U.S. patent application number 17/176557 was filed with the patent office on 2021-06-24 for predicting process control parameters for fabricating an object using deposition.
The applicant listed for this patent is RELATIVITY SPACE, INC.. Invention is credited to TIMOTHY A. ELLIS, EDWARD MEHR, JORDAN NOONE.
Application Number | 20210191363 17/176557 |
Document ID | / |
Family ID | 1000005447946 |
Filed Date | 2021-06-24 |
United States Patent
Application |
20210191363 |
Kind Code |
A1 |
MEHR; EDWARD ; et
al. |
June 24, 2021 |
PREDICTING PROCESS CONTROL PARAMETERS FOR FABRICATING AN OBJECT
USING DEPOSITION
Abstract
Process control parameters are predicted to fabricate an object
using deposition. An input design geometry is provided for the
object. A training data set includes past post-build physical
inspection data for a plurality of objects that comprise at least
one object that is different from the object to be physically
fabricated; and training data generated through a repetitive
process of randomly choosing values for each of multiple process
control parameters and scoring adjustments to the multiple process
control parameters as leading to either undesirable or desirable
outcomes, the outcomes based respectively on the presence or
absence of defects detected in a fabricated object arising from the
process control parameter adjustments. A machine learning algorithm
is trained using the provided training data set and a predicted
optimal set of the multiple process control parameters is generated
for initiating and performing the deposition process to fabricate
the object.
Inventors: |
MEHR; EDWARD; (Santa Moncia,
CA) ; ELLIS; TIMOTHY A.; (Inglewood, CA) ;
NOONE; JORDAN; (Inglewood, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
RELATIVITY SPACE, INC. |
INGLEWOOD |
CA |
US |
|
|
Family ID: |
1000005447946 |
Appl. No.: |
17/176557 |
Filed: |
February 16, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16696720 |
Nov 26, 2019 |
10921782 |
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17176557 |
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16234325 |
Dec 27, 2018 |
10539952 |
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16696720 |
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15604473 |
May 24, 2017 |
10234848 |
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16234325 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B22F 10/00 20210101;
G06N 7/005 20130101; G05B 2219/49011 20130101; G06N 20/10 20190101;
B22F 10/20 20210101; B22F 10/30 20210101; G06N 3/084 20130101; G06N
3/08 20130101; B33Y 50/02 20141201; G05B 2219/49018 20130101; G06N
20/00 20190101; G05B 19/4099 20130101; G05B 2219/49017 20130101;
G06N 7/02 20130101; G06N 5/003 20130101; G05B 2219/45165 20130101;
G05B 2219/35134 20130101; B22F 10/10 20210101; G06N 3/0454
20130101; G05B 2219/49023 20130101 |
International
Class: |
G05B 19/4099 20060101
G05B019/4099; G06N 20/00 20060101 G06N020/00; G06N 3/04 20060101
G06N003/04; G06N 20/10 20060101 G06N020/10; B33Y 50/02 20060101
B33Y050/02; B22F 10/20 20060101 B22F010/20; B22F 10/00 20060101
B22F010/00; G06N 3/08 20060101 G06N003/08; G06N 7/02 20060101
G06N007/02 |
Claims
1. A method for generating a predicted optimal set of deposition
process control parameters to fabricate an object having an input
design geometry, the method comprising: providing an input design
geometry for an object to be physically fabricated using the
deposition process; providing a training data set that comprises:
past post-build physical inspection data for a plurality of objects
that comprise at least one object that is different from the object
to be physically fabricated; and training data generated through a
repetitive process of randomly choosing values for each of multiple
process control parameters and scoring adjustments to the multiple
process control parameters as leading to either undesirable or
desirable outcomes, the outcomes based respectively on the presence
or absence of defects detected in a fabricated object arising from
the process control parameter adjustments; training a machine
learning algorithm using the provided training data set; and
generating a predicted optimal set of the multiple process control
parameters for initiating and performing the deposition process to
fabricate the object, wherein the predicted optimal set of the
multiple process control parameters are derived using the trained
machine learning algorithm.
2. The method of claim 1, wherein training the machine learning
algorithm further comprises randomly choosing values within a
specified range of a process window, generating process simulation
data with a tool to simulate a fabrication process, using the
randomly chosen values and incorporating the generated process
simulation data, into the training data set to improve a learned
model that maps process control parameter values to process
outcomes.
