have any
value between the afore-mentioned values. K.sub.dv can have a value
that is from about 3000 to about 500, from about 1000 to about
2500, from about 500 to about 2000, from about 1000 to about 3000,
or from about 1000 to about 2500. For example, the generated 3D
object may deviate from the requested 3D object by at most about
the sum of 100 micrometers and 1/1000 times the fundamental length
scale of the requested 3D object. The generated 3D object may
deviate from the requested 3D object by at most about the sum of 25
micrometers and 1/2500 times the fundamental length scale of the
requested 3D object.
[0143] The result may aid to generate and/or alter 3D forming
(e.g., printing) instructions. The forming (e.g., printing)
instructions may comprise the geometry of a desired 3D object and
optionally an alteration (e.g., a change) thereof. The alteration
may be a geometric alteration. The alteration may comprise a
corrective alteration (e.g., corrective deviation, corrective
deformation, or object pre-correction). The forming (e.g.,
printing) instructions may comprise altering one or more process
parameters of the 3D printing. For example, the forming (e.g.,
printing) instructions may comprise controlling one or more energy
beam characteristics (e.g., power density, path, and/or hatching),
which can individually or collectively be altered. In some
embodiments, the energy beam path used during one or more forming
operations for forming an object is adjusted. In some embodiments,
the speed of the energy beam is varied depending on whether is
transforming a region (e.g., critical regions versus non-critical
regions) of the 3D object. A critical region can be one that is
prone to deformation (e.g., during and/or after the forming
process). For example, the energy beam may be at a first speed when
transforming a first region of the object, and at a second speed
(e.g., slower or faster than the first speed) when transforming a
second region of the object. In some cases, this varied speed can
be used to adjust (e.g., optimize or increase) throughput while
maintaining quality of certain regions of the object. FIG. 3 shows
an example of an energy beam path 301. The path may comprise an
oscillating sub-path shown as a magnified path example 302. FIG. 4
shows various examples of energy beam paths and/or hatchings; for
example, paths 410, 411, and 416 comprise continuous paths; paths
412, 413, 414, and 415 comprise discontinuous paths comprising a
plurality of sub paths (e.g., hatchings); and the arrows designate
the direction at which the energy beam travels along the paths or
sub-paths. The energy beam can be a scanning energy beam, tiling
energy beam, or a combination of both. Examples of scanning and
tiling energy beams are described in U.S. patent application Ser.
No. 15/435,065, filed on Feb. 16, 2017, which is incorporated by
reference herein in its entirety. In some embodiments, the one or
more processing parameters may be altered based on empirical data
collected during and/or after a forming process. For example,
comparison of a geometric model and a corresponding object (e.g.,
test object) can be used to determine regions of the object that
experienced more deformations than other regions. This information
can be used to modify the forming instructions (e.g., in these
regions) to at least partially compensate for such deformations.
For instance, a power density of the energy beam (e.g., laser beam)
can be modified (e.g., decreased or increased) as the energy beam
transforms a pre-transformed material of a region to a transformed
material. In some cases, the energy beam is modified from a
scanning energy beam to a tiling energy beam (or vice versa). In
some cases, the footprint of the energy beam on the exposes surface
of the material bed is modified. In some cases, the path of the
energy beam is modified. The comparison between the geometric model
(e.g., (virtual) model markers) and the object (e.g., physical
markers) can be performed in real time (e.g., during the forming of
the object), such that the one or more process modifications can
occur in situ. In this way, the one or more markers may serve as a
tracking device of a forming (e.g., printing) process. Real time
may be during forming of the 3D object, a plurality of layers of
the 3D object, a layer of the 3D object, a hatch line as part of a
layer of a 3D object, a plurality of hatch lines, a melt pool, or a
plurality of melt pools. A plurality may be any integer number from
2 to 10. A plurality may be any integer number of at least 2, or of
at least 10.
[0144] The comparison between the test model and the test object
(e.g., test 3D object) may give a result. The comparison of a
metrological characteristics (e.g., distance and/or shape) between
at least two markers in test model and the respective at least two
markers of the test object (e.g., test 3D object) may give a
result. The comparison of a metrological characteristics of at
least one marker in test model and the respective at least one
marker of the test object (e.g., test 3D object) may give a result.
The metrological characteristics of a marker may comprise its FSL,
shape, or volume.
[0145] In some embodiments, the test 3D object is different from
the 3D object at least due to the presence of one or more markers
in the test 3D object. The one or more markers may be chosen such
that the difference between the test 3D object and the requested
(e.g., desired) 3D object is insubstantial. Insubstantial change
may be relative to a mechanical variation and/or deformation (e.g.,
of the portion where the one or more markers reside). For example,
when the metrological characteristics measured is a distance
between the (e.g., center) of two markers, and the comparison of
this respective distance between the test model and the test 3D
object, a small change is one that is at most B according to the
following metric: (a measured distance between a first marker and a
second marker in the test 3D object), divided by (a measured
distance between the respective first marker and a second marker in
the test model)=1+B. B can be at most about 0.001, 0.005, 0.01,
0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, or 0.2. B can
be between any of the afore-mentioned values (e.g., from about
0.001 to about 0.2, from about 0.001 to about 0.05, from about 0.01
to about 0.06, from about 0.04 to about 0.07, or from about 0.06 to
about 0.2). B may be specific to a material (e.g., material type).
The result may allow empirical estimation and/or prediction of a
deformation of at least one portion of the 3D object (e.g.,
adjacent to the marker and/or including the marker). The result may
allow the design of forming (e.g., printing) instructions (e.g., 3D
printing instructions) that result in reduction of deformation in
at least a portion of the 3D object. The result may allow the
design of forming (e.g., printing) instructions (e.g., 3D printing
instructions) that result high fidelity forming (e.g., printing).
The result may aid in understanding and/or differentiating between
various mechanism that cause deformation and/or addition to at
least a portion of the 3D object. The result may aid in predicting
various mechanism that cause deformation and/or addition to at
least a portion of the 3D object. For example, differentiation
between expansion and extension mechanisms. For example, various
mechanisms leading to dimensional inaccuracy.
[0146] In some embodiments, empirical methods without the use of
markers are used to estimate an amount of expected deformation. For
example, a registration process involving applying a rigid-body
transformation from coordinates of a point cloud to a CAD
coordinate system can be used. In some cases, using markers
(whether they are added or are pre-existing features) can provide
improved results over registration processes. For example, in some
cases, a forming process (or other suitable transformation process)
can result in a large degree of deformation when compared to an
original geometric model having a requested geometry. Using markers
at different regions of the object can reduce errors related to
registration.
[0147] The one or more markers can have any suitable size(s). The
marker may be small. Small may be relative to the 3D object,
portion of the 3D object on which the marker is located. Small may
be relative to a different between a presence or absence of the
marker on the model of the 3D object, as measured by a (e.g.,
small, inconsequential, or negligent) difference in the physical 3D
object formed based on the model of the 3D object. For example, if
a non-marked 3D object is formed (e.g., printed) based on a
non-marked model, and a marked 3D object is formed (e.g., printed)
based on a marked model, and both marked 3D object and non-marked
3D object are substantially identical, then the mark size may be
referred to as small. Substantial can be relative to the intended
purpose of the 3D object.
[0148] In some embodiments, the sizes of the markers can depend on
the forming process (e.g., thickness of each layer) and/or an
imaging process (e.g., resolution of the imaging process). For
example, in some cases the markers are insignificant enough (e.g.,
small enough) to have a (e.g., substantially) negligible effect on
the forming operation. In some cases, the markers are significant
enough (e.g., large and/or dense enough) to be detectable using an
imaging system (e.g., using light, x-ray, or other electromagnetic
radiation), such as a scanner (e.g., a 3D Computerized Tomography
(CT) scanner). In some embodiments, the one or more markers have
FLS (e.g., diameters or lengths) of at most about 0.01 mm, 0.05 mm,
0.1 mm, 0.25 mm, 0.30 mm, 0.50 mm, 0.75 mm, 0.8 mm, 1.0 mm, 1.25
mm, 1.3 mm, 1.5 mm, 1.75 mm, 1.8 mm, 2.0 mm, 2.5 mm, 3.0 mm, 4.0
mm, 4.5 mm, 5.0 mm, 10.0 mm, 20.0 mm, 50 mm, or 100 mm. In some
embodiments, the one or more markers have FLS (e.g., diameters or
lengths) of any value between the afore-mentioned values (e.g.,
from about 0.01 mm to about 100 mm, from about 0.01 mm to about 5.0
mm, or from about 1.5 mm to about 5.0 mm). The locations of the one
or more markers may be assessed using metrological measurements.
The location may include the center(s) and/or edge(s) of the
marker, an FLS of the marker(s) (e.g., diameter(s), spherical
equivalent diameter(s), diameter(s) of a bounding circle, or
largest of height(s), width(s) and length(s)), and/or volume(s),
and/or shape(s) of the marker(s).
[0149] The one or more markers can have any suitable shape(s). In
some embodiments, the one or more markers have a 3D shape, such as
one or more of a spherical, hemispherical, ellipsoid, cone, or
polyhedron shape. In some embodiments, the one or more markers are
conducive to dense packing (e.g., spherical close packing, e.g.,
body centered cubic (BCC), face centered cubic (FCC), or hexagonal
close-packed (HCP) arrangement). In some embodiments, the one or
more markers have a 2D shape, such as one or more of a circular,
elliptical, or polygonal shape. In some embodiments, at least two
of the one or more markers of a 3D object have (e.g.,
substantially) the same shapes. In some embodiments, at least two
of the one or more markers of a 3D object have different shapes, or
different sets of shapes. In some embodiments, at least one of the
one or more markers of a 3D object is composed of the same material
as a rest of the 3D object that excludes the markers. In some
embodiments, at least one of the one or more markers of a 3D object
is composed of a different material than the rest of the 3D object
that excludes the markers. In some embodiments, at least one of the
markers has a different material density than the rest of the 3D
object that excludes the markers. In some embodiments, the one or
more markers correspond to defects (e.g., material inconsistencies,
or pores) of the 3D object. In some cases, the one or more markers
are lines (e.g., 2D lines, or 3D raised or recessed lines, e.g.,
tessellation borders). In some cases, the one or more markers are
ridges, edges, borders, rims and/or boundaries along a surface of
the 3D object. In some embodiments, the one or more markers include
a number of lines (e.g., raised lines), and/or ridges. The lines
and/or ridges may be organized in a pattern (e.g., mesh pattern,
tessellations, or grid). In some embodiments, the one or more
markers include (or be transformed into) one or more point-clouds.
The point clouds may correspond to data points in X, Y, Z
coordinate system. In some embodiments, the point cloud represents
an external surface (or part of an external surface) of an object.
The point clouds may be generated from an image of a 3D object
(e.g., using any suitable 2D and/or 3D scanning technology and
methodology). In some embodiments, the one or more markers
correspond to features (e.g., mesh lines, tessellations, grid
lines) of one or more models (e.g., polygon mesh, triangle mesh,
non-uniform rational basis spline (NURBS), and/or computer-aided
design (CAD)) generated from one or more point clouds. In some
embodiments, the one or more markers correspond to augmented
reality (AR) code (e.g., embossed AR code).
[0150] The locations of the physical (e.g., structural) one or more
markers may be assessed using metrological measurements. The
location may comprise the center and/or edge of the mark, its FLS
(e.g., the diameter, spherical equivalent diameter, diameter of a
bounding circle, or largest of height, width and length) its
volume, and/or its overall shape. Examples of metrological
measurements can be found in PCT application PCT/US2015/065297;
U.S. patent application Ser. No. 15/435,090; PCT patent application
serial number PCT/US17/18191; European patent application serial
number EP17156707.6; U.S. patent application Ser. No. 15/435,065;
and in U.S. provisional patent application Ser. No. 62/401,534,
each of which is incorporated by reference in its entirety.
[0151] According to one or more deviations of the actual marked 3D
object from its respective position on the marked model of the 3D
object, the forming (e.g., printing) instructions for the desired
3D object may be varied. The variation may comprise a geometric
variation. The variation may comprise object pre correction (also
referred to as "object pre-forming corrections," or "object
pre-print correction", abbreviated as "OPC"). The OPC may comprise
geometric corrections of a model of the 3D object, for example, as
part of a print preparation procedure of the 3D object (e.g.,
preparing the printing instructions). Variation of the printing
instructions may comprise a variation of the model of the 3D object
that will result in printing a 3D object comprising a lower degree
of deformation as compared to the originally printed 3D object that
is printed with the non-varied printing instructions. The creation
of the printing instructions (e.g., comprising OPC) may comprise
using simulations. The simulations may utilize the variations
between the test model (i.e., model of the marked 3D object), and
the test object (i.e., the printed marked 3D object).
[0152] A corrective modification can include an alteration (e.g., a
geometrical alteration) of a model of a desired 3D object. The
altered model of the 3D object may result (e.g., though using
respective forming (e.g., printing) instructions) in a 3D object
that is substantially similar to the desired 3D object (e.g., to
the non-altered model of the 3D object). Corrective modification
may be any corrective deformation disclosed in: Patent Applications
serial number PCT/US16/34857 that was filed on May 27, 2016, titled
"THREE-DIMENSIONAL PRINTING AND THREE-DIMENSIONAL OBJECTS
THREE-DIMENSIONAL PRINTING AND THREE-DIMENSIONAL OBJECTS FORMED
USING THE SAME;" Provisional Patent Application Ser. No.
62/401,534, PCT patent application serial number PCT/US17/18191;
European patent application serial number EP17156707.6; and in U.S.
patent application Ser. No. 15/435,065, each of which is entirely
incorporated herein by reference. The corrective modification of
the intended 3D structure may be termed herein as "geometric
modification."
[0153] FIG. 5 shows examples of stages in formation of a 3D object
503 represented as three layers (e.g., numbered 1-3 in object 503),
which is shown as a vertical cross section, and is situated on a
platform 504. The first formed layer is formed as a negatively
curved layer #1 of object 501. Once the second layer (#2 of object
502) is formed, the first layer #1 may flatten out (e.g., its
radius of curvature is increased, its curvature approaches zero).
Once the third layer (#3 of object 503) is formed, the layers of
the 3D object become (e.g., substantially) flat (e.g., planar).
Layer #1 may be said to be formed as a correctively modified (e.g.,
deformed) layer. The corrective modification may enable a formation
of a substantially non-deformed 3D object. FIG. 6 shows an example
of a 3D object 604 that was formed (e.g., printed) according to a
model 603, which model slices (e.g., 605) were deformed during
and/or after the 3D forming (e.g., printing) 608 resulting in the
desired 3D object 604 comprising the respective layer 606. FIG. 6
shows an example of a corrective modification, depicted as a
vertical cross section of the model (603) and its respective formed
(e.g., printed) (604) 3D object. The manner of forming (e.g.,
printing) one or more subsequent layers to the correctively
modified layers may take into account (e.g., in situ and/or real
time) measurements from one or more sensors of the system (e.g., 3D
printer). The corrective modification may comprise a model of a
layer of hardened material as part of the 3D object, or a portion
of that layer (e.g., as represented in the model of the 3D object).
The corrective modification may be of the model of a requested 3D
object. The corrective modification may be corrective deviation
and/or deformation. FIG. 7 shows an example of a forming (e.g., 3D
printing) system comprising a 3D object 701, a sensor 718 which
senses the returning beam 720 that was emitted 719 by an emitter
717 (e.g., energy source 717).
[0154] In some embodiments, the requested 3D object and/or test 3D
object may be formed (e.g., printed) to completion. In some
instances, a portion of the requested 3D object and/or test 3D
object may be formed (e.g., printed). For example, different stages
in the forming (e.g., printing) of the 3D object and/or test 3D
object may be formed (e.g., printed). FIG. 8 shows an example of a
model of a 3D object 823 that is used in the forming (e.g.,
printing) instructions for a formed (e.g., printed) 3D object 824.
The 3D forming (e.g., printing) instructions comprise forming
(e.g., printing) the 3D object layer-wise. FIG. 8 shows an example
of a model comprising a multiplicity (e.g., plurality) of slices
(e.g., 805, 815, 825) each of which corresponds to a respective
layer (e.g., of hardened material) in the 3D object (e.g., 806,
816, 826, respectively). An example of various stages of the
forming (e.g., printing) can be depicted in the 3D objects 804,
814, and 824. For example, at a first stage: a first portion 803
(including slice 805) is formed (e.g., printed) 808 to form object
804 having layer 806 (corresponding to slice 805); at a second
stage: a second portion 813 (including slice 815) is formed (e.g.,
printed 818) to form object 814 having layer 816 (corresponding to
slice 815); and at a third stage: a third portion 823 (including
slice 825) is formed (e.g., printed) 828 to form object 824 having
layer 826 (corresponding to slice 825). The different stages (e.g.,
804, 814, and 824) can be each formed (e.g., printed) in a separate
material bed (e.g., during separate 3D forming (e.g., printing)
processes, in which case the forming (e.g., printing) processes
808, 818, 828 in the example in FIG. 8 are separate). The different
stages can be formed (e.g., printed) simultaneously in a material
bed (e.g., during one 3D forming (e.g., printing) process, in which
case the forming (e.g., printing) processes 808, 818, 828 in the
example in FIG. 8 are the same forming (e.g., printing) process).
In some instances, more than one stage may be formed (e.g.,
printed) together in one material bed. Forming (e.g., printing) of
several stages of the test 3D object, and a comparison to its test
model, may allow monitoring (e.g., though inspection of the
markers) of the development of deformation in the test object. The
comparison may highlight varied degrees of deformation in different
portions of the 3D object. Subsequently design alteration of the
model of the 3D object and/or the 3D object may take place. In some
instances, the design alterations may substantially not alter the
formed (e.g., printed) 3D object. Substantially may be relative to
the intended purpose of the 3D object.
[0155] It should be noted that embodiments described herein are not
limited to a printing processes. The embodiments may be used during
any suitable forming process, or combination of forming processes.
The embodiments described herein can be used to generate a
corrected geometric model independent of the process(es) used to
form an object. The embodiments can be applied to any suitable
process that involves deformation of an object (e.g., dimensional
changes). The embodiments can be applied to any suitable process
that involves transformation of a state of material (e.g., solid to
liquid, liquid to solid.). The embodiments can be applied to any
suitable process that involves transformation of the distribution
of the material (e.g., powder to bulk). The embodiments can be
applied to any suitable process that involves transformation of the
microstructure of the material (e.g., solid-solid transformation,
transformation in metallurgical and/or crystal structure). For
example, the embodiments may be used in molding (e.g., injection
molding), casting, extruding, welding (e.g., laser welding),
cladding, machining, polishing, buffing, or any suitable
combination thereof, e.g., including in combination with any
suitable printing (e.g., 3D printing) processes. The embodiments
described herein may not be limited to any type of printing
process. For example, the printing process can include one or more
selective of: laser melting (SLM), selective laser sintering (SLS),
direct metal laser sintering (DMLS), shape deposition manufacturing
(SDM), green body techniques, and fused deposition modeling (FDM)
processes. Other methods can include those that involve curing
liquid materials, such as stereo lithography (SLA) processes. Other
methods can include laminated object manufacturing (LOM)
processes.
[0156] A result of the comparison between a model of an object and
an object may allow for performing a weighted deformation of
various portions of an adjusted geometric model for forming (e.g.,
printing) a subsequent 3D object. The result may afford a metric
for the estimation of the forming (e.g., printing) fidelity of
various portion of the 3D object. In this manner, a designer may
include its intent (e.g., as design constrains) into the forming
(e.g., printing) instructions, which may allow an intent based
variation of the forming (e.g., printing) instructions (e.g., and
the model of the 3D object). A result may aid in formation of a
success metric for the formed (e.g., printed) 3D object (e.g.,
based on design intent).
[0157] The one or more markers may be placed (e.g., substantially)
homogenously across a model of the 3D object. The one or more
markers may be (e.g., strategically) placed in certain locations
(e.g., portions) of the model of the 3D object. The type and/or
positions of the markers may be chosen by a customer (e.g., a
client). In some embodiments, the location of the markers can be
chosen based on a geometry of the 3D object. For example, in some
cases more markers may be positioned on/in portions of a 3D object
that are expected to experience more deformation relative to other
portions of the 3D object (which can have less markers or no
markers). In some embodiments, the positions of the markers are
chosen based on mathematical calculation (e.g., Poisson disk
sampling or minimum marker-to-marker and marker-to edge Euclidean
distance matrix).
[0158] Systems, apparatus, software and methods presented herein
can facilitate formation of custom or stock 3D objects for a
customer. A customer can be an individual, a corporation,
organization, government, non-profit organization, company,
hospital, medical practitioner, engineer, retailer, any other
entity, or individual. The customer may be one that is interested
in receiving the 3D object and/or that ordered the 3D object. A
customer can submit a request for formation of a 3D object. The
customer can provide an item of value in exchange for the 3D
object. The customer can provide a design or a model for the 3D
object. The customer can provide the design in the form of a stereo
lithography (STL) file. The customer can provide a design where the
design can be a definition of the shape and dimensions of the 3D
object in any other numerical or physical form (e.g., structure).
In some cases, the customer can provide a 3D model, sketch, or
image as a design of an object to be generated. The design can be
transformed in to instructions usable by the forming (e.g.,
printing) system to additively generate the 3D object. The customer
can provide a request to form the 3D object from a specific
material or group of materials (e.g., a material as described
herein). In some cases, the design (e.g., model of the 3D object)
may not contain auxiliary features or marks of any past presence of
auxiliary support features.
[0159] In response to the customer request the 3D object can be
formed or generated with the forming (e.g., printing) method,
system, software and/or apparatus (e.g., embodiments) as described
herein. In some cases, the 3D object can be formed by an additive
3D printing process. Additively generating the 3D object can
comprise successively depositing and transforming a pre-transformed
material comprising one or more material types (e.g., as specified
by the customer). The 3D object can subsequently be delivered to
the customer. The 3D object can be formed with or without the
generation or removal of auxiliary features (e.g., that is
indicative of a presence or removal of the auxiliary support
feature). Auxiliary features can be support features that prevent a
3D object from shifting, deforming and/or moving during the 3D
forming (e.g., printing).
[0160] The one or more markers may be detected via an analytical
method (e.g., as shown in FIG. 7). The analytical method may
comprise using a metrology detector (e.g., metrological mapping).
The analytical method may comprise using temperature mapping. The
analytical method may comprise using optical surface scanning
technology as a method to detect the surface markers. The optical
surface scanning may comprise three-dimensional optical surface
scanning. For example, Computerized Tomography (i.e., CT) scan can
be used to view the markers (e.g., interior markers). In some
embodiments, structured light 3D scanning is used. The CT scan may
be performed after and/or during the forming (e.g., 3D printing)
process. In some embodiments, the imaging is performed by an
imaging system, such as a sensing (e.g., imaging) system 1200 shown
in FIG. 12, which will be described in detail herein. In some
embodiments, the imaging is performed in the system for forming the
object (e.g., 3D printing system). FIG. 7 shows an example of a 3D
printing system 700 comprising sensing (e.g., imaging) capability,
which will be described in detail herein. Various analytical
methods, systems, software, and apparatuses (e.g., as mentioned
herein) are disclosed in Patent Application serial number
PCT/US2015/065297 that was filed on Dec. 11, 2015, titled "FEEDBACK
CONTROL SYSTEMS FOR THREE-DIMENSIONAL PRINTING," U.S. patent
application Ser. No. 15/435,090, filed on Feb. 16, 2017, titled
"ACCURATE THREE-DIMENSIONAL PRINTING," PCT patent application
serial number PCT/US17/18191 filed on Feb. 16, 2017, titled
"ACCURATE THREE-DIMENSIONAL PRINTING," European patent application
serial number EP17156707.6 filed on Feb. 16, 2017, titled "ACCURATE
THREE-DIMENSIONAL PRINTING," U.S. patent application Ser. No.
15/435,065 filed Feb. 16, 2017, titled "ACCURATE THREE-DIMENSIONAL
PRINTING," and in U.S. provisional patent application Ser. No.
62/401,534 filed on Sep. 29, 2016, titled "ACCURATE
THREE-DIMENSIONAL PRINTING," each of which is incorporated herein
by reference in its entirety. The sensing (e.g., 3D
imaging/scanning) can be used to create corresponding
representative data (sometimes represented in image form) of an
object (also referred to herein as "sensor data," "image data,"
"image," "scan," "scan data," "scanned image," or "virtual data").
The image can be rendered on a computer as a reconstruction (e.g.,
3D reconstruction) of the object. It should be noted that in some
cases image data corresponds to a geometric model, as described
herein. If the object has markers (physical markers), the image of
the object can have markers (also referred to herein as "image
markers") corresponding to the physical markers of the object
(and/or model markers of a geometric model). FIG. 20A shows a
perspective view (photograph) of an example 3D object 2000 having a
disc cone shape (e.g., having a diameter of about 8 cm) and having
physical markers 2002. FIG. 20B shows a perspective view of an
example image 2010 of a 3D object (e.g., 2000) having image markers
2012 corresponding to physical markers (e.g., 2002). The scanned
image (and associated data) can include information regarding the
location of the images markers (marker point clouds) on a surface
of the object and/or an interior volume of the object. In some
cases, the imaging technique is chosen based on an accuracy (e.g.,
resolution) of the marker point clouds. In some embodiments, the
size (e.g., diameters or lengths) of the markers (e.g., physical
markers) is based on the accuracy (e.g., resolution) of the imaging
technique(s). In some embodiments, the one or more markers (e.g.,
each of the markers) have FLS (e.g., diameters or lengths) of at
least about 0.1 .mu.m, 0.50 .mu.m, 1.0 .mu.m, 2.0 .mu.m, 3.0 .mu.m,
4.0 .mu.m, 5.0 .mu.m, 10.0 .mu.m, 20.0 .mu.m, 50 .mu.m, or 100
.mu.m. In some embodiments, the one or more markers have a FLS
(e.g., diameters or lengths) of any value between the
afore-mentioned values (e.g., from about 0.1 .mu.m to about 100
.mu.m, from about 5.0 .mu.m to about 50 .mu.m, or from about 5.0
.mu.m to about 100 .mu.m).
[0161] As described herein, in some embodiments, a 3D printing
system (e.g., 700 of FIG. 7) can be configured to print a 3D object
(e.g., 701), as well as detect markers on and/or in the 3D object.