3. The method of claim 1, wherein providing the training data set
further comprises providing process simulation data, process
characterization data, or in-process inspection data for a
plurality of design geometries or portions thereof.
4. The method of claim 1, wherein providing the training data set
further comprises providing process characterization data,
in-process inspection data, or past post-build physical inspection
data that is generated by a skilled operator while manually
adjusting one or more of the multiple the process control
parameters.
5. The method of claim 1, further comprising: receiving data for
multiple object properties from each of multiple sensors as the
object is being physically fabricated, wherein providing the
training data set further comprises providing the data, and wherein
generating a predicted optimal set of the multiple process control
parameters comprises adjusting one or more of the multiple process
control parameters as the object is being physically
fabricated.
6. The method of claim 5, further comprising removing noise from
the data prior to providing the data.
7. The method of claim 5, wherein the data comprises acoustic
energy or mechanical energy that is reflected, scattered, absorbed,
transmitted, or emitted by the object.
8. The method of claim 1, wherein the machine learning algorithm
comprises an artificial neural network.
9. The method of claim 1, wherein the defects detected in the
fabricated object are detected as differences between object
property data and a reference data set that are larger than a
specified threshold, and are classified using a one-class support
vector machine and a training data set that comprises object
property data for defective and defect-free objects.
10. The method of claim 1, further comprising providing
instructions to an apparatus to perform a free form deposition
process to fabricate the object, wherein generating a predicted
optimal set of the multiple process control parameters further
comprises adjusting the one or more process control parameters
while the apparatus is physically performing the free form
deposition process.
11. A machine-readable storage medium having instructions therein
which when executed by the machine cause the machine to perform
operations comprising: receiving an input design geometry for an
object to be physically fabricated by a deposition process using a
predicted optimal set of deposition process control parameters;
receiving a training data set that comprises: past post-build
physical inspection data for a plurality of objects that comprise
at least one object that is different from the object to be
physically fabricated; and training data generated through a
repetitive process of randomly choosing values for each of multiple
process control parameters and scoring adjustments to the multiple
process control parameters as leading to either undesirable or
desirable outcomes, the outcomes based respectively on the presence
or absence of defects detected in a fabricated object arising from
the process control parameter adjustments; training a machine
learning algorithm using the provided training data set; and
generating a predicted optimal set of the multiple process control
parameters for initiating and performing the deposition process to
fabricate the object, wherein the predicted optimal set of the
multiple process control parameters are derived using the trained
machine learning algorithm.
12. The machine-readable storage medium of claim 11, wherein
training the machine learning algorithm further comprises randomly
choosing values within a specified range of a process window,
generating process simulation data to simulate a fabrication
process, using the randomly chosen values and incorporating the
generated process simulation data, into the training data set to
improve a learned model that maps process control parameter values
to process outcomes.
13. The machine-readable storage medium of claim 12, wherein
generating process simulation data comprises generating process
simulation data using a finite element analysis.
14. A system for controlling a deposition process, the system
comprising: a deposition apparatus configured to fabricate an
object using a deposition process and based on an input design
geometry; one or more sensors configured to characterize the
deposition process of the deposition apparatus, wherein the one or
more sensors provide data for one or more process parameters or
object properties; and a processor programmed to: provide a
predicted optimal set of one or more input process control
parameters to control parameters of the deposition process during
fabrication of the object, wherein the predicted optimal set of the
one or more input process control parameters are derived using a
machine learning algorithm that has been trained using a training
data set; receive the data from the one or more sensors as input to
the machine learning algorithm; detect defects in the object during
fabrication of the object using the data from the one or more
sensors; classify the detected object defects in real time using
the machine learning algorithm; provide the classification of
detected object defects as input to the machine learning algorithm;
adjust one or more input process control parameters based on the
classification of the detected object defects, wherein the
adjustments are derived using the machine learning algorithm; and
provide instructions to the deposition apparatus to adjust the one
or more input process control parameters during fabrication of the
object.
15. The system of claim 14, wherein the processor is further
programmed to perform iteratively and incorporate the detected
object defect classifications for each iteration into the training
data set.
16. The system of claim 14, implemented as a distributed, modular
system comprising a first deposition apparatus, a first sensor, and
a first processor, wherein the first deposition apparatus, the
first sensor, and the first processor are configured to share
training data and process characterization data with a second
processor via a network.
17. The system of claim 15, wherein the one or more sensors
comprise a laser interferometer.