In some embodiments, the detection is performed in a 3D printing
system in situ and/or in real time during a 3D printing process
(e.g., as described herein). In some embodiments, the detection is
performed in a forming system in which the 3D object is formed,
during, before and/or after the forming process. For example, in
some embodiments, the detection is performed in a 3D printing
system during, before and/or after a 3D printing process. In some
embodiments, the detection is performed using a sensing (e.g., an
imaging) system (which can also be referred to as a scanning system
or detection system). The sensing system can include one or more
sensors and/or detectors. The one or more sensors and/or detectors
can be operationally coupled to one or more controllers. The
sensing system can be separate from the forming (e.g., 3D printing)
system used to form (e.g., print) the 3D object. For example, an
sensing system can be stand-alone sensing system (e.g., dedicated
to imaging one or more 3D objects) or be part of system for forming
the 3D object (e.g., a machining system (e.g., a computer numerical
control (CNC) machine), molding system, laminating system, or other
system described herein). In some embodiments, the sensing system
is part of a 3D printing system (e.g., that is the same or
different from the 3D printing system used to print the 3D object).
The sensing system can include an emitter, which can include one or
more energy sources (e.g., light source(s), X-ray source(s) and/or
electron beam(s)). The emitter can be configured to emit and direct
one or more energy beams toward the 3D object (e.g., toward a
surface of the 3D object). The one or more energy beams can
interact with the 3D object. For example, the one or more energy
beams can reflect (e.g., regularly reflect and/or irregularly
reflect) off of a surface of the 3D object, diffract off the 3D
object, refract as it passes through the 3D object, and/or
otherwise be affected by the 3D object. A sensor can be used to
detect a returning beam after interacting (e.g., impinging on) with
the 3D object. The imaging system can include any suitable type of
imaging and imaging/scanning mechanism. In some embodiments, the
imaging system includes one or more of X-ray detectors (e.g., CT
scanner). The imaging system may scan the 3D object in two
dimensions and/or three dimensions. FIG. 12 shows an example
sensing system 1200 that includes an emitter 1217, which is
configured to emit energy beam 1219, and sensor 1218, which is
configured to detect returning beam 1220 that is reflected off 3D
object 1201. In some embodiments, at least one sensor is configured
to sense one or more markers of a 3D object. One or more
controllers can be configured to (i) control sensing and/or (ii)
use sensing data, of the one or more markers of the 3D object. One
or more controllers can be configured to (i) control sensing and/or
(ii) use sensing data, of the one or more physical markers during
forming of the 3D object. One or more controllers can be configured
to (i) control sensing and/or (ii) use sensing data, of the one or
more physical markers, e.g., after forming of the 3D object. In
some embodiments, at least one detector can be configured to detect
as least one characteristic of the forming of the 3D object. The at
least one characteristic of the forming of the 3D object can
comprise a process parameter, the material used for the forming,
the geometric model, the physical model, and the alteration of
forming 3D object. One or more controllers can be configured to
control the at least one detector and/or control one or more
process parameters as a result of a detection by the at least one
detector. The one or more detectors can be configured to detect a
temperature during the forming of the 3D object. One or more
controllers can be configured to control (e.g., monitor) detection
of the temperature. The temperature can correspond to a temperature
of the 3D object and/or a vicinity of the 3D object. Vicinity can
be in a material bed that is configured to accommodate the 3D
object. Vicinity can be in a material bed that is configured to
accommodate the 3D object. The temperature can correspond to a
temperature of a melt pool and/or a vicinity of the melt pool
(e.g., up to 2, 3, 4, 5, 6, or 7 diameters of a FLS of a melt pool
generated during the forming, which diameters are centered at the
melt pool). The temperature can correspond to a temperature of an
atmosphere surrounding the 3D object. The one or more detectors can
be configured to detect at least one of cleanliness, pressure,
humidity, or oxygen level of an atmosphere surrounding the
three-dimensional object during the forming of the 3D object,
detecting a cleanliness can include detecting a number (e.g.,
amount or concentration) of particles within at least the
processing cone of the atmosphere within the processing
chamber.
[0162] The shape and/or size of the markers may allow variation in
density from an analytical standpoint. For example, holes of
difference density and/or size may allow several levels of markers
geometry that may be revealed in a CT scan. For example, Various CT
scan methodologies are disclosed in patent application
PCT/US2015/065297 which is incorporated herein by reference in its
entirety.
[0163] In some embodiments, the positions/locations of the markers
are chosen based on the geometry and expected alteration (e.g.,
deformation) of the object that result from its formation. For
example, in some embodiments, marker locations are chosen based on
portions of a surface (or volume) of the geometric model with
tessellations (mesh) densities that are greater than a
predetermined density. In some embodiments, the orientation of the
markers with respect to a surface (or volume) of the geometric
model is controlled. For example, in some embodiments, a marker is
oriented (e.g., substantially) normal with respect to surface
location of the geometric model. In some embodiment, the geometric
model with the model markers, is further processed by altering the
geometric model to a tessellated version (i.e., having
tessellations (e.g., surface mesh)). FIGS. 19A-19C show perspective
views of an example geometric model of the requested object 1900
(e.g., computer aided design (CAD) drawing) and associated model
markers. FIG. 19A shows geometric model 1900 having a requested
geometry. FIG. 19B shows the geometric model after model markers
1902 (e.g., hemispherical recesses) are added to surfaces of the
geometric model. FIG. 19C shows the geometric model with markers
converted to tessellated versions 1904 (a surface mesh). An object
can be formed (e.g., printed) using instructions (e.g., printing
instructions) that consider (e.g., based on) the geometric model.
Any suitable system and associated forming process(es) can be used
to form the object, such as described herein. The instructions
(e.g., printing instructions) can include specifics related to the
forming process, e.g., including instructions for the forming of
multiple layers during the forming process, as described
herein.
[0164] Once an object (e.g., test object) is formed, the object can
be analyzed to determine the locations of the physical markers
in/on the object. The analytical methods may comprise using any
suitable sensing (e.g., imaging) apparatus. The analytical method
may monitor the markers statically and/or dynamically (e.g., in
real time during forming process (e.g., 3D printing)). At times,
the dynamic monitoring can take place when the analytical system
and/or apparatus is integrated within the system used to form the
object (e.g., 3D printer). Dynamic monitoring may refer to on-line
monitoring during the forming process (e.g., 3D printing). In some
embodiments, static monitoring refers to inspection of the partial
and/or complete marked 3D object subsequent to the forming (e.g.,
printing) operation. In some embodiments, static monitoring refers
to inspection of the partial and/or complete marked 3D object off
line. Real time may be during formation of, for example, at least
one of: 3D object, layer within the 3D object, dwell time of an
energy beam along a path, and dwell time of an energy beam along a
hatch line dwell time of an energy beam forming a melt pool. Real
time may be during the forming (e.g., 3D printing) process or any
portion thereof. For example, real time may be during the operation
of an energy beam. For example, real time may be during the
formation of the 3D object or any portion thereof. Real time
analysis may be effectuated when the analytical tool resides in the
system used to form the object (e.g., 3D printing chamber) (e.g.,
as shown in FIG. 7).
[0165] The forming (e.g., printing) of a test 3D object and
comparison to its respective test model may aid in the detection of
various problems, concerns and/or troubleshooting during a forming
(e.g., 3D printing) process. The test object may be formed (e.g.,
printed) along with a 3D object, for example, as an alignment
mechanism of the forming system (e.g., 3D printer), its setup
and/or its parameters.
[0166] An iterative forming (e.g., printing) process using a marked
3D object (e.g., empirical process) is shown as an example in FIG.
9. The process of developing forming (e.g., printing) instructions
for a requested (e.g., desired) 3D object may comprise: (i)
generating (e.g., 901) a test model (marked geometric model) (e.g.,
912) from a model (geometric model) of a requested 3D object (e.g.,
911) by inserting one or more markers (model markers), (ii)
generating a test object (e.g., 914) through a 3D forming (e.g.,
printing) process (e.g., 902), and (iii) measuring and analyzing
the test object (e.g., 904), and comparing (e.g., 913) between the
test model (marked geometric model) and the test object. The
creation of the forming (e.g., printing) instructions for a
requested (e.g., desired) 3D object may further comprise (iv)
altering the test model (e.g., geometric alteration) to generate a
subsequent test model (adjusted geometric model) (e.g., 905) in
returning to operation (i) and forming (e.g., printing) a
respective subsequent test 3D object. The development of the
forming (e.g., printing) instructions for a requested (e.g.,
desired) 3D object may comprise an iterative process (e.g.,
comprising 904, 905, and 902) until a satisfactory test 3D object
is reached. The test model of the satisfactory test 3D object may
serve as a basis for modification (e.g., 906) of the model of the
3D object (e.g., 911) to form a modified model of the 3D object
(e.g., 915), which in turn is used to form, e.g., print (e.g., 907)
the requested (e.g., desired) 3D object 916. The forming (e.g.,
printing) instructions for the 3D object (e.g., 916) may use the
modified model of the 3D object (e.g., 915). The iterative process
(e.g., comprising 904, 905, and 902) may comprise geometrical
calibration. The development of forming (e.g., printing)
instruction may (e.g., further) comprise simulations (e.g.,
simulated and/or semi-simulated). Semi-simulated may consider
(e.g., take into account) empirical measurements (e.g., of the test
3D object).
[0167] FIG. 13 shows flowchart 1300 indicating an example empirical
process for forming an object, in accordance with some embodiments.
A geometric model of the requested object (e.g., 1302) can be
obtained. A geometric model of the requested object can correspond
to a computer representation of the requested object (e.g., having
desired geometric dimensions), e.g., a virtual object. In some
cases, the geometric model of the requested object is provided, for
example, by a customer. In some cases, the geometric model of the
requested object is obtained or generated (e.g., using any suitable
3D rendering technique). In some embodiments, this involves
creating a NURBS and/or CAD drawing of the requested object having
desired geometric dimensions and/or other properties (e.g.,
density). In some embodiments, the geometric model of the requested
object is obtained by imaging an existing 3D object (e.g., composed
of a different material than that of a requested object). Imaging
can be performed using any suitable imaging/scanning
technology/instrumentation (e.g., CT scanning) One or more model
markers (virtual markers, e.g., image markers) can optionally be
added to the geometric model of the requested object (e.g., 1304).
The markers can be any markers, e.g., as described herein. In some
embodiments, the marker(s) are added by storing positions of the
nominal marker locations in the coordinate system of the geometric
model (e.g., CAD coordinate system). In some cases, the markers are
already included in the geometric model of the requested object.
The markers can include mesh lines, tessellation borders, tile
borders, grid lines, or other point cloud features of the geometric
model of the requested object. Point cloud features can be a set of
data points in a coordinate system. A test object can then be
formed (e.g., 1306) using instructions that consider (e.g., are
based on) the geometric model of the requested object. In some
embodiments, the forming process comprises a 3D printing process.
In some embodiments, the forming process comprises molding,
casting, extruding, or machining. The forming process can comprise
additive or subtractive processing. The forming process can
comprise chemical or physical layer deposition. The forming process
can comprise powder deposition. The forming process can comprise
layer-wise manufacturing. In some embodiments, a combination of
forming techniques are used, as suitable. The test object can
include one or more physical markers corresponding to the one or
more image makers of the geometric model of the requested object. A
geometric model of the test object can be generated (e.g., 1308)
based on the test object. For example, the geometric model of the
test object can correspond to an image of the test object generated
by using one or more sensing (e.g., imaging, (e.g., scanning))
techniques. The image of the test object can be used to determine
the image marker locations in an imaging system coordinate system
(e.g., scanner coordinates system). The geometric model of the test
object (e.g., aspects of the geometric model of the test object)
can then be compared to the geometric model of the requested object
(e.g., aspects of the geometric model of the requested object)
(e.g., 1310). In some embodiments, comparing comprises comparing
(i) positions of the model markers of the geometric model of the
requested object with (ii) positions of the model markers of the
geometric model of the test object. In some embodiments, comparing
comprises comparing at least one characteristic of the model
marker(s). The at least one characteristic of the model markers may
comprise location (e.g., relative location), shape, volume, cross
section, and/or sizes of the model markers. Comparing can comprise
performing one or more regression analyses. Comparing can comprise
determining whether data (e.g., location of model markers)
associated with the geometric model of the test object (e.g.,
substantially) converges with data (e.g., location of the model
markers) associated with the geometric model of the test object
(e.g., 1312). Comparing can comprise determining a correspondence
between the model markers (e.g., locations of the model markers)
and the image markers (e.g., locations of the image markers). In
some embodiments, the comparing is of location, shape, volume,
fundamental length scale, and/or a material property. The data may
comprise the at least one characteristic of the model marker(s).
Determining convergence can involve determining whether an amount
of deviation (if any) between the at least one characteristic of
the model marker(s) of the geometric model of the test object and
the respective at least one characteristic of the model marker(s)
of the geometric model of the requested object, are within a
threshold range. For example, the threshold range can correspond to
a statistically calculated acceptable deviation using any suitable
calculation techniques, such as those described herein. In some
embodiments, the comparing involves using distance matrices,
regression analyses and/or displacement vectors as described
herein. If it is determined that data associated with the geometric
model of the test object does not (e.g., substantially) converge
with data associated with the geometric model of the test object, a
geometric model for forming the test object (initially, the
geometric model of the requested object) can be adjusted (e.g.,
corrected) (e.g., 1314). In some embodiments, the geometric model
used for the forming process is adjusted (e.g., corrected) using
one or more optimization calculations. In some cases, the
optimization involves adjusting the locations (e.g., virtually
adjusting) the model markers of the geometric model by a function
of a computed displacement vector (e.g., as discussed below). For
example, in some embodiments, the locations are adjusted by the
computed displacement vector multiplied by negative one. A
geometric deformer (e.g., b-spline free form deformer) can be used
to extend the adjusted model marker locations of the (e.g., entire)
geometric model. This process can be iteratively repeated until,
for example, (e.g., substantial) convergence (e.g., 1312).
[0168] In some embodiment, markers (e.g., model markers) are
optionally added to the adjusted geometric model used for the
forming process (e.g., repeating 1304). In some embodiments,
markers (e.g., model markers) are not added to the adjusted
geometric model. Another (e.g., second) test object can be formed
(e.g., repeating 1306), another e.g., second) geometric model of
the test object can be generated (e.g., repeating 1308), which can
be compared to the geometric model of the requested object (e.g.,
repeating 1310) to determine (e.g., substantial) convergence (e.g.,
1312). In some embodiments, the cycle of adjusting (e.g., 1314),
optional adding markers (e.g., 1304), forming (e.g., 1306),
generating (e.g., 1308), comparing (e.g., 1310), and convergence
determining (e.g., 1312), can be repeated until data associated
with the geometric model of the test object (e.g., substantially)
converges with data associated with the geometric model of the
requested object. If it is determined that data associated to the
geometric model of the test object (e.g., substantially) converge
with data associated with the geometric model of the requested
object, the markers (if used) can optionally be removed from the
geometric model considered in (e.g., used for) the forming process
(e.g., 1316) and a corrected geometric model can be generated
(e.g., 1318). In some embodiments, the corrected geometric model
corresponds to the last adjusted geometric model that is used in
the forming process. The corrected geometric model can then be used
to form the requested object (e.g., 1320). The corrected geometric
model (or finally adjusted geometric model used for forming) can be
used to form (e.g., print) multiple requested objects. In some
embodiments, the markers used are inherent object markers (e.g.,
tessellation borders), and the operation of adding marker(s) (e.g.,
1304) is not exercised.
[0169] At times, it is desirable to monitor deformation in
primitive portions of a 3D object. A primitive portion may be a
(e.g., characteristic) portion of one or more 3D objects. The
process of developing forming (e.g., printing) instructions for a
3D object primitive portion may comprise: (i) generating a test
model of the primitive portion, (ii) generating a test object, and
(iii) comparison between the two. The creation of the forming
(e.g., printing) instructions for a desired 3D object may further
comprise (iv) altering the test model of the primitive portion
(e.g., geometric alteration), and returning to operation (i). The
development of the forming (e.g., printing) instructions for a
desired 3D object primitive portion may further comprise an
iterative process until a satisfactory 3D object may be generated
using the forming (e.g., printing) instructions. The iterative
process may comprise geometrical calibration. The development of
forming (e.g., printing) instruction may (e.g., further) comprise
simulations.
[0170] The result and/or iterative process may comprise using a
learning algorithm. The learning algorithm may comprise neural
networks, or machine learning. The learning algorithm may comprise
pattern recognition. The learning algorithm may comprise artificial
intelligence, data miming, computational statistics, mathematical
optimization, predictive analytics, discrete calculus, or
differential geometry. The learning algorithms may comprise
supervised learning, reinforcement learning, unsupervised learning,
semi-supervised learning. The learning algorithm may comprise
bias-variance decomposition. The learning algorithm may comprise
decision tree learning, associated rule learning, artificial neural
networks, deep learning, inductive logic programming, support
vector machines, clustering, Bayesian networks, reinforcement
learning, representation learning, similarity and metric learning,
sparse dictionary learning, or genetic algorithms (e.g.,
evolutional algorithm). The non-transitory computer media may
comprise any of the algorithms disclosed herein. The controller
and/or processor may comprise the non-transitory computer media.
The software may comprise any of the algorithms disclosed herein.
The controller and/or processor may comprise the software.
[0171] The forming (e.g., printing) instructions of the 3D object
may comprise one or more auxiliary supports. The use of the test
model and test object comparison (e.g., comparison of their
respective one or more markers) may allow estimating the
deformation(s) associated with removal of the formed (e.g.,
printed) 3D object from the platform (e.g., build plate) by
severing the supports. This may lead to better understanding of
residual stress and/or deformation imparted on the 3D object by the
forming (e.g., printing) process due to the presence of auxiliary
support structures. This may lead to methodologies for forming
(e.g., printing) 3D objects with minimal number of auxiliary
supports, minimal stress, and/or minimal deformation. For example,
this may allow strategic removal of one or more auxiliary supports
from a model of the 3D object (e.g., that is used for forming
(e.g., printing) instruction for the 3D object). Consequently, this
may allow forming (e.g., printing) a 3D object with minimal number
of auxiliary supports. The removal of the one or more auxiliary
supports from a model of the 3D object may allow generation of a 3D
object with minimal auxiliary support. At times, forming (e.g.,
printing) a 3D object with a reduced number of auxiliary supports
(e.g., elimination thereof) may ease post processing of the
generated 3D object to form the requested 3D object. In some
embodiments, post processing refers to a procedure performed on the
3D object after its forming (e.g., printing) process (e.g.,
utilizing the energy beam) has been completed.
[0172] Post processing (e.g., further processing) may comprise
trimming (e.g., ablating). Further processing (e.g., also referred
to herein as "post processing") may comprise polishing (e.g.,
sanding). The 3D object can be devoid of surface features that are
indicative of the use of a trimming process during or after the
formation of the three-dimensional object. The trimming process may
be an operation conducted after the completion of the forming
(e.g., 3D printing) process. The trimming process may be a separate
operation from the forming (e.g., 3D printing) process. The
trimming may comprise cutting (e.g., using a piercing saw). The
trimming can comprise polishing or blasting. The blasting can
comprise solid blasting, gas blasting or liquid blasting. The solid
blasting can comprise sand blasting. The gas blasting can comprise
air blasting. The liquid blasting can comprise water blasting. The
blasting can comprise mechanical blasting. The trimming may
comprise mechanical trimming or optical trimming (e.g., annealing
using an energy beam). In some cases, the generated 3D object can
be retrieved from the system used to form the 3D object, (e.g., 3D
printer) and delivered to the customer without removal of
transformed material and/or auxiliary features. The 3D object can
be retrieved when the 3D part, composed of hardened (e.g.,
solidified) material, is at a handling temperature that is suitable
to permit the removal of the 3D object from the material bed
without substantial deformation.
[0173] In some instances, the 3D object may require post processing
(e.g., heat treatment such as, for example, annealing). Some post
processing procedures may impart deformation on the processed 3D
object after its forming (e.g., 3D printing). The use of the test
model and test object comparison (e.g., comparison of their
respective one or more markers) after the test object has been post
processed, may allow understanding of the nature and/or extend of
imparting the deformation.
[0174] The term "auxiliary features," as used herein, generally
refers to features that are part of a formed (e.g., printed) 3D
object, but are not part of the requested (e.g., desired, intended,
designed, ordered, modeled, or final) 3D object. Auxiliary features
(e.g., auxiliary supports) may provide structural support during
and/or subsequent to the formation of the 3D object. Auxiliary
features may enable the removal or energy from the 3D object that
is being formed. Auxiliary features may enable reduction of
deformations of at least a portion of a generated 3D object, which
would otherwise manifest themselves. Examples of auxiliary features
comprise heat fins, wires, anchors, handles, supports, pillars,
columns, frame, footing, scaffold, flange, projection, protrusion,
mold (a.k.a. mould), building platform (e.g., base), or other
stabilization features. In some instances, the auxiliary support is
a scaffold that encloses the 3D object or part thereof. The
scaffold may comprise lightly sintered or lightly fused powder
material. The 3D object can have auxiliary features that can be
supported by the material bed (e.g., powder bed) and not touch the
platform (e.g., base, substrate, container accommodating the
material bed, or the bottom of the enclosure). The 3D part (3D
object) in a complete or partially formed state can be completely
supported by the material bed (e.g., without touching the
substrate, base, container accommodating the material bed, or
enclosure). The material bed may comprise a flowable (e.g., not
fixed) material during the forming (e.g., 3D printing) process. The
3D object in a complete or partially formed state can be completely
supported by the material bed (e.g., without touching anything
except the material bed). The 3D object in a complete or partially
formed state can be suspended in the material bed without resting
on any additional support structures. In some cases, the 3D object
in a complete or partially formed (i.e., nascent) state can freely
float (e.g., anchorless) in the material bed.
[0175] In some embodiments, the present disclosure relates to 3D
printing apparatuses, systems, software, and methods for forming a
3D object. For example, a 3D object may be formed by sequential
addition of material or joining of pre-transformed material to form
a structure in a controlled manner (e.g., under manual or automated
control). Pre-transformed material, as understood herein, is a
material before it has been transformed during the 3D printing
process. The transformation can be effectuated by utilizing an
energy beam. The pre-transformed material may be a material that
was, or was not, transformed prior to its use in a 3D printing
process. The pre-transformed material may be a starting material
for the 3D printing process.
[0176] In a 3D printing process, the deposited pre-transformed
material may be fused, (e.g., sintered or melted), bound or
otherwise connected to form at least a portion of the desired 3D
object. Fusing, binding or otherwise connecting the material is
collectively referred to herein as "transforming" the material.
Fusing the material may refer to melting, smelting, or sintering a
pre-transformed material.
[0177] Melting may comprise liquefying the material (i.e.,
transforming to a liquefied state). A liquefied state refers to a
state in which at least a portion of a transformed material is in a
liquid state. Melting may comprise liquidizing the material (i.e.,
transforming to a liquidus state). A liquidus state refers to a
state in which an entire transformed material is in a liquid state.
The embodiments (e.g., apparatuses, methods, software, and/or
systems) provided herein are not limited to the generation of a
single 3D object, but are may be utilized to generate one or more
3D objects simultaneously (e.g., in parallel) or separately (e.g.,
sequentially). The multiplicity of 3D object may be formed in one
or more material beds (e.g., powder bed), and/or adjacent to one or
more platforms. In some embodiments, a plurality of 3D objects is
formed in one material bed and/or adjacent to one platform.
[0178] 3D printing methodologies can comprise extrusion, wire,
granular, laminated, light polymerization, or powder bed and inkjet
head 3D printing. Extrusion 3D printing can comprise robo-casting,
fused deposition modeling (FDM) or fused filament fabrication
(FFF). Wire 3D printing can comprise electron beam freeform
fabrication (EBF3). Granular 3D printing can comprise direct metal
laser sintering (DMLS), electron beam melting (EBM), selective
laser melting (SLM), selective heat sintering (SHS), or selective
laser sintering (SLS). Powder bed and inkjet head 3D printing can
comprise plaster-based 3D printing (PP). Laminated 3D printing can
comprise laminated object manufacturing (LOM). Light polymerized 3D
printing can comprise stereo-lithography (SLA), digital light
processing (DLP), or laminated object manufacturing (LOM). 3D
printing methodologies can comprise Direct Material Deposition
(DMD). The Direct Material Deposition may comprise, Laser Metal
Deposition (LMD, also known as, Laser deposition welding). 3D
printing methodologies can comprise powder feed, or wire
deposition. 3D printing may comprise Laser Engineered Net Shaping
(LENS).
[0179] 3D printing methodologies may differ from methods
traditionally used in semiconductor device fabrication (e.g., vapor
deposition, etching, annealing, masking, or molecular beam
epitaxy). In some instances, the forming process (e.g., 3D
printing) may further comprise one or more (printing) methodologies
that are traditionally used in semiconductor device fabrication. 3D
printing methodologies can differ from vapor deposition methods
such as chemical vapor deposition, physical vapor deposition, or
electrochemical deposition. In some instances, the forming process
(e.g., 3D printing) may further include vapor deposition
methods.
[0180] The deposited pre-transformed material within the enclosure
can be a liquid material, semi-solid material (e.g., gel), or a
solid material (e.g., powder). The deposited pre-transformed
material within the enclosure can be in the form of a powder,
wires, sheets, or droplets. The material (e.g., pre-transformed,
transformed, and/or hardened) may comprise elemental metal, metal
alloy, ceramics, or an allotrope of elemental carbon. The allotrope
of elemental carbon may comprise amorphous carbon, graphite,
graphene, diamond, or fullerene. The fullerene may be selected from
the group consisting of a spherical, elliptical, linear, and
tubular fullerene. The fullerene may comprise a buckyball, or a
carbon nanotube. The ceramic material may comprise cement. The
ceramic material may comprise alumina, zirconia, or carbide (e.g.,
silicon carbide, or tungsten carbide). The ceramic material may
include high performance material (HPM). The ceramic material may
include a nitride (e.g., boron nitride or aluminum nitride). The
material may comprise sand, glass, or stone. In some embodiments,
the material may comprise an organic material, for example, a
polymer or a resin (e.g., 114 W resin). The organic material may
comprise a hydrocarbon. The polymer may comprise styrene or nylon
(e.g., nylon 11). The polymer may comprise a thermoplast. The
organic material may comprise carbon and hydrogen atoms. The
organic material may comprise carbon and oxygen atoms. The organic
material may comprise carbon and nitrogen atoms. The organic
material may comprise carbon and sulfur atoms. In some embodiments,
the material may exclude an organic material. The material may
comprise a solid or a liquid. In some embodiments, the material may
comprise a silicon-based material, for example, silicon based
polymer or a resin. The material may comprise an
organosilicon-based material. The material may comprise silicon and
hydrogen atoms. The material may comprise silicon and carbon atoms.