18. The system of claim 15, wherein the object defects are detected
as differences between object property data and a reference data
set that are larger than a specified threshold, and are classified
using an autoencoder algorithm.
19. The system of claim 15, wherein the training data set further
comprises process characterization data, in-process inspection
data, or post-build inspection data that is generated by an
operator while manually adjusting the one or more process control
parameters.
20. A method comprising: providing a predicted optimal set of one
or more input process control parameters to control parameters of a
deposition fabrication process during fabrication of an object,
wherein the predicted optimal set of the one or more input process
control parameters are derived using a machine learning algorithm
that has been trained using a training data set; receiving data
characterizing the deposition process using properties of the
object being fabricated from one or more sensors as input to the
machine learning algorithm; detecting defects in the object during
fabrication of the object using the data from the one or more
sensors; classifying the detected object defects in real time using
the machine learning algorithm; providing the classification of
detected object defects as input to the machine learning algorithm;
adjusting one or more input process control parameters based on the
real-time classification of the detected object defects, wherein
the adjustments are derived using the machine learning algorithm;
and providing instructions to adjust the one or more input process
control parameters during fabrication of the object.
21. The method of claim 20, further comprising: providing a
training data set that comprises: past post-build physical
inspection data for a plurality of objects that comprise at least
one object that is different from the object to be physically
fabricated; and training data generated through a repetitive
process of randomly choosing values for each of multiple process
control parameters and scoring adjustments to the multiple process
control parameters as leading to either undesirable or desirable
outcomes, the outcomes based respectively on the presence or
absence of defects detected in a fabricated object arising from the
process control parameter adjustments; and training the machine
learning algorithm using the provided training data set.
22. A method for adaptive control of a free form deposition
process, the method comprising: providing an input design geometry
for an object to be physically fabricated using the free form
deposition process; providing a training data set that comprises:
process characterization data for a plurality of objects that
comprise at least one object that is different from the object to
be physically fabricated; and training data generated through a
repetitive process of randomly choosing values for each of multiple
process control parameters and scoring adjustments to the multiple
process control parameters as leading to either undesirable or
desirable outcomes, the outcomes based respectively on the presence
or absence of defects detected in a fabricated object arising from
the process control parameter adjustments; providing a predicted
optimal set of the multiple process control parameters for
initiating the free form deposition process, wherein the predicted
optimal set of the multiple process control parameters are derived
using a machine learning algorithm that has been trained using the
training data set; providing a real-time classification of detected
object defects using the machine learning algorithm that has been
trained using the training data, wherein real-time data from
multiple sensors is provided as input to the machine learning
algorithm, and wherein the real-time classification of detected
object defects is output from the machine learning algorithm; and
providing instructions to perform the free form deposition process
to fabricate the object, wherein the machine learning algorithm
adjusts the multiple process control parameters while physically
performing the free form deposition process.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of application Ser. No.
16/696,720, filed on Nov. 26, 2019, which is a continuation of
application Ser. No. 16/234,325, filed on Dec. 27, 2018, now Pat.
No. 10,539,952, which is a continuation of application Ser. No.
15/604,473, filed on May 24, 2017, now Pat. No. 10,234,848.
BACKGROUND OF THE INVENTION
[0002] Additive manufacturing processes are fabrication techniques
that allow one to produce functional complex parts layer by layer,
without the use of molds or dies. Despite recent advances in the
methods and apparatus used for various types of additive
manufacturing, a need exists for methods that allow rapid
optimization and adjustment of the process control parameters used
in response to changes in process or environmental parameters, as
well as for improving the quality of the parts that are produced.
Methods and systems are disclosed for performing automated
classification of object defects using machine learning algorithms.
Also disclosed are methods and systems for performing real-time
adaptive control of free form deposition or joining processes,
including additive manufacturing or welding processes, to improve
process yield, throughput, and quality.
SUMMARY
[0003] Disclosed herein are methods for real-time adaptive control
of a free form deposition process or a joining process, the methods
comprising: a) providing an input design geometry for an object; b)
providing a training data set, wherein the training data set
comprises process simulation data, process characterization data,
in-process inspection data, post-build inspection data, or any
combination thereof, for a plurality of design geometries or
portions thereof that are the same as or different from the input
design geometry of step (a); c) providing a predicted optimal set
or sequence of one or more process control parameters for
fabricating the object, wherein the predicted optimal set of one or
more process control parameters are derived using a machine
learning algorithm that has been trained using the training data
set of step (b); and d) performing the free form deposition process
or the joining process to fabricate the object, wherein real-time
process characterization data is provided as input to the machine
learning algorithm to adjust one or more process control parameters
in real-time.