In some embodiments, the material may exclude a silicon-based
material. The powder material may be coated by a coating (e.g.,
organic coating such as the organic material (e.g., plastic
coating)). The material may be devoid of organic material. The
liquid material may be compartmentalized into reactors, vesicles,
or droplets. The compartmentalized material may be
compartmentalized in one or more layers. The material may be a
composite material comprising a secondary material. The secondary
material can be a reinforcing material (e.g., a material that forms
a fiber). The reinforcing material may comprise a carbon fiber,
Kevlar.RTM., Twaron.RTM., ultra-high-molecular-weight polyethylene,
or glass fiber. The material can comprise powder (e.g., granular
material) and/or wires. The bound material can comprise chemical
bonding. Transforming can comprise chemical bonding. Chemical
bonding can comprise covalent bonding. The pre-transformed material
may be pulverous. The printed 3D object can be made of a single
material (e.g., single material type) or multiple materials (e.g.,
multiple material types). Sometimes one portion of the 3D object
and/or of the material bed may comprise one material, and another
portion may comprise a second material different from the first
material. The material may be a single material type (e.g., a
single alloy or a single elemental metal). The material may
comprise one or more material types. For example, the material may
comprise two alloys, an alloy and an elemental metal, an alloy and
a ceramic, or an alloy and an elemental carbon. The material may
comprise an alloy and alloying elements (e.g., for inoculation).
The material may comprise blends of material types. The material
may comprise blends with elemental metal or with metal alloy. The
material may comprise blends excluding (e.g., without) elemental
metal or including (e.g., with) metal alloy. The material may
comprise a stainless steel. The material may comprise a titanium
alloy, aluminum alloy, and/or nickel alloy.
[0181] In some cases, a layer within the 3D object comprises a
single type of material. In some examples, a layer of the 3D object
may comprise a single elemental metal type, or a single alloy type.
In some examples, a layer within the 3D object may comprise several
types of material (e.g., an elemental metal and an alloy, an alloy
and a ceramic, an alloy and an elemental carbon). In certain
embodiments, each type of material comprises only a single member
of that type. For example: a single member of elemental metal
(e.g., iron), a single member of metal alloy (e.g., stainless
steel), a single member of ceramic material (e.g., silicon carbide
or tungsten carbide), or a single member of elemental carbon (e.g.,
graphite). In some cases, a layer of the 3D object comprises more
than one type of material. In some cases, a layer of the 3D object
comprises more than member of a type of material.
[0182] In some examples the material bed, platform, or both
material bed and platform comprise a material type which
constituents (e.g., atoms) readily lose their outer shell
electrons, resulting in a free flowing cloud of electrons within
their otherwise solid arrangement. In some examples, the material
(e.g., pre-transformed, transformed, and/or hardened), the base, or
both the material and the base comprise a material type
characterized in having high electrical conductivity, low
electrical resistivity, high thermal conductivity, or high density.
The high electrical conductivity can be at least about 1*10.sup.5
Siemens per meter (S/m), 5*10.sup.5 S/m, 1*10.sup.6 S/m, 5*10.sup.6
S/m, 1*10.sup.7 S/m, 5*10.sup.7 S/m, or 1*10.sup.8 S/m. The symbol
"*" designates the mathematical operation "times." The high
electrical conductivity can be between any of the aforementioned
electrical conductivity values (e.g., from about 1*10.sup.5 S/m to
about 1*10.sup.8 S/m). The thermal conductivity, electrical
resistivity, electrical conductivity, electrical resistivity,
and/or density can be measured at ambient temperature (e.g., at
R.T., or 20.degree. C.). The low electrical resistivity may be at
most about 1*10.sup.-5 ohm times meter (.omega.*m), 5*10.sup.-6
.OMEGA.*m, 1*10.sup.-6 .OMEGA.*m, 5*10.sup.-7 .OMEGA.*m,
1*10.sup.-7 .OMEGA.*m, 5*10.sup.-8 or 1*10.sup.-8 .OMEGA.*m. The
low electrical resistivity can be between any of the aforementioned
values (e.g., from about 1.times.10.sup.-5 .OMEGA.*m to about
1.times.10.sup.-8 .OMEGA.*m). The high thermal conductivity may be
at least about 10 Watts per meter times Kelvin (W/mK), 15 W/mK, 20
W/mK, 35 W/mK, 50 W/mK, 100 W/mK, 150 W/mK, 200 W/mK, 205 W/mK, 300
W/mK, 350 W/mK, 400 W/mK, 450 W/mK, 500 W/mK, 550 W/mK, 600 W/mK,
700 W/mK, 800 W/mK, 900 W/mK, or 1000 W/mK. The high thermal
conductivity can be between any of the aforementioned thermal
conductivity values (e.g., from about 20 W/mK to about 1000 W/mK).
The high density may be at least about 1.5 grams per cubic
centimeter (g/cm.sup.3), 1.7 g/cm.sup.3, 2 g/cm.sup.3, 2.5
g/cm.sup.3, 2.7 g/cm.sup.3, 3 g/cm.sup.3, 4 g/cm.sup.3, 5
g/cm.sup.3, 6 g/cm.sup.3, 7 g/cm.sup.3, 8 g/cm.sup.3, 9 g/cm.sup.3,
10 g/cm.sup.3, 11 g/cm.sup.3, 12 g/cm.sup.3, 13 g/cm.sup.3, 14
g/cm.sup.3, 15 g/cm.sup.3, 16 g/cm.sup.3, 17/cm.sup.3, 18
g/cm.sup.3, 19 g/cm.sup.3, 20 g/cm.sup.3, or 25 g/cm.sup.3. The
high density can be any value between the afore mentioned values
(e.g., from about 1 g/cm.sup.3 to about 25 g/cm.sup.3).
[0183] The elemental metal can be an alkali metal, an alkaline
earth metal, a transition metal, a rare earth element metal, or
another metal. The alkali metal can be Lithium, Sodium, Potassium,
Rubidium, Cesium, or Francium. The alkali earth metal can be
Beryllium, Magnesium, Calcium, Strontium, Barium, or Radium. The
transition metal can be Scandium, Titanium, Vanadium, Chromium,
Manganese, Iron, Cobalt, Nickel, Copper, Zinc, Yttrium, Zirconium,
Platinum, Gold, Rutherfordium, Dubnium, Seaborgium, Bohrium,
Hassium, Meitnerium, Ununbium, Niobium, Iridium, Molybdenum,
Technetium, Ruthenium, Rhodium, Palladium, Silver, Cadmium,
Hafnium, Tantalum, Tungsten, Rhenium or Osmium. The transition
metal can be mercury. The rare earth metal can be a lanthanide or
an actinide. The antinode metal can be Lanthanum, Cerium,
Praseodymium, Neodymium, Promethium, Samarium, Europium,
Gadolinium, Terbium, Dysprosium, Holmium, Erbium, Thulium,
Ytterbium, or Lutetium. The actinide metal can be Actinium,
Thorium, Protactinium, Uranium, Neptunium, Plutonium, Americium,
Curium, Berkelium, Californium, Einsteinium, Fermium, Mendelevium,
Nobelium, or Lawrencium. The other metal can be Aluminum, Gallium,
Indium, Tin, Thallium, Lead, or Bismuth. The material may comprise
a precious metal. The precious metal may comprise gold, silver,
palladium, ruthenium, rhodium, osmium, iridium, or platinum. The
material may comprise at least about 40%, 50%, 60%, 70%, 80%, 90%,
95%, 97%, 98%, 99%, 99.5% or more precious metal. The
pre-transformed (or transformed) material may comprise at most
about 40%, 50%, 60%, 70%, 80%, 90%, 95%, 97%, 98%, 99%, 99.5% or
less precious metal. The material may comprise precious metal with
any value in between the afore-mentioned values. The material may
comprise at least a minimal percentage of precious metal according
to the laws in the particular jurisdiction.
[0184] The metal alloy can comprise iron based alloy, nickel based
alloy, cobalt based alloy, chrome based alloy, cobalt chrome based
alloy, titanium based alloy, magnesium based alloy, or copper based
alloy. The alloy may comprise an oxidation or corrosion resistant
alloy. The alloy may comprise a super alloy (e.g., Inconel). The
super alloy may comprise Inconel 600, 617, 625, 690, 718 or X-750.
The alloy may comprise an alloy used for aerospace applications,
automotive application, surgical application, or implant
applications. The metal may include a metal used for aerospace
applications, automotive application, surgical application, or
implant applications. The super alloy may comprise IN 738 LC, IN
939, Rene 80, IN 6203 (e.g., IN 6203 DS), PWA 1483 (e.g., PWA 1483
SX), or Alloy 247.
[0185] The material (e.g., alloy or elemental) may comprise a
material used for applications in industries comprising aerospace
(e.g., aerospace super alloys), jet engine, missile, automotive,
marine, locomotive, satellite, defense, oil & gas, energy
generation, semiconductor, fashion, construction, agriculture,
printing, or medical. The material may comprise an alloy used for
products comprising, devices, medical devices (human &
veterinary), machinery, cell phones, semiconductor equipment,
generators, engines, pistons, electronics (e.g., circuits),
electronic equipment, agriculture equipment, motor, gear,
transmission, communication equipment, computing equipment (e.g.,
laptop, cell phone, i-pad), air conditioning, generators,
furniture, musical equipment, art, jewelry, cooking equipment, or
sport gear. The material may comprise an alloy used for products
for human or veterinary applications comprising implants, or
prosthetics. The metal alloy may comprise an alloy used for
applications in the fields comprising human or veterinary surgery,
implants (e.g., dental), or prosthetics.
[0186] The alloy may include a high-performance alloy. The alloy
may include an alloy exhibiting at least one of excellent
mechanical strength, resistance to thermal creep deformation, good
surface stability, resistance to corrosion, and resistance to
oxidation. The alloy may include a face-centered cubic austenitic
crystal structure. The alloy may comprise Hastelloy, Inconel,
Waspaloy, Rene alloy (e.g., Rene-80, Rene-77, Rene-220, or
Rene-41), Haynes alloy, Incoloy, MP98T, TMS alloy, MTEK (e.g., MTEK
grade MAR-M-247, MAR-M-509, MAR-M-R41, or MAR-M-X-45), or CMSX
(e.g., CMSX-3, or CMSX-4). The alloy can be a single crystal
alloy.
[0187] In some instances, the iron-based alloy can comprise
Elinvar, Fernico, Ferroalloys, Invar, Iron hydride, Kovar,
Spiegeleisen, Staballoy (stainless steel), or Steel. In some
instances, the metal alloy is steel. The Ferroalloy may comprise
Ferroboron, Ferrocerium, Ferrochrome, Ferromagnesium,
Ferromanganese, Ferromolybdenum, Ferronickel, Ferrophosphorus,
Ferrosilicon, Ferrotitanium, Ferrouranium, or Ferrovanadium. The
iron-based alloy may include cast iron or pig iron. The steel may
include Bulat steel, Chromoly, Crucible steel, Damascus steel,
Hadfield steel, High speed steel, HSLA steel, Maraging steel,
Reynolds 531, Silicon steel, Spring steel, Stainless steel, Tool
steel, Weathering steel, or Wootz steel. The high-speed steel may
include Mushet steel. The stainless steel may include AL-6XN, Alloy
20, celestrium, marine grade stainless, Martensitic stainless
steel, surgical stainless steel, or Zeron 100. The tool steel may
include Silver steel. The steel may comprise stainless steel,
Nickel steel, Nickel-chromium steel, Molybdenum steel, Chromium
steel, Chromium-vanadium steel, Tungsten steel,
Nickel-chromium-molybdenum steel or Silicon-manganese steel. The
steel may be comprised of any Society of Automotive Engineers (SAE)
grade such as 440F, 410, 312, 430, 440A, 440B, 440C, 304, 305,
304L, 304L, 301, 304LN, 301LN, 2304, 316, 316L, 316LN, 316, 316LN,
316L, 316L, 316, 317L, 2205, 409, 904L, 321, 254SMO, 316Ti, 321H or
304H. The steel may comprise stainless steel of at least one
crystalline structure selected from the group consisting of
austenitic, superaustenitic, ferritic, martensitic, duplex and
precipitation-hardening martensitic. Duplex stainless steel may be
lean duplex, standard duplex, super duplex or hyper duplex. The
stainless steel may comprise surgical grade stainless steel (e.g.,
austenitic 316, martensitic 420 or martensitic 440). The austenitic
316 stainless steel may include 316L or 316LVM. The steel may
include 17-4 Precipitation Hardening steel (also known as type 630
is a chromium-copper precipitation hardening stainless steel;
17-4PH steel). The stainless steel may comprise 360L stainless
steel.
[0188] The titanium-based alloys may include alpha alloys, near
alpha alloys, alpha and beta alloys, or beta alloys. The titanium
alloy may comprise grade 1, 2, 2H, 3, 4, 5, 6, 7, 7H, 8, 9, 10, 11,
12, 13, 14, 15, 16, 16H, 17, 18, 19, 20, 21, 2, 23, 24, 25, 26,
26H, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 or higher. In
some instances the titanium base alloy includes TiAl.sub.6V.sub.4
or TiAl.sub.6Nb.sub.7.
[0189] The Nickel based alloy may include Alnico, Alumel, Chromel,
Cupronickel, Ferronickel, German silver, Hastelloy, Inconel, Monel
metal, Nichrome, Nickel-carbon, Nicrosil, Nisil, Nitinol, or
Magnetically "soft" alloys. The magnetically "soft" alloys may
comprise Mu-metal, Permalloy, Supermalloy, or Brass. The Brass may
include nickel hydride, stainless or coin silver. The cobalt alloy
may include Megallium, Stellite (e. g. Talonite), Ultimet, or
Vitallium. The chromium alloy may include chromium hydroxide, or
Nichrome.
[0190] The aluminum-based alloy may include AA-8000, Al--Li
(aluminum-lithium), Alnico, Duralumin, Hiduminium, Kryron
Magnalium, Nambe, Scandium-aluminum, or, Y alloy. The magnesium
alloy may be Elektron, Magnox or T-Mg--Al--Zn (Bergman phase)
alloy. At times, the material excludes at least one aluminum-based
alloy (e.g., AlSi.sub.10Mg).
[0191] The copper based alloy may comprise Arsenical copper,
Beryllium copper, Billon, Brass, Bronze, Constantan, Copper
hydride, Copper-tungsten, Corinthian bronze, Cunife, Cupronickel,
Cymbal alloys, Devarda's alloy, Electrum, Hepatizon, Heusler alloy,
Manganin, Molybdochalkos, Nickel silver, Nordic gold, Shakudo or
Tumbaga. The Brass may include Calamine brass, Chinese silver,
Dutch metal, Gilding metal, Muntz metal, Pinchbeck, Prince's metal,
or Tombac. The Bronze may include Aluminum bronze, Arsenical
bronze, Bell metal, Florentine bronze, Guanin, Gunmetal, Glucydur,
Phosphor bronze, Ormolu or Speculum metal. The elemental carbon may
comprise graphite, Graphene, diamond, amorphous carbon, carbon
fiber, carbon nanotube, or fullerene.
[0192] The powder material (also referred to herein as a "pulverous
material") may comprise a solid comprising fine particles. The
powder may be a granular material. The powder can be composed of
individual particles. At least some of the particles can be
spherical, oval, prismatic, cubic, or irregularly shaped. At least
some of the particles can have a FLS (e.g., diameter, spherical
equivalent diameter, length, width, or diameter of a bounding
sphere). The FLS of at least some of the particles can be from
about 1 nanometers (nm) to about 1000 micrometers (microns), 500
microns, 400 microns, 300 microns, 200 microns, 100 microns, 50
microns, 40 microns, 30 microns, 20 microns, 10 microns, 1 micron,
500 nm, 400 nm, 300 nm, 200 nm, 100 nm, 50 nm, 40 nm, 30 nm, 20 nm,
10 nm, or 5 nm. At least some of the particles can have a FLS of at
least about 1000 micrometers (microns), 500 microns, 400 microns,
300 microns, 200 microns, 100 microns, 50 microns, 40 microns, 30
microns, 20 microns, 10 microns, 1 micron, 500 nm, 400 nm, 300 nm,
200 nm, 100 nm, 50 nm, 40 nm, 30 nm, 20 nm, 10 nm, 5 nanometers
(nm) or more. At least some of the particles can have a FLS of at
most about 1000 micrometers (microns), 500 microns, 400 microns,
300 microns, 200 microns, 100 microns, 50 microns, 40 microns, 30
microns, 20 microns, 10 microns, 1 micron, 500 nm, 400 nm, 300 nm,
200 nm, 100 nm, 50 nm, 40 nm, 30 nm, 20 nm, 10 nm, 5 nm or less. In
some cases, at least some of the powder particles may have a FLS in
between any of the afore-mentioned FLSs.
[0193] The powder can be composed of a homogenously shaped particle
mixture such that all of the particles have substantially the same
shape and FLS magnitude within at most about 1%, 5%, 8%, 10%, 15%,
20%, 25%, 30%, 35%, 40%, 50%, 60%, 70%, or less distribution of
FLS. In some cases, the powder can be a heterogeneous mixture such
that the particles have variable shape and/or FLS magnitude. In
some examples, at least about 30%, 40%, 50%, 60%, or 70% (by
weight) of the particles within the powder material have a largest
FLS that is smaller than the median largest FLS of the powder
material. In some examples, at least about 30%, 40%, 50%, 60%, or
70% (by weight) of the particles within the powder material have a
largest FLS that is smaller than the mean largest FLS of the powder
material.
[0194] In some examples, the size of the largest FLS of the
transformed material (e.g., height) is greater than the average
largest FLS of the powder material by at least about 1.1 times, 1.2
times, 1.4 times, 1.6 times, 1.8 times, 2 times, 4 times, 6 times,
8 times, or 10 times. In some examples, the size of the largest FLS
of the transformed material is greater than the median largest FLS
of the powder material by at most about 1.1 times, 1.2 times, 1.4
times, 1.6 times, 1.8 times, 2 times, 4 times, 6 times, 8 times, or
10 times. The powder material can have a median largest FLS that is
at least about 1 .mu.m, 5 .mu.m, 10 .mu.m, 20 .mu.m, 30 .mu.m, 40
.mu.m, 50 .mu.m, 100 .mu.m, or 200 .mu.m. The powder material can
have a median largest FLS that is at most about 1 .mu.m, 5 .mu.m,
10 .mu.m, 20 .mu.m, 30 .mu.m, 40 .mu.m, 50 .mu.m, 100 .mu.m, or 200
.mu.m. In some cases, the powder particles may have a FLS in
between any of the FLS listed above (e.g., from about 1 .mu.m to
about 200 .mu.m, from about 1 .mu.m to about 50 .mu.m, or from
about 5 .mu.m to about 40 .mu.m).
[0195] In another aspect provided herein is a method for generating
a 3D object comprising: (a) depositing a layer of pre-transformed
material in an enclosure (e.g., to form a material bed such as a
powder bed); (b) providing energy (e.g., using an energy beam) to
at least a portion of the layer of pre-transformed material
according to a path for transforming the at least a portion of the
layer of pre-transformed material to form a transformed material as
at least a portion of the 3D object; and (c) optionally repeating
operations (a) to (b) to generate the 3D object. The method may
further comprise after operation (b) and before operation (c):
allowing the transformed material to harden into a hardened
material that forms at least a portion of the 3D object. The
enclosure may comprise at least one chamber. The enclosure (e.g.,
the chamber) may comprise a building platform (e.g., a substrate
and/or base). The 3D object may be printed adjacent to (e.g.,
above) the building platform.
[0196] The controller may monitor and/or direct (e.g., physical)
alteration of the operating conditions of the apparatuses,
software, and/or methods described herein. Control may comprise
regulate, manipulate, restrict, direct, monitor, adjust, modulate,
vary, alter, restrain, check, guide, or manage. The control may
comprise controlling a control variable (e.g. temperature, power,
power per unit area, and/or profile). The control can comprise real
time or off-line control. A calculation can be done in real time,
and/or off line. The power may be of the energy source. The power
per unit are may be of the energy beam. The profile may be an
energy beam profile. The temperature may be of the irradiated area
and/or an area at the immediate vicinity of the irradiated area
(e.g., up to five or six diameters of a FLS of the irradiated
area). The controller may be a manual or a non-manual controller.
The controller may be an automatic controller. The controller may
operate upon request. The controller may be a programmable
controller. The controller may be programed. The controller may
comprise a processing unit (e.g., CPU or GPU). The controller may
receive an input (e.g., from a sensor). The controller may deliver
an output. The controller may comprise multiple (e.g., sub-)
controllers. The controller may receive multiple inputs. The
controller may generate multiple outputs. The controller may be a
single input single output controller (SISO) or a multiple input
multiple output controller (MIMO). The controller may interpret the
input signal received. The controller may acquire data from the one
or more sensors. Acquire may comprise receive or extract. The data
may comprise measurement, estimation, determination, generation, or
any combination thereof. The controller may comprise feedback
control. The controller may comprise feed-forward control. The
control may comprise on-off control, proportional control,
proportional-integral (PI) control, or
proportional-integral-derivative (PID) control. The control may
comprise open loop control, or closed loop control. The controller
may comprise closed loop control. The controller may comprise open
loop control. The controller may comprise a user interface. The
user interface may comprise a keyboard, keypad, mouse, touch
screen, microphone, speech recognition package, camera, imaging
system, or any combination thereof. The outputs may include a
display (e.g., screen), speaker, or printer.
[0197] The methods, systems and/or the apparatus described herein
may further comprise a control system. The control system can be in
communication with one or more energy sources and/or energy (e.g.,
energy beams). The energy sources may be of the same type or of
different types, e.g., as described herein. For example, the energy
sources can be both lasers, or a laser and an electron beam. For
example, the control system may be in communication with the first
energy and/or with the second energy. The control system may
regulate the one or more energies (e.g., energy beams). The
controller may regulate at least one characteristic of the energy
beam. The control system may regulate the energy supplied by the
one or more energy sources. For example, the control system may
regulate the energy supplied by a first energy beam and by a second
energy beam, to the pre-transformed material within the material
bed. The control system may regulate the position of the one or
more energy beams (e.g., along their respective trajectories). For
example, the control system may regulate the position of the first
energy beam and/or the position of the second energy beam.
[0198] The 3D printing system may comprise a processor. The
processor may be a processing unit. The controller may comprise a
processing unit. The processing unit may be central. The processing
unit may comprise a central processing unit (herein "CPU"). The
controllers or control mechanisms (e.g., comprising a computer
system) may be programmed to implement methods of the disclosure.
The processor (e.g., 3D printer processor) may be programmed to
implement methods of the disclosure. The controller may control at
least one component of the forming systems and/or apparatuses
disclosed herein. FIG. 11 is a schematic example of a computer
system 1100 that is programmed or otherwise configured to
facilitate the formation of a 3D object according to the methods
provided herein. The computer system 1100 can control (e.g.,
direct, monitor, and/or regulate) various features of printing
methods, apparatuses and systems of the present disclosure, such
as, for example, control force, translation, heating, cooling
and/or maintaining the temperature of a material bed, process
parameters (e.g., chamber pressure), scanning rate (e.g., of the
energy beam and/or the platform), scanning route of the energy
source, position and/or temperature of the cooling member(s),
application of the amount of energy emitted to a selected location,
or any combination thereof. The computer system 1100 can be part
of, or be in communication with, a 3D printing system or apparatus.
The computer may be coupled to one or more mechanisms disclosed
herein, and/or any parts thereof. For example, the computer may be
coupled to one or more sensors, valves, switches, motors, pumps,
scanners, optical components, or any combination thereof.
[0199] The computer system 1100 can include a processing unit 1106
(also "processor," "computer" and "computer processor" used
herein). The computer system may include memory or memory location
1102 (e.g., random-access memory, read-only memory, flash memory),
electronic storage unit 1104 (e.g., hard disk), communication
interface 1103 (e.g., network adapter) for communicating with one
or more other systems, and peripheral devices 1105, such as cache,
other memory, data storage and/or electronic display adapters. The
memory 1102, storage unit 1104, interface 1103, and peripheral
devices 1105 are in communication with the processing unit 1106
through a communication bus (solid lines), such as a motherboard.
The storage unit can be a data storage unit (or data repository)
for storing data. The computer system can be operatively coupled to
a computer network ("network") 1101 with the aid of the
communication interface. The network can be the Internet, an
internet and/or extranet, or an intranet and/or extranet that is in
communication with the Internet. In some cases, the network is a
telecommunication and/or data network. The network can include one
or more computer servers, which can enable distributed computing,
such as cloud computing. The network, in some cases with the aid of
the computer system, can implement a peer-to-peer network, which
may enable devices coupled to the computer system to behave as a
client or a server.
[0200] The processing unit can execute a sequence of
machine-readable instructions, which can be embodied in a program
or software. The instructions may be stored in a memory location,
such as the memory 1102. The instructions can be directed to the
processing unit, which can subsequently program or otherwise
configure the processing unit to implement methods of the present
disclosure. Examples of operations performed by the processing unit
can include fetch, decode, execute, and write back. The processing
unit may interpret and/or execute instructions. The processor may
include a microprocessor, a data processor, a central processing
unit (CPU), a graphical processing unit (GPU), a system-on-chip
(SOC), a co-processor, a network processor, an application specific
integrated circuit (ASIC), an application specific instruction-set
processor (ASIPs), a controller, a programmable logic device (PLD),
a chipset, a field programmable gate array (FPGA), or any
combination thereof. The processing unit can be part of a circuit,
such as an integrated circuit. One or more other components of the
system 1100 can be included in the circuit.