[0004] In some embodiments, steps (b)-(d) are performed iteratively
and process characterization data, in-process inspection data,
post-build inspection data, or any combination thereof for each
iteration is incorporated into the training data set. In some
embodiments, the free form deposition process or joining process is
a stereolithography (SLA), digital light processing (DLP), fused
deposition modeling (FDM), selective laser sintering (SLS),
selective laser melting (SLM), or electronic beam melting (EBM), or
welding process. In some embodiments, the free form deposition
process is a liquid-to-solid free form deposition process. In some
embodiments, the liquid-to-solid free form deposition process is a
laser metal-wire deposition process. In some embodiments, the
process simulation data is provided by performing finite element
analysis (FEA), finite volume analysis (FVA), finite difference
analysis (FDA), computational fluid dynamics (CFD) calculations, or
any combination thereof. In some embodiments, the one or more
process control parameters to be predicted or controlled comprise a
rate of material deposition, a rate of displacement for a
deposition apparatus, a rate of acceleration for a deposition
apparatus, a direction of displacement for a deposition apparatus,
a location of a deposition apparatus as a function of time (a tool
path), an angle of a deposition apparatus with respect to a
deposition direction, an angle of overhang in an intended geometry,
an intensity of heat flux into a material during deposition, a size
and shape of a heat flux surface, a flow rate and angle of
shielding gas flow, a temperature of a baseplate, an ambient
temperature control during a deposition process, a temperature of a
deposition material prior to deposition, a current or voltage
setting in a resistive heating apparatus, a voltage frequency or
amplitude in an inductive heating apparatus, a choice of deposition
material, a ratio by volume or a ratio by weight of deposition
materials if more than one deposition material is used, or any
combination thereof. In some embodiments, the process simulation
data comprises a prediction of a bulk or peak temperature of a
deposited material, a cooling rate of a deposited material, a
chemical composition of a deposited material, a segregation state
of constituents in a deposited material, a geometrical property of
a deposited material, an intensity of heat flux out of a material
during deposition, an electromagnetic emission from a deposition
material, an acoustic emission from a deposition material, or any
combination thereof, as a function of a set of specified input
process control parameters. In some embodiments, the process
characterization data comprises a measurement of a bulk or peak
temperature of a deposited material, a cooling rate of a deposited
material, a chemical composition of a deposited material, a
segregation state of constituents in a deposited material, a
geometrical property of a deposited material, a rate of material
deposition, a rate of displacement for a deposition apparatus, a
location (tool path) of a deposition apparatus, an angle of a
deposition apparatus with respect to a deposition direction, a
deposition apparatus status indicator, an angle of overhang in a
deposited geometry, an angle of overhang in an intended geometry,
an intensity of heat flux into a material during deposition, an
intensity of heat flux out of a material during deposition, an
electromagnetic emission from a deposition material, an acoustic
emission from a deposition material, an electrical conductivity of
a deposition material, a thermal conductivity of a deposition
material, a defect in the geometry of an object being fabricated,
or any combination thereof. In some embodiments, the in-process or
post-build inspection data comprises data from a visual or machine
vision-based inspection of surface finish, a visual or machine
vision-based inspection of surface cracks and pores, a test of a
mechanical property such as strength, hardness, ductility, fatigue,
a test of a chemical property such as composition, segregation of
constituent materials, a defect characterization methodology such
as X-ray diffraction or imaging, CT scanning, ultrasonic imaging,
Eddy current sensor array measurements, or thermography, or any
combination thereof. In some embodiments, the machine learning
algorithm comprises a supervised learning algorithm, an
unsupervised learning algorithm, a semi-supervised learning
algorithm, a reinforcement learning algorithm, a deep learning
algorithm, or any combination thereof. In some embodiments, the
machine learning algorithm comprises an artificial neural network
algorithm, a Gaussian process regression algorithm, a logistical
model tree algorithm, a random forest algorithm, a fuzzy classifier
algorithm, a decision tree algorithm, a hierarchical clustering
algorithm, a k-means algorithm, a fuzzy clustering algorithm, a
deep Boltzmann machine learning algorithm, a deep convolutional
neural network algorithm, a deep recurrent neural network, or any
combination thereof. In some embodiments, the machine learning
algorithm comprises an artificial neural network. In some
embodiments, the artificial neural network comprises an input
layer, an output layer, and at least 1 hidden layer. In some
embodiments, the artificial neural network comprises an input
layer, an output layer, and at least 5 hidden layers. In some
embodiments, the artificial neural network comprises an input
layer, an output layer, and at least 10 hidden layers. In some
embodiments, the number of nodes in the input layer is at least 10.