[0201] The storage unit 1104 can store files, such as drivers,
libraries and saved programs. The storage unit can store user data
(e.g., user preferences and user programs). In some cases, the
computer system can include one or more additional data storage
units that are external to the computer system, such as located on
a remote server that is in communication with the computer system
through an intranet or the Internet.
[0202] The computer system can communicate with one or more remote
computer systems through a network. For instance, the computer
system can communicate with a remote computer system of a user
(e.g., operator). Examples of remote computer systems include
personal computers (e.g., portable PC), slate or tablet PC's (e.g.,
Apple.RTM. iPad, Samsung.RTM. Galaxy Tab), telephones, Smart phones
(e.g., Apple.RTM. iPhone, Android-enabled device, Blackberry.RTM.),
or personal digital assistants. A user (e.g., client) can access
the computer system via the network.
[0203] Methods as described herein can be implemented by way of
machine (e.g., computer processor) executable code stored on an
electronic storage location of the computer system, such as, for
example, on the memory 1102 or electronic storage unit 1104. The
machine executable or machine-readable code can be provided in the
form of software. During use, the processor 1106 can execute the
code. In some cases, the code can be retrieved from the storage
unit and stored on the memory for ready access by the processor. In
some situations, the electronic storage unit can be precluded, and
machine-executable instructions are stored on memory.
[0204] The code can be pre-compiled and configured for use with a
machine have a processer adapted to execute the code, or can be
compiled during runtime. The code can be supplied in a programming
language that can be selected to enable the code to execute in a
pre-compiled or as-compiled fashion.
[0205] The processing unit may include one or more cores. The
computer system may comprise a single core processor, multi core
processor, or a plurality of processors for parallel processing.
The processing unit may comprise one or more central processing
unit (CPU) and/or a graphic processing unit (GPU). The multiple
cores may be disposed in a physical unit (e.g., Central Processing
Unit, or Graphic Processing Unit). The processing unit may include
one or more processing units. The physical unit may be a single
physical unit. The physical unit may be a die. The physical unit
may comprise cache coherency circuitry. The multiple cores may be
disposed in close proximity. The physical unit may comprise an
integrated circuit chip. The integrated circuit chip may comprise
one or more transistors. The integrated circuit chip may comprise
at least about 0.2 billion transistors (BT), 0.5 BT, 1 BT, 2 BT, 3
BT, 5 BT, 6 BT, 7 BT, 8 BT, 9 BT, 10 BT, 15 BT, 20 BT, 25 BT, 30
BT, 40 BT, or 50 BT. The integrated circuit chip may comprise at
most about 7 BT, 8 BT, 9 BT, 10 BT, 15 BT, 20 BT, 25 BT, 30 BT, 40
BT, 50 BT, 70 BT, or 100 BT. The integrated circuit chip may
comprise any number of transistors between the afore-mentioned
numbers (e.g., from about 0.2 BT to about 100 BT, from about 1 BT
to about 8 BT, from about 8 BT to about 40 BT, or from about 40 BT
to about 100 BT). The integrated circuit chip may have an area of
at least about 50 mm.sup.2, 60 mm.sup.2, 70 mm.sup.2, 80 mm.sup.2,
90 mm.sup.2, 100 mm.sup.2, 200 mm.sup.2, 300 mm.sup.2, 400
mm.sup.2, 500 mm.sup.2, 600 mm.sup.2, 700 mm.sup.2, or 800
mm.sup.2. The integrated circuit chip may have an area of at most
about 50 mm.sup.2, 60 mm.sup.2, 70 mm.sup.2, 80 mm.sup.2, 90
mm.sup.2, 100 mm.sup.2, 200 mm.sup.2, 300 mm.sup.2, 400 mm.sup.2,
500 mm.sup.2, 600 mm.sup.2, 700 mm.sup.2, or 800 mm.sup.2. The
integrated circuit chip may have an area of any value between the
afore-mentioned values (e.g., from about 50 mm.sup.2 to about 800
mm.sup.2, from about 50 mm.sup.2 to about 500 mm.sup.2, or from
about 500 mm.sup.2 to about 800 mm.sup.2). The close proximity may
allow substantial preservation of communication signals that travel
between the cores. The close proximity may diminish communication
signal degradation. A core as understood herein is a computing
component having independent central processing capabilities. The
computing system may comprise a multiplicity of cores, which are
disposed on a single computing component. The multiplicity of cores
may include two or more independent central processing units. The
independent central processing units may constitute a unit that
read and execute program instructions. The independent central
processing units may constitute parallel processing units. The
parallel processing units may be cores and/or digital signal
processing slices (DSP slices). The multiplicity of cores can be
parallel cores. The multiplicity of DSP slices can be parallel DSP
slices. The multiplicity of cores and/or DSP slices can function in
parallel. The multiplicity of cores may include at least about 2,
10, 40, 100, 400, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000,
9000, 10000, 11000, 12000, 13000, 14000 or 15000 cores. The
multiplicity of cores may include at most about 1000, 2000, 3000,
4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000,
14000, 15000, 20000, 30000, or 40000 cores. The multiplicity of
cores may include cores of any number between the afore-mentioned
numbers (e.g., from about 2 to about 40000, from about 2 to about
400, from about 400 to about 4000, from about 2000 to about 4000,
from about 4000 to about 10000, from about 4000 to about 15000, or
from about 15000 to about 40000 cores). In some processors (e.g.,
FPGA), the cores may be equivalent to multiple digital signal
processor (DSP) slices (e.g., slices). The plurality of DSP slices
may be equal to any of plurality core values mentioned herein. The
processor may comprise low latency in data transfer (e.g., from one
core to another). Latency may refer to the time delay between the
cause and the effect of a physical change in the processor (e.g., a
signal). Latency may refer to the time elapsed from the source
(e.g., first core) sending a packet to the destination (e.g.,
second core) receiving it (also referred as two-point latency).
One-point latency may refer to the time elapsed from the source
(e.g., first core) sending a packet (e.g., signal) to the
destination (e.g., second core) receiving it, and the designation
sending a packet back to the source (e.g., the packet making a
round trip). The latency may be sufficiently low to allow a high
number of floating point operations per second (FLOPS). The number
of FLOPS may be at least about 0.1 Tera FLOPS (T-FLOPS), 0.2
T-FLOPS, 0.25 T-FLOPS, 0.5 T-FLOPS, 0.75 T-FLOPS, 1 T-FLOPS, 2
T-FLOPS, 3 T-FLOPS, 5 T-FLOPS, 6 T-FLOPS, 7 T-FLOPS, 8 T-FLOPS, 9
T-FLOPS, or 10 T-FLOPS. The number of flops may be at most about
0.2 T-FLOPS, 0.25 T-FLOPS, 0.5 T-FLOPS, 0.75 T-FLOPS, 1 T-FLOPS, 2
T-FLOPS, 3 T-FLOPS, 5 T-FLOPS, 6 T-FLOPS, 7 T-FLOPS, 8 T-FLOPS, 9
T-FLOPS, 10 T-FLOPS, 20 T-FLOPS, or 30 T-FLOPS. The number of FLOPS
may be any value between the afore-mentioned values (e.g., from
about 0.1 T-FLOP to about 30 T-FLOP, from about 0.1 T-FLOPS to
about 1 T-FLOPS, from about 1 T-FLOPS to about 4 T-FLOPS, from
about 4 T-FLOPS to about 10 T-FLOPS, from about 1 T-FLOPS to about
10 T-FLOPS, or from about 10 T-FLOPS to about 30 T-FLOPS). In some
processors (e.g., FPGA), the operations per second may be measured
as (e.g., Giga) multiply-accumulate operations per second (e.g.,
MACs or GMACs). The MACs value can be equal to any of the T-FLOPS
values mentioned herein measured as Tera-MACs (T-MACs) instead of
T-FLOPS respectively. The FLOPS can be measured according to a
benchmark. The benchmark may be a HPC Challenge Benchmark. The
benchmark may comprise mathematical operations (e.g., equation
calculation such as linear equations), graphical operations (e.g.,
rendering), or encryption/decryption benchmark. The benchmark may
comprise a High Performance UNPACK, matrix multiplication (e.g.,
DGEMM), sustained memory bandwidth to/from memory (e.g., STREAM),
array transposing rate measurement (e.g., PTRANS), Random-access,
rate of Fast Fourier Transform (e.g., on a large one-dimensional
vector using the generalized Cooley-Tukey algorithm), or
Communication Bandwidth and Latency (e.g., MPI-centric performance
measurements based on the effective bandwidth/latency benchmark).
UNPACK may refer to a software library for performing numerical
linear algebra on a digital computer. DGEMM may refer to double
precision general matrix multiplication. STREAM benchmark may refer
to a synthetic benchmark designed to measure sustainable memory
bandwidth (in MB/s) and a corresponding computation rate for four
simple vector kernels (Copy, Scale, Add and Triad). PTRANS
benchmark may refer to a rate measurement at which the system can
transpose a large array (global). MPI refers to Message Passing
Interface.
[0206] The computer system may include hyper-threading technology.
The computer system may include a chip processor with integrated
transform, lighting, triangle setup, triangle clipping, rendering
engine, or any combination thereof. The rendering engine may be
capable of processing at least about 10 million polygons per
second. The rendering engines may be capable of processing at least
about 10 million calculations per second. As an example, the GPU
may include a GPU by Nvidia, ATI Technologies, S3 Graphics,
Advanced Micro Devices (AMD), or Matrox. The processing unit may be
able to process algorithms comprising a matrix or a vector. The
core may comprise a complex instruction set computing core (CISC),
or reduced instruction set computing (RISC).
[0207] The computer system may include an electronic chip that is
reprogrammable (e.g., field programmable gate array (FPGA)). For
example, the FPGA may comprise Tabula, Altera, or Xilinx FPGA. The
electronic chips may comprise one or more programmable logic blocks
(e.g., an array). The logic blocks may compute combinational
functions, logic gates, or any combination thereof. The computer
system may include custom hardware. The custom hardware may
comprise an algorithm.
[0208] The computer system may include configurable computing,
partially reconfigurable computing, reconfigurable computing, or
any combination thereof. The computer system may include a FPGA.
The computer system may include an integrated circuit that performs
the algorithm. For example, the reconfigurable computing system may
comprise FPGA, CPU, GPU, or multi-core microprocessors. The
reconfigurable computing system may comprise a High-Performance
Reconfigurable Computing architecture (HPRC). The partially
reconfigurable computing may include module-based partial
reconfiguration, or difference-based partial reconfiguration. The
FPGA may comprise configurable FPGA logic, and/or fixed-function
hardware comprising multipliers, memories, microprocessor cores,
first in-first out (FIFO) and/or error correcting code (ECC) logic,
digital signal processing (DSP) blocks, peripheral Component
interconnect express (PCI Express) controllers, ethernet media
access control (MAC) blocks, or high-speed serial transceivers. DSP
blocks can be DSP slices.
[0209] The computing system may include an integrated circuit that
performs the algorithm (e.g., control algorithm). The physical unit
(e.g., the cache coherency circuitry within) may have a clock time
of at least about 0.1 Gigabits per second (Gbit/s), 0.5 Gbit/s, 1
Gbit/s, 2 Gbit/s, 5 Gbit/s, 6 Gbit/s, 7 Gbit/s, 8 Gbit/s, 9 Gbit/s,
10 Gbit/s, or 50 Gbit/s. The physical unit may have a clock time of
any value between the afore-mentioned values (e.g., from about 0.1
Gbit/s to about 50 Gbit/s, or from about 5 Gbit/s to about 10
Gbit/s). The physical unit may produce the algorithm output in at
most about 0.1 microsecond (.mu.s), 1 .mu.s, 10 .mu.s, 100 .mu.s,
or 1 millisecond (ms). The physical unit may produce the algorithm
output in any time between the above mentioned times (e.g., from
about 0.1 .mu.s, to about 1 ms, from about 0.1 .mu.s, to about 100
.mu.s, or from about 0.1 .mu.s to about 10 .mu.s).
[0210] In some instances, the controller may use calculations, real
time measurements, or any combination thereof to regulate the
energy beam(s). The sensor (e.g., temperature and/or positional
sensor) may provide a signal (e.g., input for the controller and/or
processor) at a rate of at least about 0.1 KHz, 1 KHz, 10 KHz, 100
KHz, 1000 KHz, or 10000 KHz). The sensor may provide a signal at a
rate between any of the above-mentioned rates (e.g., from about 0.1
KHz to about 10000 KHz, from about 0.1 KHz to about 1000 KHz, or
from about 1000 KHz to about 10000 KHz). The memory bandwidth of
the processing unit may be at least about 1 gigabytes per second
(Gbytes/s), 10 Gbytes/s, 100 Gbytes/s, 200 Gbytes/s, 300 Gbytes/s,
400 Gbytes/s, 500 Gbytes/s, 600 Gbytes/s, 700 Gbytes/s, 800
Gbytes/s, 900 Gbytes/s, or 1000 Gbytes/s. The memory bandwidth of
the processing unit may be at most about 1 gigabyte per second
(Gbytes/s), 10 Gbytes/s, 100 Gbytes/s, 200 Gbytes/s, 300 Gbytes/s,
400 Gbytes/s, 500 Gbytes/s, 600 Gbytes/s, 700 Gbytes/s, 800
Gbytes/s, 900 Gbytes/s, or 1000 Gbytes/s. The memory bandwidth of
the processing unit may have any value between the aforementioned
values (e.g., from about 1 Gbytes/s to about 1000 Gbytes/s, from
about 100 Gbytes/s to about 500 Gbytes/s, from about 500 Gbytes/s
to about 1000 Gbytes/s, or from about 200 Gbytes/s to about 400
Gbytes/s). The sensor measurements may be real-time measurements.
The real time measurements may be conducted during the 3D printing
process. The real-time measurements may be in situ measurements in
the 3D printing system and/or apparatus, the real time measurements
may be during the formation of the 3D object. In some instances,
the processing unit may use the signal obtained from the at least
one sensor to provide a processing unit output, which output is
provided by the processing system at a speed of at most about 100
min, 50 min, 25 min, 15 min, 10 min, 5 min, 1 min, 0.5 min (i.e.,
30 sec), 15 sec, 10 sec, 5 sec, 1 sec, 0.5 sec, 0.25 sec, 0.2 sec,
0.1 sec, 80 milliseconds (msec), 50 msec, 10 msec, 5 msec, 1 msec,
80 microseconds (.mu.sec), 50 .mu.sec, 20 .mu.sec, 10 .mu.sec, 5
.mu.sec, or 1 .mu.sec. In some instances, the processing unit may
use the signal obtained from the at least one sensor to provide a
processing unit output, which output is provided at a speed of any
value between the afore-mentioned values (e.g., from about 100 min
to about 1 .mu.sec, from about 100 min to about 10 min, from about
10 min to about 1 min, from about 5 min to about 0.5 min, from
about 30 sec to about 0.1 sec, from about 0.1 sec to about 1 msec,
from about 80 msec to about 10 .mu.sec, from about 50 .mu.sec to
about 1 .mu.sec, from about 20 .mu.sec to about 1 .mu.sec, or from
about 10 .mu.sec to about 1 .mu.sec).
[0211] The processing unit output may comprise an evaluation of the
temperature at a location, position at a location (e.g., vertical
and/or horizontal), or a map of locations. The location may be on
the target surface. The map may comprise a topological or
temperature map. The temperature sensor may comprise a temperature
imaging device (e.g., IR imaging device).
[0212] The processing unit may use the signal obtained from the at
least one sensor in an algorithm that is used in controlling the
energy beam. The algorithm may comprise the path of the energy
beam. In some instances, the algorithm may be used to alter the
path of the energy beam on the target surface. The path may deviate
from a cross section of a model corresponding to the desired 3D
object. The processing unit may use the output in an algorithm that
is used in determining the manner in which a model of the desired
3D object may be sliced. The processing unit may use the signal
obtained from the at least one sensor in an algorithm that is used
to configure one or more parameters and/or apparatuses relating to
the 3D printing process. The parameters may comprise a
characteristic of the energy beam. The parameters may comprise
movement of the platform and/or material bed. The parameters may
comprise relative movement of the energy beam and the material bed.
In some instances, the energy beam, the platform (e.g., material
bed disposed on the platform), or both may translate. Alternatively
or additionally, the controller may use historical data for the
control. Alternatively or additionally, the processing unit may use
historical data in its one or more algorithms. The parameters may
comprise the height of the layer of pre-transformed (e.g., powder)
material disposed in the enclosure and/or the gap by which the
cooling element (e.g., heat sink) is separated from the target
surface. The target surface may be the exposed layer of the
material bed.
[0213] Aspects of the systems, apparatuses, and/or methods provided
herein, such as the computer system, can be embodied in programming
(e.g., using a software). Various aspects of the technology may be
thought of as "product," "object," or "articles of manufacture"
typically in the form of machine (or processor) executable code
and/or associated data that is carried on or embodied in a type of
machine-readable medium. Machine-executable code can be stored on
an electronic storage unit, such memory (e.g., read-only memory,
random-access memory, flash memory) or a hard disk. The storage may
comprise non-volatile storage media. "Storage" type media can
include any or all of the tangible memory of the computers,
processors or the like, or associated modules thereof, such as
various semiconductor memories, tape drives, disk drives, external
drives, and the like, which may provide non-transitory storage at
any time for the software programming.
[0214] The memory may comprise a random access memory (RAM),
dynamic random access memory (DRAM), static random access memory
(SRAM), synchronous dynamic random access memory (SDRAM),
ferroelectric random access memory (FRAM), read only memory (ROM),
programmable read only memory (PROM), erasable programmable read
only memory (EPROM), electrically erasable programmable read only
memory (EEPROM), a flash memory, or any combination thereof. The
flash memory may comprise a negative-AND (NAND) or NOR logic gates.
A NAND gate (negative-AND) may be a logic gate which produces an
output which is false only if all its inputs are true. The output
of the NAND gate may be complement to that of the AND gate. The
storage may include a hard disk (e.g., a magnetic disk, an optical
disk, a magneto-optic disk, a solid state disk, etc.), a compact
disc (CD), a digital versatile disc (DVD), a floppy disk, a
cartridge, a magnetic tape, and/or another type of
computer-readable medium, along with a corresponding drive.
[0215] All or portions of the software may at times be communicated
through the Internet or various other telecommunication networks.
Such communications, for example, may enable loading of the
software from one computer or processor into another, for example,
from a management server or host computer into the computer
platform of an application server. Thus, another type of media that
may bear the software elements includes optical, electrical and
electromagnetic waves, such as used across physical interfaces
between local devices, through wired and optical landline networks
and over various air-links. The physical elements that carry such
waves, such as wired or wireless links, optical links, or the like,
also may be considered as media bearing the software. As used
herein, unless restricted to non-transitory, tangible "storage"
media, terms such as computer or machine "readable medium" refer to
any medium that participates in providing instructions to a
processor for execution.
[0216] Hence, a machine-readable medium, such as
computer-executable code, may take many forms, including but not
limited to, a tangible storage medium, a carrier wave medium, or
physical transmission medium. Non-volatile storage media include,
for example, optical or magnetic disks, such as any of the storage
devices in any computer(s) or the like, such as may be used to
implement the databases. Volatile storage media can include dynamic
memory, such as main memory of such a computer platform. Tangible
transmission media can include coaxial cables, wire (e.g., copper
wire), and/or fiber optics, including the wires that comprise a bus
within a computer system. Carrier-wave transmission media may take
the form of electric or electromagnetic signals, or acoustic or
light waves such as those generated during radio frequency (RF) and
infrared (IR) data communications. Common forms of
computer-readable media therefore include for example: a floppy
disk, a flexible disk, hard disk, magnetic tape, any other magnetic
medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch
cards paper tape, any other physical storage medium with patterns
of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other
memory chip or cartridge, a carrier wave transporting data or
instructions, cables or links transporting such a carrier wave, any
other medium from which a computer may read programming code and/or
data, or any combination thereof. The memory and/or storage may
comprise a storing device external to and/or removable from device,
such as a Universal Serial Bus (USB) memory stick, or/and a hard
disk. Many of these forms of computer readable media may be
involved in carrying one or more sequences of one or more
instructions to a processor for execution.
[0217] The computer system can include or be in communication with
an electronic display that comprises a user interface (UI) for
providing, for example, a model design or graphical representation
of a 3D object to be printed. Examples of UI's include, without
limitation, a graphical user interface (GUI) and web-based user
interface. The computer system can monitor and/or control various
aspects of the 3D printing system. The control may be manual and/or
programmed. The control may rely on feedback mechanisms (e.g., from
the one or more sensors). The control may rely on historical data.
The feedback mechanism may be pre-programmed. The feedback
mechanisms may rely on input from sensors (described herein) that
are connected to the control unit (i.e., control system or control
mechanism e.g., computer) and/or processing unit. The computer
system may store historical data concerning various aspects of the
operation of the 3D printing system. The historical data may be
retrieved at predetermined times and/or at a whim. The historical
data may be accessed by an operator and/or by a user. The
historical, sensor, and/or operative data may be provided in an
output unit such as a display unit. The output unit (e.g., monitor)
may output various parameters of the 3D printing system (as
described herein) in real time or in a delayed time. The output
unit may output the current 3D printed object, the ordered 3D
printed object, or both. The output unit may output the printing
progress of the 3D printed object. The output unit may output at
least one of the total time, time remaining, and time expanded on
printing the 3D object. The output unit may output (e.g., display,
voice, and/or print) the status of sensors, their reading, and/or
time for their calibration or maintenance. The output unit may
output the type of material(s) used and various characteristics of
the material(s) such as temperature and flowability of the
pre-transformed material. The output unit may output the amount of
oxygen, water, and pressure in the printing chamber (i.e., the
chamber where the 3D object is being printed). The computer may
generate a report comprising various parameters of the 3D printing
system, method, and or objects at predetermined time(s), on a
request (e.g., from an operator), and/or at a whim. The output unit
may comprise a screen, printer, or speaker. The control system may
provide a report. The report may comprise any items recited as
optionally output by the output unit.
[0218] The system and/or apparatus described herein (e.g.,
controller) and/or any of their components may comprise an output
and/or an input device. The input device may comprise a keyboard,
touch pad, or microphone. The output device may be a sensory output
device. The output device may include a visual, tactile, or audio
device. The audio device may include a loudspeaker. The visual
output device may include a screen and/or a formed (e.g., printed)
hard copy (e.g., paper). The output device may include a printer.
The input device may include a camera, a microphone, a keyboard, or
a touch screen.
[0219] The computer system can include, or be in communication
with, an electronic display unit that comprises a user interface
(UI) for providing, for example, a model design or graphical
representation of an object to be formed (e.g., printed). Examples
of UI's include a graphical user interface (GUI) and web-based user
interface. The historical and/or operative data may be displayed on
a display unit. The computer system may store historical data
concerning various aspects of the operation of the cleaning system.
The historical data may be retrieved at predetermined times and/or
at a whim. The historical data may be accessed by an operator
and/or by a user. The display unit (e.g., monitor) may display
various parameters of the forming (e.g., printing) system (as
described herein) in real time or in a delayed time. The display
unit may display the desired formed (e.g., printed) 3D object
(e.g., according to a model), the formed (e.g., printed) 3D object,
real time display of the 3D object as it is being formed (e.g.,
printed), or any combination thereof. The display unit may display
the cleaning progress of the object, or various aspects thereof.
The display unit may display at least one of the total time, time
remaining, and time expanded on the cleaned object during the
cleaning process. The display unit may display the status of
sensors, their reading, and/or time for their calibration or
maintenance. The display unit may display the type or types of
material used and various characteristics of the material or
materials such as temperature and flowability of the
pre-transformed material. The particulate material that did not
transform to form the 3D object (e.g., the remainder) disposed in
the material bed may be flowable (e.g., during the forming (e.g.,
3D printing) process). The display unit may display the amount of a
certain gas in the chamber. The gas may comprise oxygen, hydrogen,
water vapor, or any of the gasses mentioned herein. The display
unit may display the pressure in the chamber. The computer may
generate a report comprising various parameters of the methods,
objects, apparatuses, or systems described herein. The report may
be generated at predetermined time(s), on a request (e.g., from an
operator) or at a whim.
[0220] The one or more controllers can be control any suitable one
or more methods used to form 3D objects as described herein.
Various suitable control systems are disclosed in PCT patent
application serial number PCT/US2015/065297; PCT patent application
serial number PCT/US17/18191; European patent application serial
number EP17156707.6; U.S. patent application Ser. No. 15/435,065;
and U.S. provisional patent application Ser. No. 62/401,534; each
of which is incorporated herein by reference in its entirety. The
one or more controllers can comprise one or more central processing
unit (CPU), input/output (I/O) and/or communications module. The
CPU can comprise electronic circuitry that carries out instructions
of a computer program by performing basic arithmetic, logical,
control and I/O operations specified by the instructions. The
controller can comprise a suitable software (e.g., operating
system). The control system may optionally include a feedback
control loop and/or feed-forward control loop. The control system
may be configured to control (e.g. in real time) a power of the
energy source, speed of the energy beam, power density of the
energy beam, dwell time of the energy beam, energy beam footprint
(e.g., on the exposed surface of the material bed), and/or
cross-section of the energy beam, to maintain a target parameter of
one or more forming 3D objects. The target parameter may comprise a
temperature, or power of the energy beam and/or source. In some
examples, maintaining a target temperature for maintaining on one
or more characteristics of one or more melt pools. The
characteristics of the melt pool may comprise its FLS, temperature,
fluidity, viscosity, shape (e.g., of a melt pool cross section),
volume, or overall shape. The control system may be configured to
control (e.g. in real time) a temperature, to maintain a target
parameter of one or more forming 3D objects, e.g., a target
temperature of one or more positions of the material bed to
maintain on one or more melt pools.