In some embodiments, the number of nodes in the input layer is at
least 100. In some embodiments, the number of nodes in the input
layer is at least 1,000. In some embodiments, at least one stream
of process characterization data is provided to the machine
learning algorithm at a rate of at least 10 Hz. In some
embodiments, at least one stream of process characterization data
is provided to the machine learning algorithm at a rate of at least
100 Hz. In some embodiments, at least one stream of process
characterization data is provided to the machine learning algorithm
at a rate of at least 1,000 Hz. In some embodiments, the one or
more process control parameters are adjusted at a rate of at least
10 Hz. In some embodiments, the one or more process control
parameters are adjusted at a rate of at least 100 Hz. In some
embodiments, the one or more process control parameters are
adjusted at a rate of at least 1,000 Hz. In some embodiments, the
method is implemented using a single integrated system comprising a
deposition apparatus, a sensor, and a processor. In some
embodiments, the method is implemented using a distributed, modular
system comprising a first deposition apparatus, a first sensor, and
a first processor, wherein the first deposition apparatus, the
first sensor, and the first processor are configured to share
training data and/or real-time process characterization data via a
local area network (LAN), an intranet, an extranet, or an internet.
In some embodiments, the training data set resides in the internet
cloud. In some embodiments, the sharing of data between the first
deposition apparatus, the first sensor, and the first processor is
facilitated by use of a data compression algorithm, a data feature
extraction algorithm, or a data dimensionality reduction algorithm.
In some embodiments, the training data set is shared between and
updated using data from a plurality of deposition apparatus and
sensors that are configured to share data via a local area network
(LAN), an intranet, an extranet, or an internet. In some
embodiments, the training data set further comprises process
characterization data, in-process inspection data, post-build
inspection data, or any combination thereof, that is generated by a
skilled operator while manually adjusting the input process control
parameters. In some embodiments, as part of the training of the
machine learning algorithm, the machine learning algorithm randomly
chooses values within a specified range for each of a set of one or
more process control parameters, and incorporates the resulting
process simulation data, process characterization data, in-process
inspection data, post-build inspection data, or any combination
thereof, into the training data set to improve a learned model that
maps process control parameter values to process outcomes.
[0005] Also disclosed herein are systems for controlling a free
form deposition process or a joining process, the systems
comprising: a) a first deposition apparatus, wherein the deposition
apparatus is capable of fabricating an object based on an input
design geometry; b) one or more process characterization sensors,
wherein the one or more process characterization sensors provide
real-time data for one or more process parameters or object
properties; and c) a processor programmed to (i) provide a
predicted optimal set of one or more input process control
parameters, and (ii) to adjust one or more process control
parameters in real-time based on a stream of real-time process
characterization data provided by the one or more process
characterization sensors, wherein the predictions and adjustments
are derived using a machine learning algorithm that has been
trained using a training data set.
[0006] In some embodiments, the system further comprises a computer
memory device within which machine learning algorithm software,
sensor data from the one or more process characterization sensors,
predicted or adjusted values of one or more process control
parameters, the training data set, or any combination thereof, is
stored. In some embodiments, the first deposition apparatus, the
one or more process characterization sensors, and the processor are
incorporated into a single integrated system. In some embodiments,
the first deposition apparatus, the one or more process
characterization sensors, and the processor are configured as
distributed system modules that share training data and/or
real-time process characterization data via a local area network
(LAN), an intranet, an extranet, or an internet. In some
embodiments, the training data set resides in the internet cloud,
and is shared between and updated using data from a plurality of
deposition apparatus and sensors that are configured to share data
via a local area network (LAN), an intranet, an extranet, or an
internet. In some embodiments, the training data set comprises
process simulation data, process characterization data, in-process
inspection data, post-build inspection data, or any combination
thereof, for a plurality of objects that are the same as or
different from the object of step (a). In some embodiments, the one
or more process characterization sensors comprise temperature
sensors, position sensors, motion sensors, touch/proximity sensors,
accelerometers, profilometers, goniometers, image sensors and
machine vision systems, electrical conductivity sensors, thermal
conductivity sensors, strain gauges, durometers, X-ray diffraction
or imaging devices, CT scanning devices, ultrasonic imaging
devices, Eddy current sensor arrays, thermographs, deposition
apparatus status indicators, or any combination thereof. In some
embodiments, the one or more process characterization sensors
comprise at least one laser interferometer, machine vision system,
or sensor that detects electromagnetic radiation that is reflected,
scattered, absorbed, transmitted, or emitted by the object. In some
embodiments, the machine vision system is configured as a visible
light-based system used for measurement of object dimensions. In
some embodiments, the machine vision system is configured as a
visible light-based system used for measurement of object surface
finish. In some embodiments, the machine vision system is
configured as an infrared-based system used for measurement of
object temperature or heat flux within the object. In some
embodiments, the machine vision system is configured as an X-ray
diffraction-based system used for measurement of object material
properties. In some embodiments, the one or more process control
parameters to be predicted or adjusted comprise a rate of material
deposition, a rate of displacement for a deposition apparatus, a
rate of acceleration for a deposition apparatus, a direction of
displacement for a deposition apparatus, an angle of a deposition
apparatus with respect to a deposition direction, an intensity of
heat flux into a material during deposition, a size and shape of a
heat flux surface, a flow rate and angle of shielding gas flow, a
temperature of a deposition apparatus, an ambient temperature
control during a deposition process, a temperature of a deposition
material prior to deposition, a current or voltage setting in a
resistive heating apparatus, a voltage frequency or amplitude in an
inductive heating apparatus, a choice of deposition material, a
ratio by volume or a ratio by weight of deposition materials if
more than one deposition material is used, or any combination
thereof. In some embodiments, the machine learning algorithm
comprises a supervised learning algorithm, an unsupervised learning
algorithm, a semi-supervised learning algorithm, a reinforcement
learning algorithm, a deep learning algorithm, or any combination
thereof. In some embodiments, the machine learning algorithm
comprises an artificial neural network. In some embodiments, the
artificial neural network comprises an input layer, an output
layer, and at least 5 hidden layers. In some embodiments, the
number of nodes in the input layer is at least 100. In some
embodiments, at least one stream of real-time process
characterization data is provided to the machine learning algorithm
at a rate of at least 100 Hz. In some embodiments, the one or more
process control parameters are adjusted at a rate of at least 100
Hz.
[0007] Disclosed herein are methods for automated classification of
object defects, the methods comprising: a) providing a training
data set, wherein the training data set comprises fabrication
process simulation data, fabrication process characterization data,
in-process inspection data, post-build inspection data, or any
combination thereof, for a plurality of design geometries that are
the same as or different from that of the object; b) providing one
or more sensors, wherein the one or more sensors provide real-time
data for one or more object properties; c) providing a processor
programmed to provide a classification of detected object defects
using a machine learning algorithm that has been trained using the
training data set of step (a), wherein the real-time data from the
one or more sensors is provided as input to the machine learning
algorithm and allows the classification of detected object defects
to be adjusted in real-time.
[0008] In some embodiments, the method further comprises removing
noise from the object property data provided by the one or more
sensors prior to providing it to the machine learning algorithm. In
some embodiments, noise is removed from the object property data
using a signal averaging algorithm, smoothing filter algorithm,
Kalman filter algorithm, nonlinear filter algorithm, total
variation minimization algorithm, or any combination thereof. In
some embodiments, the one or more sensors provide data on
electromagnetic radiation that is reflected, scattered, absorbed,
transmitted, or emitted by the object. In some embodiments, the one
or more sensors comprise image sensors or machine vision systems.