[0221] The control system can include any suitable number of
controllers, and can be used to control any number of suitable
(e.g., different) operations. For example, in some embodiments, a
controller (e.g., a single controller) is used to control
generating one or more computer models (e.g., physics model (e.g.,
and associated simulation process), geometric model, adjusted
geometric model) and to control forming instructions (e.g.,
printing instructions, molding instructions, machining
instructions) for forming of one or more 3D objects. In some
embodiments, a number of controllers are used to control (e.g.
direct) generating one or more computer models and to control
forming instructions for forming of one or more 3D objects. For
example, a first controller can be used to control (e.g. direct)
generating one or more computer models, and a second controller can
be used to control (e.g. direct) forming instructions for forming
of one or more 3D objects. In some embodiments, multiple
controllers are used to control generating one or more computer
models, and multiple controllers are used to control forming
instructions for forming of one or more 3D objects. In some
embodiments, one controller is used to control generating one or
more computer models, and multiple controllers are used to control
forming instructions for forming of one or more 3D objects. In some
embodiments, multiple controllers are used to control generating
one or more computer models, and one controller is used to control
forming instructions for forming of one or more 3D objects.
[0222] Methods, apparatuses, and/or systems of the present
disclosure can be implemented by way of one or more algorithms. An
algorithm can be implemented by way of software upon execution by
one or more computer processors. For example, the processor can be
programmed to calculate the path of the energy beam and/or the
power per unit area emitted by the energy source (e.g., that should
be provided to the material bed in order to achieve the desired
result).
[0223] The at least one sensor can be operatively coupled to a
control system (e.g., computer control system). The sensor may
comprise light sensor, acoustic sensor, vibration sensor, chemical
sensor, electrical sensor, magnetic sensor, fluidity sensor,
movement sensor, speed sensor, position sensor, pressure sensor,
force sensor, density sensor, distance sensor, or proximity sensor.
The sensor may include temperature sensor, weight sensor, material
(e.g., powder) level sensor, metrology sensor, gas sensor, or
humidity sensor. The metrology sensor may comprise measurement
sensor (e.g., height, length, width, angle, and/or volume). The
metrology sensor may comprise a magnetic, acceleration,
orientation, or optical sensor. The sensor may transmit and/or
receive sound (e.g., echo), magnetic, electronic, or
electromagnetic signal. The electromagnetic signal may comprise a
visible, infrared, ultraviolet, ultrasound, radio wave, or
microwave signal. The metrology sensor may measure the tile. The
metrology sensor may measure the gap. The metrology sensor may
measure at least a portion of the layer of material. The layer of
material may be a pre-transformed material (e.g., powder),
transformed material, or hardened material. The metrology sensor
may measure at least a portion of the 3D object. The gas sensor may
sense any of the gas delineated herein. The distance sensor can be
a type of metrology sensor. The distance sensor may comprise an
optical sensor, or capacitance sensor. The temperature sensor can
comprise Bolometer, Bimetallic strip, calorimeter, Exhaust gas
temperature gauge, Flame detection, Gardon gauge, Golay cell, Heat
flux sensor, Infrared thermometer, Microbolometer, Microwave
radiometer, Net radiometer, Quartz thermometer, Resistance
temperature detector, Resistance thermometer, Silicon band gap
temperature sensor, Special sensor microwave/imager, Temperature
gauge, Thermistor, Thermocouple, Thermometer (e.g., resistance
thermometer), or Pyrometer. The temperature sensor may comprise an
optical sensor. The temperature sensor may comprise image
processing. The temperature sensor may comprise a camera (e.g., IR
camera, CCD camera). The pressure sensor may comprise Barograph,
Barometer, Boost gauge, Bourdon gauge, Hot filament ionization
gauge, Ionization gauge, McLeod gauge, Oscillating U-tube,
Permanent Downhole Gauge, Piezometer, Pirani gauge, Pressure
sensor, Pressure gauge, Tactile sensor, or Time pressure gauge. The
position sensor may comprise Auxanometer, Capacitive displacement
sensor, Capacitive sensing, Free fall sensor, Gravimeter,
Gyroscopic sensor, Impact sensor, Inclinometer, Integrated circuit
piezoelectric sensor, Laser rangefinder, Laser surface velocimeter,
LIDAR, Linear encoder, Linear variable differential transformer
(LVDT), Liquid capacitive inclinometers, Odometer, Photoelectric
sensor, Piezoelectric accelerometer, Rate sensor, Rotary encoder,
Rotary variable differential transformer, Selsyn, Shock detector,
Shock data logger, Tilt sensor, Tachometer, Ultrasonic thickness
gauge, Variable reluctance sensor, or Velocity receiver. The
optical sensor may comprise a Charge-coupled device, Colorimeter,
Contact image sensor, Electro-optical sensor, Infra-red sensor,
Kinetic inductance detector, light emitting diode (e.g., light
sensor), Light-addressable potentiometric sensor, Nichols
radiometer, Fiber optic sensor, Optical position sensor, Photo
detector, Photodiode, Photomultiplier tubes, Phototransistor,
Photoelectric sensor, Photoionization detector, Photomultiplier,
Photo resistor, Photo switch, Phototube, Scintillometer,
Shack-Hartmann, Single-photon avalanche diode, Superconducting
nanowire single-photon detector, Transition edge sensor, Visible
light photon counter, or Wave front sensor. The weight of the
material bed can be monitored by one or more weight sensors in, or
adjacent to, the material. For example, a weight sensor in the
material bed can be at the bottom of the material bed. The weight
sensor can be between the bottom of the enclosure (e.g., FIG. 2,
211) and the substrate (e.g., FIG. 2, 209) on which the base (e.g.,
FIG. 2, 202) or the material bed (e.g., FIG. 2, 204) may be
disposed. The weight sensor can be between the bottom of the
enclosure and the base on which the material bed may be disposed.
The weight sensor can be between the bottom of the enclosure and
the material bed. A weight sensor can comprise a pressure sensor.
The weight sensor may comprise a spring scale, a hydraulic scale, a
pneumatic scale, or a balance. At least a portion of the pressure
sensor can be exposed on a bottom surface of the material bed. In
some cases, the weight sensor can comprise a button load cell. The
button load cell can sense pressure from powder adjacent to the
load cell. In another example, one or more sensors (e.g., optical
sensors or optical level sensors) can be provided adjacent to the
material bed such as above, below, or to the side of the material
bed. In some examples, the one or more sensors can sense the powder
level. The material (e.g., powder) level sensor can be in
communication with a material dispensing mechanism (e.g., powder
dispenser). Alternatively, or additionally a sensor can be
configured to monitor the weight of the material bed by monitoring
a weight of a structure that contains the material bed. One or more
position sensors (e.g., height sensors) can measure the height of
the material bed relative to the substrate. The position sensor can
be optical sensor. The position sensor can determine a distance
between one or more energy beams (e.g., a laser or an electron
beam.) and a surface of the material (e.g., powder). The one or
more sensors may be connected to a control system (e.g., to a
processor, to a computer).
[0224] In some embodiments, the energy beam includes a radiation
comprising an electromagnetic, or charged particle beam. The energy
beam may include radiation comprising electromagnetic, electron,
positron, proton, plasma, radical or ionic radiation. The
electromagnetic beam may comprise microwave, infrared, ultraviolet,
or visible radiation. The energy beam may include an
electromagnetic energy beam, electron beam, particle beam, or ion
beam. An ion beam may include a cation or an anion. A particle beam
may include radicals. The electromagnetic beam may comprise a laser
beam. The energy beam may comprise plasma. The energy source may
include a laser source. The energy source may include an electron
gun. The energy source may include an energy source capable of
delivering energy to a point or to an area. In some embodiments,
the energy source can be a laser source. The laser source may
comprise a CO.sub.2, Nd:YAG, Neodymium (e.g., neodymium-glass), an
Ytterbium, or an excimer laser. The energy source may include an
energy source capable of delivering energy to a point or to an
area. The energy source (e.g., transforming energy source) can
provide an energy beam having an energy density of at least about
50 joules/cm.sup.2 (J/cm.sup.2), 100 J/cm.sup.2, 200 J/cm.sup.2,
300 J/cm.sup.2, 400 J/cm.sup.2, 500 J/cm.sup.2, 600 J/cm.sup.2, 700
J/cm.sup.2, 800 J/cm.sup.2, 1000 J/cm.sup.2, 1500 J/cm.sup.2, 2000
J/cm.sup.2, 2500 J/cm.sup.2, 3000 J/cm.sup.2, 3500 J/cm.sup.2, 4000
J/cm.sup.2, 4500 J/cm.sup.2, or 5000 J/cm.sup.2. The energy source
can provide an energy beam having an energy density of at most
about 50 J/cm.sup.2, 100 J/cm.sup.2, 200 J/cm.sup.2, 300
J/cm.sup.2, 400 J/cm.sup.2, 500 J/cm.sup.2, 600 J/cm.sup.2, 700
J/cm.sup.2, 800 J/cm.sup.2, 1000 J/cm.sup.2, 500 J/cm.sup.2, 1000
J/cm.sup.2, 1500 J/cm.sup.2, 2000 J/cm.sup.2, 2500 J/cm.sup.2, 3000
J/cm.sup.2, 3500 J/cm.sup.2, 4000 J/cm.sup.2, 4500 J/cm.sup.2, or
5000 J/cm.sup.2. The energy source can provide an energy beam
having an energy density of a value between the afore-mentioned
values (e.g., from about 50 J/cm.sup.2 to about 5000 J/cm.sup.2,
from about 200 J/cm.sup.2 to about 1500 J/cm.sup.2, from about 1500
J/cm.sup.2 to about 2500 J/cm.sup.2, from about 100 J/cm.sup.2 to
about 3000 J/cm.sup.2, or from about 2500 J/cm.sup.2 to about 5000
J/cm.sup.2). In an example, a laser can provide light energy at a
peak wavelength of at least about 100 nanometer (nm), 500 nm, 750
nm, 1000 nm, 1010 nm, 1020 nm, 1030 nm, 1040 nm, 1050 nm, 1060 nm,
1070 nm, 1080 nm, 1090 nm, 1100 nm, 1200 nm, 1500 nm, 1600 nm, 1700
nm, 1800 nm, 1900 nm, or 2000 nm. In an example a laser can provide
light energy at a peak wavelength of at most about 2000 nm, 1900
nm, 1800 nm, 1700 nm, 1600 nm, 1500 nm, 1200 nm, 1100 nm, 1090 nm,
1080 nm, 1070 nm, 1060 nm, 1050 nm, 1040 nm, 1030 nm, 1020 nm, 1010
nm, 1000 nm, 750 nm, 500 nm, or 100 nm. The laser can provide light
energy at a peak wavelength between any of the afore-mentioned peak
wavelength values (e.g., from about 100 nm to about 2000 nm, from
about 500 nm to about 1500 nm, or from about 1000 nm to about 1100
nm). The energy beam (e.g., laser) may have a power of at least
about 0.5 Watt (W), 1 W, 2 W, 3 W, 4 W, 5 W, 10 W, 20 W, 30 W, 40
W, 50 W, 60 W, 70 W, 80 W, 90 W, 100 W, 120 W, 150 W, 200 W, 250 W,
300 W, 350 W, 400 W, 500 W, 750 W, 800 W, 900 W, 1000 W, 1500 W,
2000 W, 3000 W, or 4000 W. The energy beam may have a power of at
most about 0.5 W, 1 W, 2 W, 3 W, 4 W, 5 W, 10 W, 20 W, 30 W, 40 W,
50 W, 60 W, 70 W, 80 W, 90 W, 100 W, 120 W, 150 W, 200 W, 250 W,
300 W, 350 W, 400 W, 500 W, 750 W, 800 W, 900 W, 1000 W, 1500, 2000
W, 3000 W, or 4000 W. The energy beam may have a power between any
of the afore-mentioned laser power values (e.g., from about 0.5 W
to about 100 W, from about 1 W to about 10 W, from about 100 W to
about 1000 W, or from about 1000 W to about 4000 W). The first
energy source (e.g., producing the transforming energy beam) may
have at least one of the characteristics of the second energy
source. The powder density (e.g., power per unit area) of the
energy beam may at least about 10000 W/mm.sup.2, 20000 W/mm.sup.2,
30000 W/mm.sup.2, 50000 W/mm.sup.2, 60000 W/mm.sup.2, 70000
W/mm.sup.2, 80000 W/mm.sup.2, 90000 W/mm.sup.2, or 100000
W/mm.sup.2. The powder density of the energy beam may be at most
about 10000 W/mm.sup.2, 20000 W/mm.sup.2, 30000 W/mm.sup.2, 50000
W/mm.sup.2, 60000 W/mm.sup.2, 70000 W/mm.sup.2, 80000 W/mm.sup.2,
90000 W/mm.sup.2, or 100000 W/mm.sup.2. The powder density of the
energy beam may be any value between the aforementioned values
(e.g., from about 10000 W/mm.sup.2 to about 100000 W/mm.sup.2, from
about 10000 W/mm.sup.2 to about 50000 W/mm.sup.2, or from about
50000 W/mm.sup.2 to about 100000 W/mm.sup.2). The scanning speed of
the energy beam may be at least about 50 millimeters per second
(mm/sec), 100 mm/sec, 500 mm/sec, 1000 mm/sec, 2000 mm/sec, 3000
mm/sec, 4000 mm/sec, or 50000 mm/sec. The scanning speed of the
energy beam may be at most about 50 mm/sec, 100 mm/sec, 500 mm/sec,
1000 mm/sec, 2000 mm/sec, 3000 mm/sec, 4000 mm/sec, or 50000
mm/sec. The scanning speed of the energy beam may any value between
the aforementioned values (e.g., from about 50 mm/sec to about
50000 mm/sec, from about 50 mm/sec to about 3000 mm/sec, or from
about 2000 mm/sec to about 50000 mm/sec). The energy beam may be
continuous or non-continuous (e.g., pulsing). The energy beam may
be modulated before and/or during the formation of a transformed
material as part of the 3D object. The energy beam may be modulated
before and/or during the 3D printing process (e.g., using one or
more controllers).
[0225] In some embodiments, two types of energy beams may be
employed for the forming process, e.g., a tiling and a hatching
energy beam, e.g., type-1 and type-2 energy beams. The hatching
energy beam may continuously move along a trajectory (e.g., path).
The hatching energy beam may be type-1 or type-2 energy beam. The
tiling energy beam may move intermittently along a trajectory. The
tiling energy beam may move along a trajectory and (i) transform a
pre-transformed material to a transformed material (referred to
herein as "dwell time"), and (ii) non transform a pre-transformed
material to a transformed material (referred to herein as
"intermission time"). At least one characteristic of the energy
beam may be controlled during the dwell time and/or intermission
time (e.g., in real time and/or in situ during a forming
operation).
[0226] In some embodiments, the type-2 energy beam comprises (i) an
extended exposure area, (ii) extended exposure time, (iii) low
power density (e.g., power per unit area) or (iv) an intensity
profile that can fill an area with a flat (e.g., top head) energy
profile. Extended may be in comparison with the type-1 energy beam.
The extended exposure time may be at least about 1 millisecond and
at most 100 milliseconds. In some embodiments, an energy profile of
the tiling energy source may exclude a Gaussian beam or round top
beam. In some embodiments, an energy profile of the tiling energy
source may include a Gaussian beam or round top beam. In some
embodiments, the 3D printer comprises a type-1 energy beams. In
some embodiments, an energy profile of the hatching energy may
comprise a Gaussian energy beam. In some embodiments, an energy
profile of the type-1 energy beam may exclude a Gaussian energy
beam. The type-1 energy beam may have any cross-sectional shape
comprising an ellipse (e.g., circle), or a polygon (e.g., as
disclosed herein). The type-1 energy beam may have a cross section
with a diameter of at least about 25 .mu.m, 50 .mu.m, 100 .mu.m,
150 .mu.m, 200 .mu.m, or 250 .mu.m. The type-1 energy beam may have
a cross section with a diameter of at most about 40 micrometers
(.mu.m), 50 .mu.m, 60 .mu.m, 70 .mu.m, 80 .mu.m, 100 .mu.m, 150
.mu.m, 200 .mu.m, or 250 .mu.m. The type-1 energy beam may have a
cross section with a diameter of any value between the
afore-mentioned values (e.g., from about 40 .mu.m to about 240
.mu.m, from about 40 .mu.m to about 100 .mu.m, from about 50 .mu.m
to about 150 .mu.m, or from about 150 .mu.m to about 250 .mu.m).
The power density (e.g., power per unit area) of the type-1 energy
beam may at least about 5000 W/mm.sup.2, 10000 W/mm.sup.2, 20000
W/mm.sup.2, 30000 W/mm.sup.2, 50000 W/mm.sup.2, 60000 W/mm.sup.2,
70000 W/mm.sup.2, 80000 W/mm.sup.2, 90000 W/mm.sup.2, or 100000
W/mm.sup.2. The power density of the type-1 energy beam may be at
most about 5000 W/mm.sup.2, 10000 W/mm.sup.2, 20000 W/mm.sup.2,
30000 W/mm.sup.2, 50000 W/mm.sup.2, 60000 W/mm.sup.2, 70000
W/mm.sup.2, 80000 W/mm.sup.2, 90000 W/mm.sup.2, or 100000
W/mm.sup.2. The power density of the type-1 energy beam may be any
value between the afore-mentioned values (e.g., from about 5000
W/mm.sup.2 to about 100000 W/mm.sup.2, from about 10000 W/mm.sup.2
to about 50000 W/mm.sup.2, or from about 50000 W/mm.sup.2 to about
100000 W/mm.sup.2). The hatching speed of the type-1 energy beam
may be at least about 50 millimeters per second (mm/sec), 100
mm/sec, 500 mm/sec, 1000 mm/sec, 2000 mm/sec, 3000 mm/sec, 4000
mm/sec, or 50000 mm/sec. The hatching speed of the type-1 energy
beam may be at most about 50 mm/sec, 100 mm/sec, 500 mm/sec, 1000
mm/sec, 2000 mm/sec, 3000 mm/sec, 4000 mm/sec, or 50000 mm/sec. The
hatching speed of the type-1 energy beam may any value between the
afore-mentioned values (e.g., from about 50 mm/sec to about 50000
mm/sec, from about 50 mm/sec to about 3000 mm/sec, or from about
2000 mm/sec to about 50000 mm/sec). The type-1 energy beam may be
continuous or non-continuous (e.g., pulsing). In some embodiments,
the type-1 energy beam compensates for heat loss at the edges of
the target surface after the heat tiling process (e.g., forming the
tiles by utilizing the type-2 energy beam). The type-1 energy beam
may be continuously moving along the path. The type-2 energy beam
may stop and move along the path (e.g., the type-2 energy beam may
transform a portion of the material bed along a path of tiles
during the "stop" time and cease to transform the material bed
along the path of tiles during the "move" time. The target surface
may be an exposed surface of the 3D object, of the platform, and/or
of the material bed.
[0227] The type-2 energy beam may have an extended cross section.
For example, the type-2 energy beam has a FLS (e.g., cross
sectional diameter) may be larger than the type-1 energy beam. The
FLS of a cross section of the type-2 energy beam may be at least
about 0.05 millimeters (mm), 0.1 mm, 0.2 mm, 0.3 mm, 0.4 mm, 0.5
mm, 0.8 mm, 1 mm, 1.5 mm, 2 mm, 2.5 mm, 3 mm, 3.5 mm, 4 mm, 4.5 mm,
or 5 mm. The FLS of a cross section of the type-2 energy beam may
be between any of the afore-mentioned values (e.g., from about 0.05
mm to about 5 mm, from about 0.05 mm to about 0.2 mm from about 0.3
mm to about 2.5 mm, or from about 2.5 mm to about 5 mm). The cross
section of the energy beam can be at least about 0.1 millimeter
squared (mm.sup.2), or 0.2. The diameter of the energy beam can be
at least about 50 micrometers (.mu.m), 70 .mu.m, 80 .mu.m, 100
.mu.m, 150 .mu.m, 200 .mu.m, 250 .mu.m, 300 .mu.m, 350, 400 .mu.m,
500 .mu.m, or 600 .mu.m. The distance between the first position
and the second position can be at least about 50 micrometers
(.mu.m), 70 .mu.m, 80 .mu.m, 100 .mu.m, 200 .mu.m, or 250 .mu.m.
The FLS may be measured at full width half maximum intensity of the
energy beam. In some embodiments, the type-2 energy beam is a
focused energy beam. In some embodiments, the type-2 energy beam is
a defocused energy beam. The energy profile of the type-2 energy
beam may be (e.g., substantially) uniform (e.g., in the beam cross
sectional area that forms the tile). The energy profile of the
type-2 energy beam may be (e.g., substantially) uniform during the
exposure time (e.g., also referred to herein as tiling time, or
dwell time). The exposure time (e.g., at the target surface) of the
type-2 energy beam may be at least about 0.1 milliseconds (msec),
0.5 msec, 1 msec, 10 msec, 20 msec, 30 msec, 40 msec, 50 msec, 60
msec, 70 msec, 80 msec, 90 msec, 100 msec, 200 msec, 400 msec, 500
msec, 1000 msec, 2500 msec, or 5000 msec. The exposure time (e.g.,
at the target surface) of the type-2 energy beam may be at most
about 10 msec, 20 msec, 30 msec, 40 msec, 50 msec, 60 msec, 70
msec, 80 msec, 90 msec, 100 msec, 200 msec, 400 msec, 500 msec,
1000 msec, 2500 msec, or 5000 msec. The exposure time may be
between any of the above-mentioned exposure times (e.g., from about
0.1 msec to about 5000 msec, from about 0.1 to about 1 msec, from
about 1 msec to about 50 msec, from about 50 msec to about 100
msec, from about 100 msec to about 1000 msec, from about 20 msec to
about 200 msec, or from about 1000 msec to about 5000 msec). The
exposure time may be the dwell time. The power per unit area of the
type-2 energy beam may be at least about 100 Watts per millimeter
square (W/mm.sup.2), 200 W/mm.sup.2, 300 W/mm.sup.2, 400
W/mm.sup.2, 500 W/mm.sup.2, 600 W/mm.sup.2, 700 W/mm.sup.2, 800
W/mm.sup.2, 900 W/mm.sup.2, 1000 W/mm.sup.2, 2000 W/mm.sup.2, 3000
W/mm.sup.2, 5000 W/mm2, or 7000 W/mm.sup.2. The power per unit area
of the type-2 energy beam may be at most about 100 W/mm.sup.2, 200
W/mm.sup.2, 300 W/mm.sup.2, 400 W/mm.sup.2, 500 W/mm.sup.2, 600
W/mm.sup.2, 700 W/mm.sup.2, 800 W/mm.sup.2, 900 W/mm.sup.2, 1000
W/mm.sup.2, 2000 W/mm.sup.2, 3000 W/mm.sup.2, 5000 W/mm.sup.2, 7000
W/mm.sup.2, 8000 W/mm.sup.2, 9000 W/mm.sup.2, or 10000 W/mm.sup.2.
The power per unit area of the type-2 energy beam may be any value
between the afore-mentioned values (e.g., from about 100 W/mm.sup.2
to about 3000 W/mm.sup.2, from about 100 W/mm.sup.2 to about 5000
W/mm.sup.2, from about 100 W/mm.sup.2 to about 9000 W/mm.sup.2,
from about 100 W/mm.sup.2 to about 500 W/mm.sup.2, from about 500
W/mm.sup.2 to about 3000 W/mm.sup.2, from about 1000 W/mm.sup.2 to
about 7000 W/mm.sup.2, or from about 500 W/mm.sup.2 to about 8000
W/mm.sup.2). The type-2 energy beam may emit energy stream towards
the target surface in a step and repeat sequence.
[0228] The FLS (e.g., the diameter, spherical equivalent diameter,
diameter of a bounding circle, or largest of height, width and
length) of the formed (e.g., printed) 3D object or a portion
thereof can be at least about 50 micrometers (.mu.m), 80 .mu.m, 100
.mu.m, 120 .mu.m, 150 .mu.m, 170 .mu.m, 200 .mu.m, 230 .mu.m, 250
.mu.m, 270 .mu.m, 300 .mu.m, 400 .mu.m, 500 .mu.m, 600 .mu.m, 700
.mu.m, 800 .mu.m, 1 mm, 1.5 mm, 2 mm, 3 mm, 5 mm, 1 cm, 1.5 cm, 2
cm, 10 cm, 20 cm, 30 cm, 40 cm, 50 cm, 60 cm, 70 cm, 80 cm, 90 cm,
1m, 2m, 3m, 4m, 5m, 10m, 50m, 80m, or 100m. The FLS of the formed
(e.g., printed) 3D object or a portion thereof can be at most about
150 .mu.m, 170 .mu.m, 200 .mu.m, 230 .mu.m, 250 .mu.m, 270 .mu.m,
300 .mu.m, 400 .mu.m, 500 .mu.m, 600 .mu.m, 700 .mu.m, 800 .mu.m, 1
mm, 1.5 mm, 2 mm, 3 mm, 5 mm, 1 cm, 1.5 cm, 2 cm, 10 cm, 20 cm, 30
cm, 40 cm, 50 cm, 60 cm, 70 cm, 80 cm, 90 cm, 1m, 2m, 3m, 4m, 5m,
10m, 50m, 80m, 100m, 500m, or 1000m. The FLS of the formed (e.g.,
printed) 3D object or a portion thereof can any value between the
afore-mentioned values (e.g., from about 50 .mu.m to about 1000m,
from about 500 .mu.m to about 100m, from about 50 .mu.m to about 50
cm, or from about 50 cm to about 1000m). In some cases, the FLS of
the formed (e.g., printed) 3D object or a portion thereof may be in
between any of the afore-mentioned FLS values. The portion of the
3D object may be a heated portion or disposed portion (e.g.,
tile).