In some embodiments, the electromagnetic radiation is ultraviolet,
visible, or infrared light. In some embodiments, the one or more
sensors provide data on acoustic energy or mechanical energy that
is reflected, scattered, absorbed, transmitted, or emitted by the
object. In some embodiments, subtraction of a reference data set is
used to increase contrast between normal and defective features of
the object. In some embodiments, the one or more sensors provide
data on an electrical conductivity or a thermal conductivity of the
object. In some embodiments, the machine learning algorithm
comprises a supervised learning algorithm, an unsupervised learning
algorithm, a semi-supervised learning algorithm, a reinforcement
learning algorithm, a deep learning algorithm, or any combination
thereof. In some embodiments, at least one of the one or more
sensors provide data as input to the machine learning algorithm at
a rate of at least 100 Hz. In some embodiments, the classification
of detected object defects is adjusted at a rate of at least 100
Hz. In some embodiments, the object defects that are detected are
classified using a support vector machine (SVM), artificial neural
network (ANN), or decision tree-based expert learning system. In
some embodiments, the object defects are detected as differences
between object property data and a reference data set that are
larger than a specified threshold, and are classified using a
one-class support vector machine (SVM) or autoencoder algorithm. In
some embodiments, the object defects are detected and classified
using an unsupervised one-class support vector machine (SVM),
autoencoder, clustering, or nearest neighbor (kNN) machine learning
algorithm and a training data set that comprises object property
data for defective and defect-free objects.
[0009] Disclosed herein are methods for real-time adaptive control
of a free form deposition process or a joining process, the methods
comprising: a) providing an input design geometry for an object; b)
providing a training data set, wherein the training data set
comprises process simulation data, process characterization data,
in-process inspection data, post-build inspection data, or any
combination thereof, for a plurality of design geometries or
portions thereof that are the same as or different from the input
design geometry of step (a); c) providing a set or sequence of one
or more process control parameters for initiating the free form
deposition process or joining process to fabricate the object; and
d) performing the free form deposition process or the joining
process to fabricate the object, wherein real-time process
characterization data is provided as input to a machine learning
algorithm that has been trained using the training data set of step
(b) to adjust the one or more process control parameters in
real-time. In some embodiments, the predicted optimal set or
sequence of one or more process control parameters for initiating
the free form deposition process or the joining process is also
derived using the machine learning algorithm.
INCORPORATION BY REFERENCE
[0010] All publications, patents, and patent applications mentioned
in this specification are herein incorporated by reference in their
entirety to the same extent as if each individual publication,
patent, or patent application was specifically and individually
indicated to be incorporated by reference in its entirety. In the
event of a conflict between a term herein and a term in an
incorporated reference, the term herein controls.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] 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 of which:
[0012] FIG. 1 provides a schematic illustration of a machine
learning-based system for providing real-time adaptive control of
free form deposition processes, e.g., additive manufacturing
processes.
[0013] FIG. 2 is a schematic diagram of an example set-up for a
material deposition process, e.g., a laser-metal wire deposition
process, according to some embodiments of the present
disclosure.
[0014] FIGS. 3A-C provide schematic illustrations of the conversion
of a CAD design for a three-dimensional object to a continuous,
spiral wound "two-dimensional" layer (of finite thickness) and
associated helical tool path (FIG. 3A), or a stacked series of
"two-dimensional" layers and associated circular, layer-by-layer
tool paths (FIG. 3B) for deposition of material using an additive
manufacturing process. FIG. 3C: illustration of the tool path for a
robotically manipulated deposition tool and simulation of the
resulting object fabricated using an additive manufacturing
process.
[0015] FIGS. 4A-C provide examples of FEA simulation data for
modeling of a laser-metal wire deposition melt pool. FIG. 4A:
isometric view of color-encoded three-dimensional FEA simulation
data for the liquid fraction of material in the melt pool being
deposited by a laser-metal wire deposition process. FIG. 4B:
cross-sectional view of the FEA simulation data for the liquid
fraction of material in the melt pool. FIG. 4C: cross-sectional
view of color-encoded three-dimensional FEA simulation data for the
static temperature of the material in the melt pool.
[0016] FIG. 5 is a diagram of one non-limiting example of a
specific type of additive manufacturing system, i.e., a laser-metal
wire deposition system.
[0017] FIGS. 6A-B illustrate one non-limiting example of in-process
feature monitoring using interferometry. FIG. 6A: schematic
illustration of laser beams used to probe the geometry of the wire
feed and melt pool overlaid with a photo of a laser-metal wire
deposition process. FIG. 6B: cross-sectional profiles (i.e., height
profiles across the width of the deposition) of the wire feed
(solid line; peak) and previously deposited layer (solid line;
shoulders) and resulting melt pool (dashed line). The x-axis
(width) dimension is plotted in arbitrary units. The y-axis
(height) dimension is plotted in units of millimeters relative to a
fixed reference point below the deposition layer.