[0229] The layer of pre-transformed material (e.g., powder) may be
of a predetermined height (thickness). The layer of pre-transformed
material can comprise the material prior to its transformation in
the forming (e.g., 3D printing) process. The layer of
pre-transformed material may have an upper surface that is
substantially flat, leveled, or smooth. In some instances, the
layer of pre-transformed material may have an upper surface that is
not flat, leveled, or smooth. The layer of pre-transformed material
may have an upper surface that is corrugated or uneven. The layer
of pre-transformed material may have an average or mean (e.g.,
pre-determined) height. The height of the layer of pre-transformed
material (e.g., powder) may be at least about 5 micrometers
(.mu.m), 10 .mu.m, 20 .mu.m, 30 .mu.m, 40 .mu.m, 50 .mu.m, 60
.mu.m, 70 .mu.m, 80 .mu.m, 90 .mu.m, 100 .mu.m, 200 .mu.m, 300
.mu.m, 400 .mu.m, 500 .mu.m, 600 .mu.m, 700 .mu.m, 800 .mu.m, 900
.mu.m, 1 mm, 2 mm, 3 mm, 4 mm, 5 mm, 6 mm, 7 mm, 8 mm, 9 mm, 10 mm,
20 mm, 30 mm, 40 mm, 50 mm, 60 mm, 70 mm, 80 mm, 90 mm, 100 mm, 200
mm, 300 mm, 400 mm, 500 mm, 600 mm, 700 mm, 800 mm, 900 mm, or 1000
mm. The height of the layer of pre-transformed material may be at
most about 5 micrometers (.mu.m), 10 .mu.m, 20 .mu.m, 30 .mu.m, 40
.mu.m, 50 .mu.m, 60 .mu.m, 70 .mu.m, 80 .mu.m, 90 .mu.m, 100 .mu.m,
200 .mu.m, 300 .mu.m, 400 .mu.m, 500 .mu.m, 600 .mu.m, 700 .mu.m,
800 .mu.m, 900 .mu.m, 1 mm, 2 mm, 3 mm, 4 mm, 5 mm, 6 mm, 7 mm, 8
mm, 9 mm, 10 mm, 20 mm, 30 mm, 40 mm, 50 mm, 60 mm, 70 mm, 80 mm,
90 mm, 100 mm, 200 mm, 300 mm, 400 mm, 500 mm, 600 mm, 700 mm, 800
mm, 900 mm, or 1000 mm. The height of the layer of pre-transformed
material may be any number between the afore-mentioned heights
(e.g., from about 5 .mu.m to about 1000 mm, from about 5 .mu.m to
about 1 mm, from about 25 .mu.m to about 1 mm, or from about 1 mm
to about 1000 mm). The "height" of the layer of material (e.g.,
powder) may at times be referred to as the "thickness" of the layer
of material. In some instances, the layer of hardened material may
be a sheet of metal. The layer of hardened material may be
fabricated using a 3D manufacturing methodology. Occasionally, the
first layer of hardened material may be thicker than a subsequent
layer of hardened material. The first layer of hardened material
may be at least about 1.1 times, 1.2 times, 1.4 times, 1.6 times,
1.8 times, 2 times, 4 times, 6 times, 8 times, 10 times, 20 times,
30 times, 50 times, 100 times, 500 times, 1000 times, or thicker
(higher) than the average (or mean) thickness of a subsequent layer
of hardened material, the average thickens of an average subsequent
layer of hardened material, or the average thickness of any of the
subsequent layers of hardened material. FIG. 5 shows an example of
a schematic cross section in a 3D object 503 comprised of layers of
hardened material numbered 1 to 3, with 1 being the first layer
(e.g., bottom skin layer). In some instances, layer #1 can be
thicker than any of the subsequent layers (e.g., layers #2 to #3).
In some instances, layer #1 can be thicker than an average thickens
of the subsequent layers (e.g., layers #2 to #3). The very first
layer of hardened material formed in the material bed by forming
(e.g., 3D printing) may be referred herein as the "bottom skin"
layer.
[0230] In some instances, one or more intervening layers separate
adjacent components from one another. For example, the one or more
intervening layers can have a thickness of at most about 10
micrometers ("microns"), 1 micron, 500 nanometers ("nm"), 100 nm,
50 nm, 10 nm, or 1 nm. For example, the one or more intervening
layers can have a thickness of at least about 10 micrometers
("microns"), 1 micron, 500 nanometers ("nm"), 100 nm, 50 nm, 10 nm,
or 1 nm. In an example, a first layer is adjacent to a second layer
when the first layer is in direct contact with the second layer. In
another example, a first layer is adjacent to a second layer when
the first layer is separated from the second layer by a third
layer. In some instances, adjacent to may be `above` or `below.`
Below can be in the direction of the gravitational force or towards
the platform. Above can be in the direction opposite to the
gravitational force or away from the platform.
[0231] A comparison between a geometric model and a formed object
can be done using any suitable technique. In some embodiments, an
image (created, for example, from an imaging and/or scanning
operation) of an object can be compared to the (virtual) geometric
model (e.g., initial/original geometric model) that was used to
form the object. For example, the locations of the image markers
(representing the physical markers of the 3D object) can be
compared to the locations of the model markers of the geometric
model of the 3D object. The comparison can comprise performing a
data analysis, e.g., as described herein. Data analysis may
comprise data mining. For example, the data analysis can comprise a
regression (e.g., least squares) analysis. Regression analysis may
comprise parametric or non-parametric regression. Parametric
regression may comprise linear, or least squares regression.
Non-parametric regression may comprise Gaussian process (Kriging),
Kernel, or non-parametric multiplicative regression. The regression
analysis may comprise a regression tree. If it is determined that
the locations of the physical markers are within a predetermined
threshold range (e.g., within an acceptable variance, or an
acceptable error range) of the locations of the model markers, a
corresponding object (also referred to herein as a "requested
object", "final object", or "desired object") can be formed (e.g.,
printed). If it is determined that the locations of the physical
markers are not within the predetermined threshold (e.g., outside
of an acceptable variance, or error range) of the locations of the
model markers, the geometric model can be adjusted (e.g.,
corrected, improved, updated) to. The adjustment of the geometric
model may compensate for the deformation caused by the forming
process (e.g., OPC). In some embodiments, mythologies of continuum
mechanics are used as tracking methods. Continuum mechanics may
comprise fluid mechanics. Fluid mechanics may comprise an
Lagrangian or Eulerian frame of reference (e.g., specification of
the flow field, e.g., coordinate system). For example, Lagrangian
particle tracking method (LPT) may be used. In some embodiments, a
Lagrangian tracking method is used: e.g., locations of the model
markers can be designated X'.sub.i and locations of the physical
markers can be designated x.sub.i; with a goal of adjusting the
geometric model to satisfy x.sub.i=X'.sub.i. In some embodiments,
the geometric model is adjusted in accordance with the following
Equation 1:
X'.sub.i.sup.(n+1)=X'.sub.i.sup.(n)+g(x.sub.i.sup.(n));
[0232] where x.sub.i is the measured locations of the physical
markers (e.g., in scanner coordinate system); X'.sub.i is the
locations of the model markers (also referred to as nominal
locations); n is the number of iterations; and g(x.sub.i) is an
adjustment function (also referred to as an "update function"). An
example calculation for an adjustment function g(x.sub.i) is
described below with reference to Equation 11. In some embodiments,
the geometric model is adjusted considering (e.g., based on) an
optimization (e.g. using OPC, e.g., as described herein). In some
embodiments, the geometric model is adjusted, e.g., using
regression analysis (e.g., a least squares fit). In some
embodiments, a nonlinear least squares technique is used. For
example, an optimization can be calculated using distances between
markers. For example, one or more matrices may be used to represent
physical markers and/or model markers, such as in accordance with
the following Equation 2:
d.sub.ij=.parallel.X'.sub.i-x.sub.j.parallel.;
[0233] where x.sub.i is the measured locations of the physical
markers (e.g., in scanner coordinate system), where X'.sub.j is the
measured locations of the model markers of the geometric model; and
d.sub.ij is the distances (e.g., distance matrix). d.sub.ij is a
symmetric matrix (e.g., m by m matrix, where m is the number of
markers) Distance matrices between a multiple number (e.g., n
number) of markers (X'.sub.i and x.sub.j) can be calculated using
Equation 2. An adjusted geometric model can be used to form one or
more additional objects (e.g., one or more test objects), which can
then be compared to an object (e.g., the comparison may be done
with a scanned image of the formed object). For example, the
process of adjustment and formation can be repeated until locations
of the physical markers and model markers (e.g., substantially)
match, e.g., in accordance with one or more of the optimization
calculations described herein. The process can be iteratively
repeated until an object has dimensions within the predetermined
threshold range (e.g., has (e.g., substantially) desirable
dimensions). For example, the process can be iteratively repeated
until the data sets (e.g., substantially) converge. The data sets
may comprise the markers and/or the geometry of the requested 3D
object and the geometry of the formed 3D object. When a
sufficiently adjusted geometric model is obtained, the adjusted
geometric model can be used to form the requested part (e.g., using
OPC). FIG. 21 shows perspective views of an example first image
2100 and second image 2120 corresponding to a first (test) object
and a second (test) object, respectively. First image 2100 includes
first image markers 2102, and a second image 2120 includes second
image markers 2122. A first object (e.g., a first object
represented by first image 2100) can be formed using instructions
considering (e.g., based on) a geometric model without any
adjustment (e.g., corrections) (which can be referred to as a
"first geometric model," "prior geometric model," "previous
geometric model," "initial geometric model," or "original geometric
model"). The image markers (e.g., image markers 2102) of the first
image can be compared to model markers of the geometric model, and
used to adjust the geometric model (which can be referred to as a
"second geometric model," "adjusted geometric model," or
"subsequent geometric model"). The adjusted geometric model is used
to form a second object (e.g., a second object represented by
second image 2120 after one adjustment). In some cases, the second
image markers of the second image can be used to further adjust the
geometric model (which can be referred to as "adjusted geometric
model," "subsequent geometric model," or "further adjusted
geometric model"). This process can be iteratively repeated until
the geometric model is finally adjusted (e.g., when convergence is
achieved).
[0234] In some embodiments, two vectors or two vector-sets are used
(e.g., one for the physical marker (or model markers) and one for
the image markers). In some embodiments, two distance matrices are
used (e.g., one for the physical marker (or model markers) and one
for the image markers). The amount of deformation can be quantified
using the distance matrices using any suitable metrics. In some
embodiments, an amount of deformation in a formed object (e.g.,
test object) is quantified by analyzing differences between two
distance matrices. In some embodiments, a first distance matrix
includes location information for the physical markers of the test
object (or corresponding image markers of the corresponding image)
and a second distance matrix includes location information for the
model markers of the geometric model. An amount of deformation in a
formed object (e.g., test object) can be quantified by solving for
the displacement vector at each of the model markers locations,
e.g., by matching the first distance matrix and a modified second
distance matrix, e.g., by matching a first distance vector set and
a modified second vector set. The modified second distance matrix
can be the distance matrix of the model markers of the geometric
model that have been displaced with the computed displacement
vectors. This computation can be performed iteratively using a
regression analysis (e.g., as disclosed herein), e.g., using a
weighted non-linear least squares regression techniques. The vector
or vector set may be represented as a matrix.
[0235] At times, it can be desirable for a geometric model to
include adjustments (if necessary) that take into account
empirically collected data from one or more forming process (e.g.,
one or more printing processes). In this way, the geometric model
can be used to reliably form multiple 3D objects with good
dimensional precision and repeatability. In some embodiments, a
geometric model is adjusted considering (e.g., based on) a
simulated process. The simulated process can involve using one or
more simulations of predicted deformation of an object, e.g., due
to changes in one or more characteristics of the object in the
formation process (e.g., printing process, extrusion process, or
molding process). The simulation(s) can consider (e.g., be based
on) one or more physics-based premises, postulations, and/or
calculations that can collectively form a model (also referred to
herein as a "physics model" or "simulated model"). The one or more
physics-based calculations can consider basic principles (e.g.,
first physics principals) of physics (e.g., comprising continuum
mechanics). A physics model can take into consideration one or more
physics-based calculations and/or empirical evidence. The physics
model may consider thermo-mechanical behavior, material properties,
geometric properties, or any combination thereof, of the (e.g.,
requested) object. A simulation using a physics model can be
applied to the geometric model to simulate a forming process. Thus,
a predicted deformation of an object as a result of the forming
process can be calculated using a physics model (and sometimes
performing an associated simulation) and/or empirical data (e.g.,
obtained from a test object). The result of the simulation applied
to the geometric model can be used to form a (virtual) simulated
object. The simulated object can then be compared to the geometric
model of the requested part to determine how accurate and/or
reliable the physics model (and simulation) are at predicting
deformation. The physics model (and simulation) can be used to
adjust the geometric model for forming an object (e.g., requested
object or simulated object) to compensate for the predicted
deformation. The adjusted geometric model can then be used as a
corrected geometric model for forming the requested part(s).
Conversely, the formed object (e.g., requested 3D object and/or
test object) may be used to train the physics model, e.g., to
achieve an accurate and/or reliable physics model. The physics
model may be optimized to fit a forming process and/or machinery.
The physics model may be used to optimize a forming process and/or
machinery.
[0236] In some embodiments, a predicted change of at least one
characteristic of the 3D object resulting from the forming process
can be calculated using a physics model, e.g., considering one or
more physics-based calculations. The physics model (e.g.,
considering one or more physics-based calculations) can be used to
at least partially resolve temporal and/or spatial scales of
interest. For example, when the material of a 3D object is being
transformed from a pre-transformed material to a transformed
material, the transformed and pre-transformed material may be
subjected to a different (e.g., higher or lower) temperature.
Different types of material (e.g., metal (including elemental metal
or metal alloy), non-metal, plastic, glass, ceramic, an allotrope
of elemental carbon, etc.) have different thermo-mechanical
characteristics (e.g., expansion and/or contraction). In some
embodiments, the physics model (e.g., and associated simulations)
includes calculations of estimated deformation (e.g., are based on)
that consider the type of material of the 3D object (e.g.,
comprising thermo-mechanics or fluid dynamics, e.g., comprising
thermal expansion, thermal conductivity, or surface tension.). The
deformation may involve changes due to thermo-mechanical properties
of the object. The thermo-mechanical properties may cause changes
in a dimension and/or another mechanical property due to
temperature change, e.g., microstructure manifestation that are
characteristic of the particular forming process. Thus, in some
embodiments, the physics model (and associated simulations)
includes calculations of estimated deformation that consider (e.g.,
are based on) continuum mechanical (e.g., comprising
thermo-mechanical and/or fluid dynamic) analyses of the object
and/or its forming process. The material of the 3D object may be in
partially or fully molten form for at least part of the
transformation process. In some embodiments, the physics model (and
associated simulations) include calculations of estimated
deformation that consider (e.g., are based on) fluid dynamics. In
some embodiments, the physics model (and associated simulations)
includes calculations of estimated deformation that consider (e.g.,
are based on) surface tension of a material (e.g., pre-transformed
and/or transformed material). The pre-transformed material may be
in one form (e.g., powder) and the transformed material may be in
another form (e.g., bulk). Thus, in some embodiments, the physics
model (and associated simulations) includes calculations of an
estimated deformation that consider change in state of the material
(e.g., in relation to density and/or surface tension). The 3D
object can be characterized as having an overall shape (e.g., cone
shape, toroidal shape, disk shape, disc cone shape, spherical
shape, wing shape, spiral shape, or bridge shape.) that can cause
it to deform in a characteristic way. Thus, in some embodiments,
the physics model (and associated simulations) includes
calculations that consider estimated deformation which can consider
an overall geometry of the object. The estimate deformation may
comprise inelastic (e.g., plastic), elastic, thermally induced, or
any suitable combination thereof. The 3D object can include a
geometric features (e.g., edges, corners, overhangs, or a cavity
ceiling) that may deform. The 3D object can comprise a complex 3D
object, e.g., having cavities, overhangs). The 3D object may
comprise non-supported segments (e.g., cavity ceiling or overhang).
The non-supported segment may have shallow angles with respect to
the build plane and/or layering plane (of the layers composing the
3D object). The shallow angles may be an angle of at most 45
degrees (.degree.), 40.degree., 35.degree., 30.degree., 25.degree.,
20.degree., 15.degree., 10.degree., 5.degree., 1.degree., or
0.5.degree. with respect to the platform and/or the average
layering plane. The non-supported segment may be (e.g.,
substantially) parallel to the platform and/or the average layering
plane. The non-supported segment may have a FLS of at least 2 mm,
10 mm, 25 mm, 45 mm, 75 mm, or 100 mm. The average layering plane
may be (e.g., substantially) planar. The average layering plane may
have a radius of curvature of at least 5 centimeters (cm), 25 cm,
50 cm, 100 cm, 5 meters (m), 10m, or 100m. The deformation may be
in the same, or in a different manner than other parts of the 3D
object. In some embodiments, the physics model (and associated
simulations) includes calculations that consider an estimated
deformation of a geometric feature of the object. One type, or
different types of energy beams (e.g., laser beam, electron beam,
or both) may be used to transform the material of the 3D object
(e.g., in 3D printing or welding). Thus, in some embodiments, the
physics model (and associated simulations) includes calculations of
estimated deformation that consider at least one characteristic of
the energy beam(s) (e.g., type of energy beam(s)). The energy
beam(s) can have different characteristics (e.g., comprising power
density, target depth, cross section, footprint, wavelength,
velocity, mode, trajectory, dwell time, intermission time, or
type.). The mode of the energy beam may comprise continuous, or
pulsing. Different types of energy beam scanning (e.g., tiling,
hatching) may be used to transform the material of the 3D object.
Thus, in some embodiments, the physics model (and associated
simulations) includes calculations of estimated deformation that
consider the path(s) of the energy beam(s). Different types of
energy beam paths and dwell times may be used to transform the
material of the 3D object. Thus, in some embodiments, the physics
model (e.g., and associated simulations) includes calculations of
estimated deformation that consider the dwell times of the energy
beam(s). One or more portions of the 3D object may be transformed
using one type of energy beam and one or more other portions of the
3D object may be transformed using a different type of energy beam.
Thus, in some embodiments, the physics model (and associated
simulations) includes calculations of estimated deformation of
different portions of the 3D object. For example, a 3D printing
operation can involve stacking of multiple layers of material, each
of which may experience heating and cooling at different times. The
different layers of a printed 3D object may experience (e.g.,
substantially) the same or different pressure gradients related to
stress of the 3D object (e.g., as distinguished from a pressure
gradient or lack thereof, within a material bed, such as described
herein). Thus, in some embodiments, the physics model (and
associated simulations) includes calculations of estimated
deformation that consider (e.g., are based on) current and/or
previous stacking (e.g., accumulation) of the layers (e.g.,
considering a strain/stress that arises from stacking of the
layers). The stress may be a latent or ancillary stress. The strain
may be a latent or ancillary strain. A physics model can include
any suitable combination of physics-based calculations and
simulations, such as suitable combinations of those described
herein.
[0237] In some cases, it may be desirable to simplify the
physics-based model. In some cases, it may be desirable to limit
the number of types of physics-based calculations and/or the number
of degrees of freedom of a physics based calculation. The type of
physics-based calculation can refer to the type of physics and/or
mathematical principals (e.g., inelastic (e.g., plastic)
deformation, elastic deformation, etc.) The degrees of freedom of a
physics-based calculation(s) can refer to the number of variables
(e.g., parameters, data points) used in the calculation(s). The
degrees of freedom can refer to a complexity of the physics-based
calculation(s), with lower degrees of freedom associated with
reduced complexity. In some embodiments, the degrees of freedom
refer to a density (coarseness) of the mesh used to model the
geometry of an object (e.g., lower density (coarser) mesh
associated with less degrees of freedom). In some embodiments, the
degrees of freedom are reduced using model reduction techniques
(e.g., "model order reduction" techniques). The degrees of freedom
may be reduced by implementing assumptions and/or estimations. The
assumptions and/or estimations may be based on a formed (e.g.,
test) object, and its comparison to a model (e.g., requested)
object, e.g., that is used to formulate the forming instructions.
In some embodiments, the degrees of freedom are reduced by using a
training algorithm. In some embodiments, the degrees of freedom are
reduced without changing (e.g., reducing) the number of types of
physics-based calculations. In some embodiments, the degrees of
freedom are reduced in addition to changing (e.g., reducing) the
number of types of physics-based calculations. For example, in some
cases it may be desirable to reduce the degrees of freedom (e.g.,
use coarser mesh) and disregard certain physics-based calculations
(e.g., disregarding inelastic (e.g., plastic) deformation while
regarding elastic deformation).
[0238] Reducing the degrees of freedom and/or the number of types
of physics-based calculations may reduce a computational cost of,
and/or time required for running the simulation (and/or generating
the physics model). Reducing the degrees of freedom may facilitate
adjusting the geometric model for forming the object within a
predetermined time (e.g., in real-time). For example, it may be
advantageous to run one or more simulations in real time (e.g.,
during a forming (e.g., printing) operation, e.g., during a
transformation operation of an energy beam). A physics model that
considers nine or more types of physics-based calculations and/or
degrees of freedom can be referred to herein as an "expanded
physics model". A physics model that considers (e.g., is based on)
eight or fewer types of physics-based calculations and/or degrees
of freedom can be referred to herein as a "reduced physics model",
"simplified physic model" or "subset physics model". The four types
of physics-based calculations may comprise: thermo-elastic,
thermo-inelastic, time dependent (e.g., vs. non-time dependent),
phase transformation, chemical reaction, dynamic inertial, boundary
conditions, or initial conditions. In some embodiments, a physics
model (e.g., reduced physics model) includes physics based
components that are expected to dominantly contribute to the
deformation of the 3D object (e.g., include dominant modes). For
example, in a particular embodiment, a reduced physics model
considers the type of material (e.g., type of alloy) of the
pre-transformed and/or the transformed material and a number (e.g.,
below a threshold) of dominant inelastic (e.g., plastic) and/or
elastic deformation of the object. As described herein, in some
embodiments, the physics model includes calculations that consider
an expected thermo-mechanical (e.g., thermo-plastic) deformation of
the object. In some cases, an estimated thermo-plastic deformation
can be used to at least partially predict deformation of the object
(as compared to, for example, the geometric model of the requested
object). The deformation may comprise warpage. In some embodiments,
an expected thermo-plastic (e.g., thermal component of a
thermo-mechanical model) is calculated by computing a thermal
balance in the material using the following Equation 3:
.rho. c .rho. .differential. T .differential. t + .gradient. x q =
.rho. r ; ##EQU00001##
[0239] Where t is time, T=T(t, x) is the temperature field, x is a
deformation point; c.sub..rho.=c.sub..rho.(T) is the heat capacity
of the material as a function of temperature; .rho.=.rho.(t, x) is
the density; r=r (t, x) is the energy source field per unit mass;
q=-.gradient..sub.xT; and .gradient..sub.xT is the temperature
gradient. The heat capacity can include a latent heat of melting
for the material and the material properties can be assumed to be
temperature dependent. An expected mechanical deformation (e.g.,
mechanical component of a thermo-mechanical model) can be
calculated by finding the function x=.PHI.(t, X) using the
following Equation 4, such that:
.gradient..sub.xP(t,X)=0;
[0240] where P=P(t, X) is a stress tensor. The stress tensor can be
the first Piola-Kirchhoff stress tensor. Equivalent forms of the
above equation can comprise a different stress tensor. The
different stress tensor may be a Cauchy, Nominal, Piola, second
Piola-Kirchhoff, or Biot stress tensor. Equation 4 can assume
inertial terms are negligible (e.g., quasistatic approximation of
the momentum equation). The constitutive model for the material can
be calculated and using the following Equation 5:
S=C:.epsilon..sub.el;
[0241] where S=F.sup.-1 P is the same or another stress tensor,
e.g., the second Piola-Kirchhoff stress tensor; C is the elastic
4-tensor of the material, and .epsilon..sub.el is the elastic
strain tensor.
[0242] The deformation may be caused by a material reaction to
external loads, body forces (e.g., gravity), changes in
temperature, chemical content, chemical reaction, or any
combination thereof.
[0243] In some embodiments, a physics model includes calculations
that consider a type of material (e.g., type of alloy) and an
expected thermo-mechanical reaction of that material to the forming
process, e.g., that causes deformation. In some embodiments, the
physics model rely on one or more assumptions. In one example, the
physics model relies on the following assumptions: (i) an optimal
energy beam process (e.g., is applied maintains a constant peak
temperature over a dwell time) (e.g., an optimal tiling process);
and (ii) a closed loop control is employed to adjust process
parameters in real time. In some embodiments, the reduced set
physics model (e.g., also) assumes: (iii) strain/stress related
effects. The strain/stress related effects may be applied to a
layer, e.g., independent of or dependent on a stress field of any
underling structure. It should be noted that these assumptions are
used in some examples and are not necessarily used in other physics
models. In some embodiments, the physics model can be used to
calculate a predicted deformation substantially in real time. The
real-time calculations can allow predictions to be provided in real
time during a forming operation. The real-time calculations can be
used in a feed forward and/or feedback (closed loop) control
system(s) that controls the forming process. In some embodiments, a
physics model can be used to filter out noise (e.g., using a filter
bank).
[0244] The physics model can include calculations using any
suitable data analysis techniques, e.g., as described herein. The
calculations may comprise predictive modeling. The calculations may
comprise exploratory data analysis. The calculations may comprise
method that facilitate visualization of genetic distance and
relatedness between populations. PCA can be done by eigenvalue
decomposition of a data covariance (or correlation) matrix or
singular value decomposition of a data matrix. The results of a PCA
are usually discussed in terms of component scores, sometimes
called factor scores (the transformed variable values corresponding
to a particular data point), and loadings (the weight by which each
standardized original variable should be multiplied to get the
component score). The calculation may comprise (e.g., true)
eigenvector-based multivariate analyses. The calculations may
reveal the internal structure of the data, e.g., in a way that best
explains the variance in the data. The calculation may comprise
factor analysis. Factor analysis may incorporate domain specific
assumptions about the underlying structure. The calculation may
comprise a canonical correlation analysis (CCA). The calculation
may define a coordinate-systems that optimally describe a
cross-covariance between two datasets. The calculation may comprise
a new orthogonal coordinate system that optimally describes
variance in a single dataset. The data analysis may comprise a
statistical procedure. The statistical procedure may use an
orthogonal transformation to convert a set of observations (e.g.,
test object, and/or formed markers) of possibly correlated
variables into a set of values of linearly uncorrelated variables
(referred to herein as "principal components," "principal modes of
variation," or "modes"). In some embodiments, a number of principal
components is at most (i) the smaller of the number of original
variables, or (ii) the number of observations. The data analysis
may comprise a transformation in which the first principal
component has the largest possible variance (e.g., accounts for a
maximum variability in the data), and each succeeding component in
turn has the highest variance possible under the constraint that it
is orthogonal to the preceding components. Resulting vectors of the
transformation may be an uncorrelated orthogonal basis set. The
data analysis can comprise a proper orthogonal decomposition (POD).
The data analysis can comprise dynamical mode analysis, or
dynamical orthogonal decomposition.
[0245] In some cases, the physics model includes calculation using
principal component analysis (PCA) techniques. PCA may be sensitive
to a relative scaling of the original variables. Results of the PCA
(e.g., the modes) may be referred to in terms of component scores
(e.g., factor scores), and loadings. The loadings may be the weight
by which each standardized original variable should be multiplied
to get the component score. Each of the modes has a unique energy,
that relates to its loading. The singular values of the
decomposition can correlate to inverse-energies of the modes. The
factor scores can be a normalization of the modes. The PCA can
consider one or more of the physics-based calculations described
herein (e.g., material type, estimated elastic and/or inelastic
deformation, fluid dynamics, etc.). The PCA can include calculating
estimated (e.g., predicted) "modes" (also referred to herein as
"components") of the formed object. Each mode can represent a
plausible (also referred herein as possible, estimated, or
probable) component of the object as a result of and/or during a
forming process. In some embodiments, the physics model includes
calculations for predicting (or estimating) modes of the object
that consider thermo-mechanical properties of the object. In some
embodiments, the modes consider elastic deformation (e.g.,
nonlinear elastic deformation) modes of the object. The modes can
represent elastic responses to inelastic forces applied to the
object. The modes can correspond to deformation geometries of the
object that result from the forming process. In one embodiment, a
predicted nonlinear elastic deformation (i) takes plastic strain
field as input, (ii) computes displacement that satisfies
equilibrium, (iii) enforces conservation of momentum applied to a
continuum, or (iv) any combination thereof. The plastic strain
field can be modeled using what can be referred to as Eigenstrain
modes. The eigenstrain modes may represent geometric states of an
object that consider inherent strain (also referred to as
Eigenstrain). Each mode (Eigenstrain mode) can have an associated
energy (also referred to herein as weight), with lower energy
(lower weight) modes associated with higher stability. In some
embodiments, a physics model considering modes can take into
account a predicted elastic response of the object brought on by
any suitable force. In some embodiments, the force is not limited
to the forces applied in the course of a particular forming
operation (e.g., resulting from the forming operation). The modes
can take into consideration: (i) a new layer that adds plastic
strain near the top of the object; (ii) a new layer that at least
partially cancels out the previously deposited plastic strain, or
(iii) any combination thereof. The associated energies for the
modes can be represented in graphical form (also referred to herein
as a spectrum of the modes). The spectrum can be used to determine
those modes that are predicted to be the most prominent of the
elastic deformation modes (e.g., modes having a lower energy). The
prominent modes can correspond to predicted modes that achieve, or
are closest in achieving, thermo-mechanical equilibrium. The
prominent modes can correspond to those modes having an associated
energy within (e.g., below) a predetermined value (threshold). The
modes can be calculated by applying a plastic strain kernel
(.epsilon..sub.kernel) at different z-layers of a stack (e.g.,
printed stack). The displacement data from single calculations can
be used to populate columns a matrix U. The calculations may
comprise factorization of a real or complex matrix. A singular
value decomposition (SVD) calculation can be solved according to
the following Equation 6:
U=V.LAMBDA.Q.sup.T;
[0246] where V is the left singular vectors; .LAMBDA. is singular
values corresponding to the spectrum of Eigenstrain modes (in
diagonal form); and Q.sup.T is right singular vectors (e.g.,
conjugate transpose of unitary matrix).
[0247] In some examples, an accumulation calculation of the plastic
strain field .epsilon. can be performed using the following
Equation 7:
.epsilon..sub.n+1.sup.P(z)=.epsilon..sub.n.sup.PW(z.sub.n+1z)+A(z.sub.n+-
1-z)K;
[0248] where (A(h)) is a plastic strain amplitude according to the
following Equation 8:
A ( h ) = c 0 exp ( - h 2 2 c 1 2 ) ; ##EQU00002##
[0249] where W(h) is an erasure function according to the following
Equation 9:
W ( h ) = 0.5 1 + exp [ 12 ( c 2 - x ) c 3 ] + 0.5 ;
##EQU00003##
[0250] and where K is a plastic strain kernel. In one example
implementation, K is calculated according to the following Equation
10:
K=diag([-1,-1,2]).
[0251] The plastic strain field .epsilon. can be used as input in
the calculation (e.g., corresponding to the forcing term) for a
predicted nonlinear elastic deformation, as described above.
[0252] FIG. 15A shows a perspective view of an example geometric
model (e.g., computer aided design (CAD) drawing) of a requested
object having a requested geometry (triangular shape (also referred
to as a tent shape). FIGS. 15B-15D show perspective views of
graphical representations of three example modes for the object of
FIG. 15A. FIG. 15B shows a first mode of the object, FIG. 15C shows
a second mode of the object, and FIG. 15D shows a third mode of the
object. The arrows in each of the FIGS. 15B-15D indicate
directional forces associated with the respective modes. FIG. 16
shows a spectrum 1600 indicating associated normalized inverse
energies (S.sup.2) of 50 modes of a tent shape object (such as the
modes shown in FIGS. 15B-15D). Spectrum 1600 indicates that those
modes having higher normalized inverse energies (S.sup.2) are most
prominent in the elastic deformation simulation. In some
embodiments, those modes having normalized inverse energy (S.sub.2)
at or above a threshold (e.g., 10.sup.-1 (e.g., corresponding to 1%
of the maximum value)) may be considered prominent, while those
modes having normalized inverse energy (S.sub.2) below the
threshold (e.g., 10.sup.-1) may be considered non-prominent. In
some embodiments, the threshold corresponds to those modes having
associated energies of at least a predetermined energy of a maximum
inverse energy mode (e.g., corresponding to a minimum energy mode)
(e.g., mode 1).
[0253] FIGS. 22A-22G show perspective views of graphical
representations of seven example modes for a 3D object having a
disc cone shape (e.g., considering the geometric model of the
requested object shown in FIG. 19A (1900)), with the arrows
indicating directional forces. FIG. 23 shows a spectrum 2300
indicating associated normalized inverse energies (S.sup.2) of 50
modes of a disc cone shaped object (such as those shown in FIGS.
22A-22G), with modes having higher inverse energies (S.sup.2) being
most dominant in the elastic deformation simulation. In some
embodiments, those modes having normalized inverse energy (S.sub.2)
at or above a threshold may be considered prominent, while those
modes having normalized inverse energy (S.sub.2) below the
threshold may be considered non-prominent. In some embodiments, the
threshold corresponds to those modes having associated energies of
at least a predetermined energy of a maximum inverse energy mode
(e.g., corresponding to a minimum energy mode) (e.g., mode 1).
[0254] FIG. 24A shows a perspective view of a graphical
representation of a geometric model of a requested object having a
bridge shape. FIGS. 24B-24E show perspective views of graphical
representations of four example modes for a 3D object having a
bridge shape (e.g., considering the geometric model of the
requested object shown in FIG. 24A), with the arrows indicating
directional forces. FIG. 25 shows a spectrum 2500 indicating
associated normalized inverse energies (S.sup.2) of 50 modes of a
bridge shaped object (such as those shown in FIGS. 24B-24E), with
modes having higher inverse energies (S.sup.2) being most dominant
in the elastic deformation simulation. In some embodiments, those
modes having normalized inverse energy (S.sub.2) at or above a
threshold may be considered prominent, while those modes having
normalized inverse energy (S.sub.2) below the threshold (e.g.,
10.sup.-1) may be considered non-prominent. In some embodiments,
the threshold corresponds to those modes having associated energies
of at least a predetermined energy of a maximum inverse energy mode
(e.g., corresponding to a minimum energy mode) (e.g., mode 1).
[0255] FIG. 33A shows a geometric model (e.g., CAD drawing) of a
requested 3D object 3300 having a spiral blade shape. FIGS. 26A-26D
show perspective views of graphical representations of four example
modes for the 3D object having a spiral blade shape (e.g.,
considering the geometric model of the requested object shown in
FIG. 33A), with the arrows indicating directional forces. FIG. 27
shows a spectrum 2700 indicating associated normalized inverse
energies (S.sup.2) of 50 modes of a spiral blade shaped object
(such as those shown in FIGS. 26A-26D), with modes having higher
inverse energies (S.sup.2) being most dominant in the elastic
deformation simulation. In some embodiments, those modes having
normalized inverse energy (S.sub.2) at or above a threshold may be
considered prominent, while those modes having normalized inverse
energy (S.sub.2) below the threshold may be considered
non-prominent. In some embodiments, the threshold corresponds to
those modes having associated energies of at least a predetermined
energy of a maximum inverse energy mode (e.g., corresponding to a
minimum energy mode) (e.g., mode 1).
[0256] Energy data associated with each mode (e.g., as represented
in a spectrum) can be used to filter out those modes that are, for
example, less predominant. Example details regarding using modes as
a filtering technique are described herein, for example, with
reference to FIG. 18. In some cases, the filtering out of
particular modes can be confirmed or contradicted by empirically
collected data (e.g., from measurements of formed (e.g., printed)
objects). In some embodiments, those modes having normalized
inverse energy (S.sub.2) at or above a threshold may be considered
prominent, while those modes having normalized inverse energy
(S.sub.2) below the threshold may be considered non-prominent. In
some embodiments, the threshold corresponds to those modes having
associated energies of at least a predetermined energy of a maximum
inverse energy mode (e.g., corresponding to a minimum energy mode)
(e.g., mode 1). The threshold may be any threshold disclosed
herein.
[0257] A displacement calculation can be used to determine measured
displacements in each of the prominent modes. For example, a
measured displacement (u) of the marker locations (X) can be
calculated according to the following Equation 11:
u ( X ) = i = 0 N c i u ( X ) ; ##EQU00004##
[0258] where N is the number of modes (e.g., predominant modes);
c.sub.i is a coefficient determined using, for example, regression
analysis (e.g., least squares fit); and .sub.l(X) is a mode (e.g.,
predominant mode) shape with marker locations (X). The geometric
model can then be corrected to adjust for the calculated
displacement. For example, a geometric model can be adjusted by
applying a negative displacement to the geometric model. For
instance, the negative displacement can correspond to the
adjustment function (update function) g(x.sub.i) described above
with reference to Equation 1.
[0259] FIG. 14 shows flowchart 1400 indicating an example simulated
process for generating a corrected geometric model for forming an
object, in accordance with some embodiments. A geometric model of
the requested object (e.g., 1402) can be obtained, such as
described herein. The geometric model of the requested object can
be obtained using any suitable 3D modeling technique (e.g.,
suitable CAD and/or non-uniform rational basis spline (NURBS)). In
some embodiments, the geometric model of the requested object
corresponds to an image (e.g., scan) of an object (e.g., a test
object) and/or data obtained using any suitable rendering
technique. One or more physics models can be generated (e.g., 1404)
considering one or more physics-based calculations, e.g., as
disclosed herein. In some embodiments, the physics model considers
fewer physics-based calculations/simulations and/or degrees of
freedom (reduced physics model), e.g., as described herein. One or
more simulations can be performed (e.g., 1406). The one or more
simulations can consider the physics model and (e.g., applied to)
the geometric model of the requested object. A simulated object can
be formed (e.g., 1408) considering (e.g., based on) the one or more
simulations. The simulated object (e.g., various aspects of the
simulated object) can then be compared to the geometric model of
the requested part (e.g., various aspects of the geometric model of
the requested object) (e.g., 1410). In some embodiments, comparing
comprises: (i) comparing dimensions (e.g., shape) of the simulated
object with (ii) corresponding dimensions (e.g., shape) of the
geometric model of the requested object. In some embodiments,
comparing comprises determining an amount of predicted deformation
(e.g., warpage) by the simulated object. Comparing can comprise
determining whether data associated with the simulated object
(e.g., substantially) converges with data associated with the
geometric model of the requested object (e.g., 1412). For example,
it can be determined whether deformation of one or more dimensions
of the simulated object are below or above a predetermined
threshold (e.g., value or range). If it is determined that the
simulated object does not (e.g., substantially) converge with the
geometric model of the requested part, the geometric model (used
form forming a simulated object) can be adjusted (e.g., 1414). One
or more simulations can be performed (e.g., repeating 1406) with
the adjusted geometric model to generate another simulated object
(e.g., second geometric model of a simulated object) (e.g.,
repeating 1408); the simulated object can be compared to the
geometric model of the requested object (e.g., repeating 1410);
until convergence (e.g., repeating 1412). If it is determined that
the simulated object (e.g., substantially) converges with the
geometric model of the requested object (e.g., 1412), a corrected
geometric model can be generated (e.g., 1416) and a requested
object can be formed (e.g., 1418). In some embodiments, the
corrected geometric model corresponds to the simulated object. The
corrected geometric model (or simulated object) can be used to form
(e.g., print) a plurality requested objects (e.g., in a single
forming process).
[0260] As described herein, a geometric model of an object can be
(e.g., iteratively) improved by using a combination of empirically
collected data (from an empirical process (e.g., FIG. 13)) and
calculated data (from a simulated process (e.g., FIG. 14)). FIG. 17
shows flowchart 1700 indicating an example process based on a
combination of an empirical process and a simulated process, in
accordance with some embodiments. A geometric model of the
requested object (e.g., 1702) (e.g., FIG. 33A, 3300, or FIG. 19A,
1900) can be obtained, using methods such as described herein. Data
can be collected using an empirical process (e.g., 1704), e.g., as
described herein. In some embodiments, the empirical process
involves using markers (model markers (e.g., FIG. 19B, 1902) and/or
physical markers (e.g., FIG. 33C, 3322, or FIG. 20A, 2002). Data
can also be collected using a simulated process (e.g., 1706), e.g.,
as described herein. In some embodiments, the simulated process
involves using a physics model and performing physics based
simulation (e.g., thermo-plastic deformation simulation, elastic
deformation simulation). In some embodiments, the simulated process
involves using a physics model for calculating modes (e.g., FIGS.
15B-15D, FIGS. 22A-22G, FIGS. 24B-24E, or FIG. 26A-26D). The
empirical and simulated processes can be performed in parallel
(e.g., simultaneously or overlapping) or sequentially (e.g.,
empirical process first and simulated process second, or simulated
process first and empirical process second). Results of the
empirical and simulated processes can be compared (e.g., 1708). For
example, if the simulated process involves using a physics model
for calculating modes, the modes can be compared to test object
(e.g., image of the test object, e.g., FIG. 33B, 3310) (e.g.,
having image markers 3312), as described herein. If the physics
model and/or geometric model (i.e., from the empirical/simulated
process) are found to generate a test object having acceptable
dimensions and/or qualities (e.g., within a threshold range), the
physics model and/or geometric model can be considered a corrected
geometric model and be used to form the requested object (e.g.,
1712). If the last physics model and/or geometric model is found to
have unacceptable dimensions/qualities (e.g., outside of the
threshold range), the physical model and/or geometric model may be
adjusted, and a corrected geometric model and/or physics model can
optionally be generated (e.g., 1710) and used to form the requested
object (e.g., 1712) (e.g., FIG. 33C (photograph), 3320, prior to
removing markers 3322). When the corrected geometric model is
adjusted considering (e.g., based on) empirically and simulation
obtained data, the formed requested object may more accurately
correspond to the (e.g., geometric model of the) requested object
(e.g., at 1702), as compared to using only empirically obtained
data or only simulation obtained data.
[0261] In some embodiments, results from the empirical process can
optionally be used to inform the simulated process. For example, a
simulated object formed from the simulated process can be compared
with an image (e.g., 3D scan) of a test object formed (e.g.,
printed) using an empirical process. Data regarding differences in
the positions of markers (e.g., model markers of the simulated
object compared with image markers of the imaged test object) can
be used to determine the accuracy of a physics model. For instance,
in some embodiments, empirically collected marker data can used to
determine the prevalence (e.g., dominance, or relative weights) of
certain modes of a predicted elastic deformation physics model. The
physics model can then be adjusted (e.g., 1716) to more accurately
simulate the forming (e.g., printing) process. This process can be
utilized for a training the physics model. The training may be for
a particular forming process and/or for a particular forming system
(or configuration thereof). In some cases, The process of informing
the simulated process from results of the empirical process, may
reduce the number of adjustments to physics model. The simulated
process can then be repeated (e.g., repeating 1706) and used to
form another test object using an empirical process (e.g.,
repeating 1704). This process (e.g., 1716, 1706, 1704, 1708) can be
iteratively repeated and used to continually adjust (e.g., improve)
the physics model (e.g., 1716). This iterative process can be
referred to as a "learning module". The learning module can be used
to "teach" the physics model. The teaching may comprise an
inelastic response of the 3D object to the forming process, as
provided by the empirically collected data. The physics model can
be said to "learn" from, or be "trained by, the empirically
collected data. The learning (e.g., training) can occur with every
iteration (e.g., continuously). That is, in some embodiments, the
physics model can be adjusted with every iteration. The learning
(e.g., training) module can include learning (e.g., training)
algorithms as described herein, for example, "neural networks
and/or machine learning. The resulting adjusted (e.g., corrected)
physics model can be referred to as a "trained physics model" (also
referred to herein as an "educated physics model", "learned physics
model", "educated model", "learned model" or "trained model").
[0262] FIG. 30 shows flowchart 3000 indicating an example process
considering (e.g., based on) a combination of an empirical process
and a simulated process, in accordance with some embodiments. A
geometric model of the requested object (e.g., 3002) can be
obtained, such as described herein. One or more simulations can be
performed using one or more physics models (e.g., 3004). The one or
more physics models can consider (e.g., be based on) physics-based
calculations, as described herein. A simulated object can be formed
(e.g., 3006) from the one or more simulations, as described herein.
The simulated object can be compared to a test object (e.g., an
image of the test object) (e.g., 3008) formed using methods
described herein. In some cases, the comparison determines how
accurately the physics model represents deformation of a physical
object (e.g., test object) resulting from the forming process. In
some embodiments, the physics model is used to adjust the geometric
model used to form the test object. In some cases, the adjusted
geometric model can be used to generate a corrected geometric model
(e.g., 3010), which is used to form the requested object (e.g.,
3012). In some cases, the adjusted geometric model corresponds to
the corrected geometric model, and utilized in the forming of the
requested object (e.g., 3012), e.g., to generate forming
instructions (e.g., printing instructions). In some embodiments,
comparing the simulated object with a test object (e.g., image of
the test object) is used to adjust the physics model (e.g., 3014).
For example, one or more parameters of the physics model can be
adjusted by taking into account (e.g., based on) the comparing. The
comparing and adjustment processes (e.g., comprising 3004, 3006,
3008, or 3014) can be iteratively repeated, and can be referred to
as a learning module. For example, a (e.g., second) simulation can
be performed (e.g., repeating 3004) considering (e.g., based on) a
(e.g., second) physics model, a (e.g., second) simulated object can
be formed (e.g., repeating 3006), a (e.g., second) comparison can
be performed (e.g., repeating 3008), and a (e.g., second)
adjustment can be made to the (e.g., second) physics model (e.g.,
repeating 3014). The learning model process can be repeated to
iteratively adjust (e.g., improve) the physics model (e.g., to form
a trained physics model), and/or a corrected geometric model. In
some cases, the learning (e.g., training) process is used on some
test objects and not on other test objects. For example, in some
cases, a number of test objects are formed without being compared
to a simulated object, and therefore not used to adjust the physics
model.
[0263] FIG. 31 shows flowchart 3100 indicating an example process
considering (e.g., based on) a combination of an empirical process
and a simulated process, in accordance with some embodiments. A
physics model can be generated (e.g., 3102). The physics model can
consider (e.g., be based on) a predicted deformation of the
three-dimensional object as a result of a forming (e.g., printing)
operation (e.g., forming process). The predicted deformation can
consider a requested geometric model of the object. The predicted
deformation can consider at least one physics-based calculation.
The predicted deformation can consider a thermo-mechanical
analysis, a type of material of the three-dimensional object,
continuum mechanics (e.g., fluid dynamics), predicted inelastic
(e.g., plastic) deformation, predicted elastic deformation,
predicted thermally induced deformation, predicted thermo-plastic
deformation, at least one characteristic of an energy beam (e.g.,
as disclosed herein), pressure (e.g., gradient or lack thereof) of
multiple layers of the three-dimensional object, heat conductance
(e.g., in the previously formed portion of the 3D object), or any
suitable combination thereof. The at least one characteristic of
the energy beam may comprise, for example, a type of energy beam,
an energy beam power density, an energy beam path, or an energy
beam dwell time. Other energy beam characteristics are disclosed
herein. The physics model can be a reduced physics model or an
expanded physics model. In some embodiments, the physics model
considers (e.g., comprises) one or more modes. The one or more
modes can consider predicted elastic deformation modes. A simulated
object can be formed (e.g., printed) based on the physics model
(e.g., 3104). The physics model can be adjusted considering a
comparison of the simulated object with a test object (e.g., 3106).
The comparison can be a comparison of the simulated object with an
image of the test object. The test object can correspond to an
object formed using any suitable process. The test object can be
formed using instructions considering the geometric model and/or
physical model. The comparison can consider comparing at least one
predicted deformation of the simulated object with at least one
deformation of the test object. The image of the test object can
include image markers corresponding to physical markers of the test
object. The comparison can include a comparison of the one or more
markers of the test object (e.g., by converting the physical test
object to a model test object) with the one or more markers that
are incorporated in and/or on the simulated object (e.g., that is
used to print the test object). The comparison can include a
comparison of one or more (identifiable) features of the test
object (e.g., edges, rims, cavities, and/or kinks) with respective
one or more features that of the simulated object (e.g., that is
used to print the test object). The comparison can include a
comparison of the one or more markers of the test object (e.g., by
converting the physical test object to a model test object) with
the one or more markers that are incorporated in and/or on the
geometric model (e.g., that is used to print the test object). The
comparison can include a comparison of one or more (identifiable)
features of the test object (e.g., edges, rims, cavities, and/or
kinks) with respective one or more features that of the geometric
model (e.g., that is used to print the test object). The geometric
model may be a model (e.g., virtual representation) of the
requested object. The comparison can include a comparison of at
least one deformation of the simulated object with at least one
deformation of the test object (e.g., image of the test object).
The comparison can be obtained by performing a data analysis. The
comparison can be obtained by performing a regression analysis
(e.g., least squares fit analysis). Generating the physics model
(e.g., 3102), forming the simulated object (e.g., 3104) and
adjusting the physics model (e.g., 3106) can be iteratively
repeated (e.g., creating a learning module). For example,
generating the physics model (e.g., 3102), forming the simulated
object (e.g., 3104) and adjusting the physics model (e.g., 3106)
can be iteratively repeated until a simulated object has dimensions
within a predetermined threshold of dimensions of the test object
(e.g., image of the test object). In some cases, at least one of
(i) generating the physics model (e.g., 3102), (ii) forming the
simulated object (e.g., 3104) and (iii) adjusting the physics model
(e.g., 3106), can be iteratively repeated (e.g., forming the
learning module) and/or can occur during a forming (e.g., printing)
operation.
[0264] The learning module can optionally be used to adjust the
physics model (e.g., and/or the physics simulation, and/or the
geometric model) over any suitable time scale. For example, in some
embodiments, the learning module is used to adjust the geometric
model over a period of forming (e.g., sequentially or in parallel)
multiple objects (e.g., test objects or requested objects). The
geometric model can be adjusted after forming any suitable number
of objects (e.g., 2, 5, 10, 50, 100, 500, 1000, 10,000, 1,000,000,
etc.). At least two of the multiple objects can be formed
sequentially. At least two of the multiple objects can be formed in
parallel. The physics model can be adjusted after forming any
number of objects between any of the afore-mentioned values (e.g.,
from about 2 to about 1,000,000, from about 2 to about 100, etc.).
In some embodiments, the learning module is used to adjust the
physics model (and/or geometric model) over a period of time (e.g.,
at least a second, minute, day, week, month, year, or a decade,).
In some embodiments, the learning module is used to adjust the
physics model (or any component thereof, and/or geometric model)
over a lifetime of the forming system (e.g., 3D printing system).
In some embodiments, the learning module is used to adjust the
physics model (or any component thereof, and/or associated
corrected geometric model) over a period of a forming operation
(e.g., in real time). In some embodiments, adjusting in real time
comprises adjusting the physics model (or any component thereof,
and/or associated corrected geometric model) during the forming of
a single layer (or multiple layers). In some embodiments, the
learning module is used as a basis to adjust one or more process
parameters of the forming process, such as at least one
characteristic of the energy beam, e.g., as described herein.
[0265] In some embodiments, the learning module is used to
calibrate one or more systems for forming objects. The calibration
can be of one or more (i) hardware component, (ii) software
component, (iii) forming procedure, or (iv) any combination
thereof. For example, the learning module can be used to: (1)
identify system mismatches and/or errors and accordingly adjust one
or more components of the system (e.g., comprising hardware or
software); (2) identify system mismatches and/or errors and adjust
the physics model to compensate accordingly for mismatches and/or
errors in one or more components of the system; and/or (3)
identify, adjust and/or account for differences in forming
processes (e.g., comprising a 3D printing, molding, or welding
process). System mismatches and/or errors can occur when different
systems of one type (e.g., different 3D printers) have different
built-in offsets. The build-in offsets may result in objects having
different dimensions when using, for example, the same forming
(e.g., printing) instructions (e.g., based on the same geometric
model). For example, a first forming system (e.g., first 3D
printer) using a first geometric model can be used to form a first
object, and a second system (e.g., second 3D printer) using the
first geometric model can be used to form a second object having
dissimilar dimensions as the first object. In (1), the learning
module can be used to diagnose problems related to hardware and/or
software of the first and/or second system, such that the hardware
and/or software can be adjusted or replaced, as applicable. In (2),
the learning module can be used to generate an adjusted physics
model that compensates for the offsets and/or errors (e.g., in the
second object) and provide (i) better consistency between objects
and/or (ii) better consistency between the formed object and the
requested object. The learning module can be used to as an
adjustment mechanism, instead of or in addition to, changing
hardware and/or software of a forming (e.g., printing) system.
Differences due to different forming processes can arise when, for
example, one type of forming system (e.g., a forming system of a
first category, of a first brand, or of a first manufacturing
batch) is used to form a first object, and another type of forming
system (e.g., a forming system of a second category, of a second
brand, or of a second manufacturing batch) is used to form a second
object having the dissimilar dimensions as compared to the first
object (when similar dimensions are desired). The forming system
category (e.g., type) can comprise (for example) molding, welding,
a type of 3D printing process (e.g., LENS, SLM, SLS, FDM, LOM, or
SLA), or a semiconductor device fabrication process (e.g., chemical
vapor deposition, or physical vapor deposition). In (3), the
learning module can be used to guide adjustment of the forming
processes, and provides objects having the (e.g., substantially)
satisfactory dimensions. In (3), the learning module can be used to
generate an adjusted physics model that accounts for different
types of forming processes, and provides objects having the (e.g.,
substantially) satisfactory dimensions. For instance, the geometric
model can be iteratively adjusted to generate a first adjusted
physics model (corresponding to a first set of forming
instructions) for the first forming system that considers empirical
data collected over a plurality of formed objects from the first
system. The same physics model can be iteratively adjusted to
generate a second adjusted geometric model (corresponding to a
second set of forming instructions) for the second forming system
that considers empirical data collected over multiple objects
formed from the second system. The physics model may be unique to a
forming system category, a forming system brand, a forming system
manufacturing batch, a forming process, a singular forming system,
or any suitable combination thereof. The physical model may be
utilized to differentiate between a forming system category, of a
forming system brand, a forming system manufacturing batch, a
forming process, between singular forming systems, or any suitable
combination thereof. The physical model may be utilized to identify
and/or calibrate a faulty forming process, a faulty forming
systems, or any combination thereof.
[0266] The processes described herein (e.g., the empirical process
and/or the simulated process) may be utilized to differentiate
between a forming system category, a forming system brand, a
forming system manufacturing batch, a forming process, between
singular forming systems, or any suitable combination thereof. The
modules described herein may be utilized to identify and/or
calibrate a faulty forming process, a faulty forming systems, or
any suitable combination thereof.
[0267] In some embodiments, the calibrating comprises using a first
system to form a first 3D object using printing instructions (e.g.,
based on a physics model, on the empirical process, and/or on the
simulated process), using a second system to form a second 3D
object using the printing instructions, comparing dimensions of the
first 3D object with the second 3D object, and (based on the
comparing): adjusting the (a) physics model, (b) geometric model
(c) hardware of the second system, (d) software of the second
system, (e) process of used for the forming process of the second
system, or (f) any combination thereof. The first system may
comprise an optimized and/or adequate system. The first 3D object
may be (e.g., substantially) similar to a requested object.
[0268] As described herein, a reduced physics model (e.g., having
reduced degrees of freedom and/or reduced number of physics-based
calculations) can be used to create a filter bank. In some
embodiments, this technique involves using a reduced physics model
in combination with empirically collected data (e.g., that can be
used as part of the empirical process) to filter out data (e.g.,
form a filter bank). For example, the FIG. 18 shows flowchart 1800
indicating an example process (including a filtering process), in
accordance with some embodiments. A geometric model of the
requested object (e.g., 1802) can be obtained, such as described
herein. A physics model (e.g., reduced physics model) can be used
to compute modes (e.g., elastic deformation modes) (e.g., 1802).
The physics model can consider a geometric model of a requested
object, as described herein. The physics model can be used to
perform a simulation considering (e.g., based on) the geometric
model. Prominent modes can be identified (e.g., 1806). This can be
done using, for example, a spectrum analysis described herein
(e.g., with reference to FIGS. 15, 23, 25 and 27). In some
embodiments, the modes having associated energies below a threshold
value (e.g., predetermined threshold value) may be considered more
energetically favorable (which can also referred to as prominent),
while those modes having energies above the threshold may be
considered less energetically favorable (which can also referred to
as non-prominent). In some embodiment, the threshold can correspond
to a percentage (e.g., predetermined percentage) of energy. For
example, returning to the spectrum 1600 of FIG. 16, in some
embodiments, those modes having normalized inverse energy (S.sub.2)
at or above a threshold (e.g., 10.sup.-1 of the normalized inverse
energy, corresponding to 1% of the normalized inverse energy) may
be considered prominent, while those modes having normalized
inverse energy (S.sub.2) below the threshold may be considered
non-prominent. In some embodiments, the threshold corresponds to
those modes having at least about 0.5%, 1%, 2%, 3%, 4%, 5%, 10%,
15%, 20%, 30%, 40%, or 50% of a maximum normalized inverse energy
of the modes. In some embodiments, the threshold corresponds to
those modes having percentage of inverse energies between any of
the afore-mentioned values (e.g., from about 0.5% to about 50%,
from about 5% to about 30%, from about 1% to about 5%, from about
0.5% to about 5%, or from about 0.5% to about 10% of a maximum
normalized inverse energy of the modes).
[0269] The prominent modes can be compared with a test object
(e.g., image of a test object) (e.g., 1808). In some embodiments,
the comparison comprises determining (e.g., computing) one or more
characteristics of the test object (e.g., image of the test object)
considering (e.g., based on) the prominent modes. The test object
can be formed using the empirical processes described herein. An
image of the test object can be obtained by sensing (e.g.,
scanning) the test object. The image can include markers (image
markers) corresponding to physical makers of the test object.
Locations in/on the prominent modes corresponding to the locations
of the image markers in/on the image can be identified. The
comparison between the prominent modes and the test object can
include comparing locations of the identified locations of the
prominent modes, with locations of the image markers of the image
of the test object. In some embodiments, a mathematical combination
of prominent modes is used to provide displacement values
associated with the markers (image markers). The mathematical
combination may comprise a linear, exponential, or analytical
geometric combination. The mathematical combination may comprise
linear approximation. The analytical geometric combination may
comprise sine, cosine, or logarithmic combination. The image of the
test object can be determined to have characteristics of prominent
modes at different weights (also referred to as "coefficients").
For example, in one embodiments, a test object may have 40%
dimensional characteristics of a first mode (e.g., FIG. 15A), 30%
dimensional characteristics of a second mode (e.g., FIG. 15B), and
30% dimensional characteristics of a third mode (e.g., FIG. 15C).
Results of the comparison can be used to estimate dimensional
accuracy of the physics model and the simulation. In some
embodiments, the results of the comparison can be used to adjust
the physics model (e.g., 1810), as described herein. The process
(e.g., 1800) can be iteratively repeated during the formation of
multiple objects (e.g., test objects and/or requested objects). In
some embodiments, identified prominent modes (e.g., 1806) are
reduce the number of iterations in which the physics model (e.g.,
1810) is being adjusted. For example, identifying prominent modes
can filter out less prominent modes (which can be referred to as
"noise"). Identifying the prominent modes (e.g., 1806) can be
referred to as a filter bank (e.g., database of most prominent
modes).
[0270] FIG. 28 shows flowchart 2800 indicating an example process
for generating instructions for forming an object, in accordance
with some embodiments. One or more prominent modes of the object
can be identified (e.g., 2802) considering (e.g., based on) a
geometric model of the object. In some embodiments, the one or more
prominent modes can be identified (e.g., 2804) during a forming
operation (e.g., in real time and/or in situ). The geometric model
can be generated using any suitable method (e.g., CAD rendered,
imaging of a test object, etc.). The one or more prominent modes
can correspond to one or more thermo-mechanical prominent modes.
The one or more prominent modes can be chosen from a number of
modes (e.g., thermo-mechanical modes), each having an associated
energy. The modes can be organized considering (e.g., based on)
their associated energies (e.g., spectrum of energies). In some
embodiments, the prominent modes correspond to modes that achieve
thermo-mechanical equilibrium within a predetermined threshold. In
some embodiments, the prominent modes correspond to modes having an
associated energy within a predetermined threshold. In some
embodiments, the modes (and prominent modes) correspond to
predicted mechanical deformation of the object. In some
embodiments, the modes (and prominent modes) correspond to
predicted elastic deformation (e.g., nonlinear elastic deformation)
of the object. In some embodiments, the modes (and prominent modes)
correspond to predicted inelastic deformation (e.g., plastic
deformation) of the object. In some embodiments, the one or more
prominent modes can constitute a filter bank. The one or more
prominent modes can be compared to the object (or geometric model
of the object) (e.g., 2804). Comparing can include performing a
regression analysis, e.g., as described herein. In some
embodiments, comparing the one or more prominent modes with the
object (or geometric model of the object) (e.g., 2804) is done
during a forming operation (e.g., in real time and/or in situ). The
geometric model can be adjusted considering (e.g., based on) the
comparing (e.g. using suitable techniques described herein).
Forming instructions can then be generated (e.g., 2806) considering
(e.g., based on) the comparing. In some embodiments, the forming
instructions can be generated (e.g., 2806) during a forming
operation (e.g., in real time and/or in situ).
[0271] FIG. 29 shows flowchart 2900 indicating an example process
for generating instructions for forming an object, in accordance
with some embodiments. A physics model can be generated (e.g.,
2902). The physics model can consider (e.g., be based on) a
predicted deformation of the object as a result of a forming
operation (e.g., process). The physics model can be a reduced
physics model. The physics model can be an expanded physics model.
The estimated (e.g., predicted) deformation can consider (e.g., be
based on) a thermo-mechanical analysis, a type of material of the
3D object, at least one characteristic of the energy beam, or any
suitable combination thereof. The thermo-mechanical analysis may
comprise continuum mechanics (e.g., fluid dynamics), mechanical
deformation (e.g., inelastic (e.g., plastic) deformation, and/or
elastic deformation), estimated thermal deformation, estimated
thermo-mechanical deformation, or pressure and/or temperature
(e.g., gradient) along the multiple layers of the 3D object (e.g.,
previously formed layers). The estimated deformation can consider a
predicted change of at least one characteristic of the 3D object.
The predicted deformation can consider at least one physics-based
calculation. The physics model can be compared with a formed (e.g.,
printed) object (e.g., 2904). The formed object can correspond to a
test object or a requested object. In some cases, the physics model
is compared with an image of the object (e.g., as a proxy for the
object). The image can be generated by scanning the object using
any suitable technique, e.g., as described herein. The image can
include image markers corresponding to physical markers of the
object. The object and/or image can include at least one
deformation as a result of a forming operation. The comparing
operation can include comparing the predicted deformation with the
at least one deformation. Comparing the physics model with the
object (and/or image of the object) can include performing a data
analysis. The data analysis can include a regression analysis,
e.g., as described herein. Generating a physics model (e.g., 2902)
and comparing the physics model with a formed object (or image of
the object) (e.g., 2904) can be iteratively repeated. In some
cases, the iterative repetition is until an adjusted geometric
model has dimensions within a predetermined threshold range of
dimensions of a geometric model (of the requested object).
Instructions for forming the object can be generated (e.g., 2906).
Generating the instructions can include generating a corrected
geometric model (e.g., OPC). In some cases, at least one of
generating the physics model (e.g., 2902), comparing the physics
model with a formed object (and/or image of an object) (e.g.,
2904), and generating forming instructions (e.g., 2906) occur
during a forming operation (e.g., in real time and/or in situ). In
some cases, at least two of generating the physics model (e.g.,
2902), comparing the physics model with a formed object (and/or
image of an object) (e.g., 2904), and generating forming
instructions (e.g., 2906) occur during a forming operation (e.g.,
in real time and/or in situ). In some cases, generating the physics
model (e.g., 2902), comparing the physics model with a formed
object (and/or image of an object) (e.g., 2904), and generating
forming instructions (e.g., 2906) occur during a forming operation
(e.g., in real time and/or in situ).
[0272] In some embodiments, the process of forming an object
applies external forces to the object. These external forces result
in stress (e.g., due to the object's internal resisting forces) and
strain in the object. The strain of an object can be measured, for
example, using a strain gage. Strain can be defined as an amount of
deformation per unit length of an object when a load is applied.
The strain .epsilon. can be defined as variance (due to
deformation) of an original length by the original length (L),
according to Equation 12:
Strain(.epsilon.)=(.DELTA.L)/L
[0273] Strain .epsilon. in accordance with Equation 12 can be
referred to as a total strain .epsilon..sub.total, and include both
inelastic strain .epsilon..sub.inelastic and elastic strain
.epsilon..sub.elastic. Note that inelastic strain
.epsilon..sub.inelastic can include any type of inelastic strain.
For example, in some embodiments, the inelastic strain
.epsilon..sub.inelastic comprises plastic strain, viscoplastic
strain, creep, or inelastic thermal strain. The plastic strain may
comprise small strain plasticity, finite strain plasticity, or
plasticity with hardening. The viscoplastic strain may be in
accordance with Norton-Hoff model, Bingham-Norton, Perzyna models,
Johnson-Cook flow stress models, Steinberg-Cochran-Guinan-Lund
(SCGL) flow stress models, Zerilli-Armstrong flow stress models,
Preston-Tonks-Wallace flow stress models, or any suitable
combination thereof. The creep may comprise Nabarro-Herring creep,
Coble creep, Harper-Dorn creep, or solute drag creep. Methods
described herein can be used to determine the total strain
.epsilon..sub.total and inelastic strain .epsilon..sub.inelastic,
which can in turn, be used to determine elastic strain
.epsilon..sub.elastic, on an object as a result of a forming (e.g.,
printing) operation. For example, a formed object may have
deformations compared to a geometric model of the requested object.
That is, the forming instructions (e.g., printing instructions) can
consider a geometric model of the requested object (e.g., having a
requested geometry), which can result in a deformed object (e.g.,
have deformed geometry). The empirical methods described herein can
be used to determine the inelastic (e.g., plastic) strain
.epsilon..sub.inelastic on the deformed object induced by the
forming process (e.g., the inelastic response). The simulated
process described herein can include elastic and/or inelastic
strain/stress (e.g., nonlinear elastic strain/stress). The
simulated process can be used to estimate the total strain
.epsilon..sub.total (e.g., using the mode analysis described
herein) and the inelastic strain .epsilon..sub.inelastic of the
deformed object, which are induced by the forming process. This can
then be used to deduce the elastic strain .epsilon..sub.elastic on
the deformed object. The stress can be determined from the
calculated strain.
[0274] FIG. 32 shows flowchart 3200 indicating an example process
for generating instructions for forming (e.g., printing) an object
(including optionally determining strain on the object), in
accordance with some embodiments. An object can be formed using
instructions considering a geometric model of the requested object.
An image (e.g., 3D scan) of the formed object can be obtained
(e.g., 3202), e.g., as described herein. The formed object may be
deformed (relative to the geometric model of the requested part) as
a result of the forming process. Modes of the object can be
computed by considering (also) the geometric model of the requested
part (e.g., 3204). The modes can be computed using any suitable
data analysis techniques, e.g., as described herein. For example,
the modes can be determined using calculations comprising: singular
value decomposition (SVD), Kosambi-Karhunen-Loeve transform (KLT),
the Hotelling transform, proper orthogonal decomposition (POD),
eigenvalue decomposition (EVD), factor analysis, Eckart-Young
theorem, Schmidt-Mirsky theorem, empirical orthogonal functions
(EOF), empirical eigenfunction decomposition, empirical component
analysis, quasiharmonic modes, spectral decomposition, or empirical
modal analysis. In some embodiments, the modes correspond to
mechanical (e.g., elastic and/or inelastic) deformation modes, as
described herein. For calculating the modes, non-linear elastic
simulation may be performed. The non-linear elastic simulation can
consider the plastic strain field imposed on the object (e.g.,
during its formation). From the modes, prominent modes can
optionally be identified (e.g., 3206). The prominent modes can be
identified using methods described herein. The image of the formed
object can be compared with the modes (e.g., prominent modes)
(e.g., 3208). The comparing operation can be performed using any
suitable method, e.g., as described herein. In some embodiments,
the comparing is used to calculating inelastic (e.g., plastic)
strain on the formed object (e.g., 3210) induced by the forming
process(es). The strain on the object can be determined independent
of the forming process for the object. The strain can be induced by
suitable process(es) (e.g., one or more printing processes, molding
processes, and/or machining processes). In some embodiments, the
strain is calculated by determining one or more deformations
variances (.DELTA.L) and one or more corresponding original lengths
(L), e.g., using methods described herein. In some embodiments, the
strain is calculated using the comparison of the image of the
formed object and the modes (e.g., prominent modes). In some
embodiments, the calculating operation includes performing a data
analysis. The data analysis can include a regression analysis,
e.g., as described herein (e.g., a least squares fit analysis). One
or more markers may be constructed on the modes. In some
embodiments, image markers (e.g., locations, consistency, and/or
shape of the image markers) of the formed object are compared to
model markers (e.g., locations of the model markers) of the
geometric model or of the modes (e.g., prominent modes). In some
embodiments, the formed object can be considered not (e.g.,
substantially) deformed, if its calculated strain is at or below a
predetermined strain threshold. In some embodiments, the strain
threshold is at most about 0.001, 0.005, 0.008, 0.01, 0.05, 0.08,
0.1, 0.5, 0.8, or 0.9. In some embodiments, the strain threshold
has any value between the afore-mentioned values (e.g., from about
0.001 to about 0.9, from about 0.001 to about 0.1, from about 0.005
to about 0.08, from about 0.005 to about 0.05, or from about 0.1 to
about 0.9). A corrected geometric model can be generated (e.g.,
3212) using, e.g., methods described herein. In some embodiments,
the corrected geometric model corresponds to the geometric model of
the requested object, iteratively adjusted geometric model, physics
model, or iteratively adjusted physics model. The corrected
geometric model can be used to form the last formed object. The
requested object can be formed using the corrected geometric model
(e.g., 3214). In some embodiments, the corrected geometric model is
used to form the object, e.g., if the strain is at least (or
between) the strain threshold(s).
[0275] Methods described herein can be used to form objects with
increased dimensional accuracy. The dimensional accuracy can be
compared to dimensions (e.g., desired dimensions) of a requested
object (e.g., as requested by a customer). In some embodiments, the
dimensional accuracy can be to within a predetermined dimensional
value. In some embodiments, the dimensional accuracy is determined
using statistical analysis (e.g., regression analysis), matrix
analysis (e.g., distance matrix), and/or other suitable
mathematical analyses, e.g., such as described herein. In some
embodiments, the dimensional accuracy considers (e.g., is based on)
a surface quality of an object. For example, the predetermined
dimensional value may correspond to a surface roughness. The
surface roughness can be associated with a surface reflectance
(e.g., shininess). The surface roughness may be measured as the
arithmetic average of the roughness profile (hereinafter "Ra"). The
3D object can have a Ra value of at most about 300 .mu.m, 200
.mu.m, 100 .mu.m, 75 .mu.m, 50 .mu.m, 45 .mu.m, 40 .mu.m, 35 .mu.m,
30 .mu.m, 25 .mu.m, 20 .mu.m, 15 .mu.m, 10 .mu.m, 7 .mu.m, 5 .mu.m,
3 .mu.m, 1 .mu.m, 500 nm, 400 nm, 300 nm, 200 nm, 100 nm, 50 nm, 40
nm, or 30 nm. The 3D object can have a Ra value between any of the
afore-mentioned Ra values (e.g., from about 300 .mu.m to about 50
.mu.m, from about 50 .mu.m to about 5 .mu.m, from about 300 nm to
about 30 nm, or from about 30 .mu.m to about 3 nm). The Ra values
may be measured by a roughness tester and/or by a microscopy method
(e.g., any microscopy method described herein). The measurements
may be conducted at ambient temperatures (e.g., R.T.), melting
point temperature (e.g., of the pre-transformed material) or
cryogenic temperatures. The roughness (e.g., Ra value) may be
measured by a contact or by a non-contact method. The roughness
measurement apparatus may comprise one or more sensors (e.g.,
optical sensors). The roughness measurement may include using a
metrological measurement device (e.g., using metrological
sensor(s)). The roughness may be measured using an electromagnetic
beam (e.g., visible or IR).
Example 1--Printing an Object Using Markers
[0276] A CAD drawing (an example of a geometric model) of a
requested object was obtained (e.g., FIG. 19A, 1900). Markers
(model markers) (e.g., FIG. 19B, 1902) were added to specified
locations on the surface of the CAD drawing. A deformed object was
printed using a 3D printing system and instructions that consider
(e.g., based on) dimensions of the CAD drawing. The deformed object
had markers (physical markers) corresponding to the model markers
on the CAD drawing. An image of the deformed object was obtained by
scanning the deformed object using a 3D scanner. The image had
markers (image markers) corresponding to the physical markers of
the deformed object and the model markers of the CAD drawing. A
least squares fit analysis was performed to determine deviations
between locations of the image markers of the image of the test
object with locations of the model markers of the CAD drawing. The
CAD drawing (geometric model) was corrected to adjust for the
deviations using a distance matrices and displacement vector
calculations. The corrected geometric model was used to generate
print instructions. The print instructions were used to form the
requested Inconel 3D object having a desired geometry. FIG. 20A,
2000 shows a request 3D object made of Inconel according to Example
6, which markers incorporated, and FIG. 20B shows its corresponding
scanned image.
Example 2--Printing an Object Using Mode Analysis
[0277] A CAD drawing (an example of a geometric model) (e.g., FIG.
33A, 3300) of a requested object was obtained. Markers (model
markers) were added to specified locations on the surface of the
CAD drawing. An Inconel test object was printed (e.g., FIG. 33),
according to Example 6, using a 3D printing system and instructions
that consider (e.g., based on) dimensions of the CAD drawing. The
test object was scanned to generate an image (e.g., FIG. 33B, 3310)
having image markers (e.g., FIG. 33B, 3310). An SVD calculation was
used to compute modes (geometric models of simulated objects)
considering (e.g., based on) predicted elastic deformations
(physics model) of the geometric model (e.g., FIG. 26A-26D). A
spectrum of normalized inverse energy (S.sub.2) of the modes (e.g.,
FIG. 27) was generated. Prominent modes (e.g., 4 prominent modes)
were identified as having normalized inverse energy (S.sub.2) at or
above a certain threshold percentage (%) of a maximum inverse
energy mode (e.g., minimum energy mode). A least squares fit
analysis (using distance matrices) was performed to determine
geometric deviations between the prominent modes (determined using
simulated process) and the image of the deformed object (determined
using empirical process) were above a predetermined threshold. A
displacement calculation was used to determine measured
displacements in each of the prominent modes. The CAD drawing
(geometric model) was corrected to adjust for the calculated
displacement. The corrected geometric model was used to generate
print instructions. The print instructions were used to print the
requested object having a requested geometry according to the
procedure delineated in Example 6. FIG. 33C, 3320 shows the request
3D object made of Inconel with markers incorporated, and FIG. 33B
shows its corresponding scanned image.
Example 3--Printing an Object Using a Learning Module
[0278] A first image of a first deformed object having image
markers was obtained, as described in Example 2. A first set of
prominent modes (e.g., 4 prominent modes) of predicted elastic
deformations (physics model) were identified, as described in
Example 2. A least squares fit analysis (using distance matrix) was
performed to determine geometric deviations between the first set
of prominent modes (determined using simulated process) and the
first image of the deformed object (determined using empirical
process) were above a predetermined threshold. A displacement
calculation was used to determine measured displacements in each of
the first set of prominent modes. The CAD drawing (geometric model)
was first corrected to adjust for the calculated displacement, and
a second deformed object was printed, according to Example 6,
considering (e.g., based on) the first corrected CAD drawing. The
physics model was adjusted to account for the calculated
displacement. The adjusted physics model was used as a basis for
generating a second set of prominent modes. A least squares fit
analysis (using distance matrix) was performed to determine
geometric deviations between the second set of prominent modes and
a second image of the second deformed object are above the
predetermined threshold. Subsequent (e.g., third, fourth and fifth)
deformed objects and sets of prominent modes were iteratively
generated and compared until geometric deviations between a final
set of prominent modes and a final image of a final deformed object
were determined to be within the predetermined threshold. The CAD
drawing was corrected to adjust for the last calculated
displacement and used to generate print instructions. The print
instructions were used to print the requested object having a
desired geometry.
Example 4--Calibrating a Printer Using a Learning Module
[0279] A first geometric model (e.g., corrected geometric model)
can be determined to print objects with desired geometries using a
first printing system. The first geometric model can be used to
print deformed objects (e.g., do not have geometric dimensions
within a predetermined threshold) using a second printing system.
Deformed objects and sets of prominent modes can be iteratively
generated and compared until geometric deviations between a final
set of prominent modes and a final image of a final deformed object
are determined to be within the predetermined threshold. The CAD
drawing can be corrected to adjust for the last calculated
displacement and used to generate print instructions. The print
instructions can be used by the second printing system to print one
or more requested objects having a desired geometry.
Example 5--Determining Strain on an Object
[0280] A deformed object was printed using a system according to
Example 6. Geometric deviations between an image of the deformed
object and prominent modes (generated using a simulated process)
were determined, as described in Example 2. Strain (.epsilon.) on
the deformed object from the forming process was calculated,
including the total strain and the inelastic (e.g., plastic)
strain. From this the elastic strain was deduced. The type of
forming process (e.g., printing, molding, machining, etc.) was not
necessary to determine the strain (.epsilon.).
Example 6--3D Printing and Scanning
[0281] In a 28 cm by 28 cm by 30 cm container at ambient
temperature and pressure, Inconel 718 powder of average particle
size 35 .mu.m was deposited in a container to form a powder bed.
The container was disposed in an enclosure to separate the powder
bed from the ambient environment. The enclosure was purged with
Argon gas for 30 minutes. A 500 W fiber laser beam was used to melt
a portion of the powder bed. The resulting 3D object was scanned
using COMET L3D compact 3D sensor scanner, manufactured by Carl
Zeiss Optotechnik GmbH, Germany.
[0282] While preferred embodiments of the present invention have
been shown and described herein, it will be obvious to those
skilled in the art that such embodiments are provided by way of
example only. It is not intended that the invention be limited by
the specific examples provided within the specification. While the
invention has been described with reference to the aforementioned
specification, the descriptions and illustrations of the
embodiments herein are not meant to be construed in a limiting
sense. Numerous variations, changes, and substitutions will now
occur to those skilled in the art without departing from the
invention. Furthermore, it shall be understood that all aspects of
the invention are not limited to the specific depictions,
configurations, or relative proportions set forth herein which
depend upon a variety of conditions and variables. It should be
understood that various alternatives to the embodiments of the
invention described herein might be employed in practicing the
invention. It is therefore contemplated that the invention shall
also cover any such alternatives, modifications, variations, or
equivalents. It is intended that the following claims define the
scope of the invention and that methods and structures within the
scope of these claims and their equivalents be covered thereby.
* * * * *