[0018] FIGS. 7A-C illustrate one non-limiting example of in-process
feature extraction from images of a laser-metal wire deposition
process obtained using a machine vision system. FIG. 7A: raw image
stream obtained from machine vision system. FIG. 7B: processed
image after de-noising, filtering, and edge detection algorithms
have been applied. FIG. 7C: processed image after application of a
feature extraction algorithm.
[0019] FIG. 8 illustrates an action prediction--reward loop for a
reinforcement learning algorithm according to some embodiments of
the present disclosure.
[0020] FIG. 9 illustrates reward function construction based on
monitoring the actions that a human operator chooses during a
manually-controlled deposition process.
[0021] FIG. 10 provides a schematic illustration of an artificial
neural network according to some embodiments of the present
disclosure, and examples of the input(s) and output(s) of a neural
network used to provide real-time, adaptive control of an additive
manufacturing deposition process.
[0022] FIG. 11 provides a schematic illustration of the
functionality of a node within a layer of an artificial neural
network.
[0023] FIG. 12 provides a schematic illustration of an integrated
system comprising an additive manufacturing deposition apparatus,
machine vision systems and/or other process monitoring tools,
process simulation tools, post-build inspection tools, and a
processor for running a machine learning algorithm that utilizes
data from the machine vision and/or process monitoring tools, the
process simulation tools, the post-build inspection tools, or any
combination thereof, to provide real-time adaptive control of the
deposition process.
[0024] FIG. 13 provides a schematic illustration of a distributed
system comprising an additive manufacturing deposition apparatus,
machine vision systems and/or other process monitoring tools,
process simulation tools, post-build inspection tools, and a
processor for running a machine learning algorithm that utilizes
data from the machine vision and/or process monitoring tools, the
process simulation tools, the post-build inspection tools, or any
combination thereof, to provide real-time adaptive control of the
deposition process. In some embodiments, the different components
or modules of the system may be physically located in different
workspaces and/or worksites, and may be linked via a local area
network (LAN), an intranet, an extranet, or the internet so that
process data (e.g., training data, process simulation data, process
control data, and post-build inspection data) and process control
instructions may be shared and exchanged between the different
modules.
[0025] FIG. 14 illustrates one non-limiting example of an
unsupervised feature extraction and data compression process.
[0026] FIG. 15 illustrates the expected outcome for one
non-limiting example of an unsupervised machine learning process
for classification of object defects.
[0027] FIGS. 16A-C provide an example of post-process image feature
extraction and correlation with build-time actions. FIG. 16A: image
of part after build process has been completed. FIG. 16B:
post-build inspection output (CT scan). FIG. 16C: the CT scan image
of
[0028] FIG. 16B after automated feature extraction; automated
feature extraction allows one to correlate part features with
build-time actions.
DETAILED DESCRIPTION
[0029] Disclosed herein are methods for automated classification of
object defects, for example, for objects fabricated using an
additive manufacturing process or welding process, where the
methods comprise: a) providing a training data set, wherein the
training data set comprises fabrication process simulation data,
fabrication process characterization data, in-process inspection
data, post-build inspection data, or any combination thereof, for a
plurality of object design geometries that are the same as or
different from the object; b) providing one or more sensors,
wherein the one or more sensors provide real-time data for one or
more object properties; c) providing a processor programmed to
provide a classification of detected object defects using a machine
learning algorithm that has been trained using the training data
set of step (a), wherein the real-time data from the one or more
sensors is provided as input to the machine learning algorithm and
allows the classification of detected object defects to be adjusted
in real-time. Also disclosed are systems designed to perform
automated classification of object defects.
[0030] Disclosed herein are methods for real-time adaptive control
of an additive manufacturing or welding process comprising: a)
providing an input design geometry for an object; b) providing a
training data set, wherein the training data set comprises process
simulation data, process characterization data, in-process
inspection data, post-build inspection data, or any combination
thereof, for a plurality of design geometries that are the same as
or different from the input design geometry of step (a) or any
portion thereof; c) providing a predicted optimal set/sequence of
one or more process control parameters for fabricating the object,
wherein the predicted optimal set of one or more process control
parameters are derived using a machine learning algorithm that has
been trained using the training data set of step (b); and d)
performing the additive manufacturing or welding process to
fabricate the object, wherein real-time process characterization
data is provided as input to the machine learning algorithm to
adjust one or more process control parameters in real-time. Also
disclosed are systems designed to implement these methods, as
illustrated schematically in FIG. 1. As indicated in FIG. 1, in
some embodiments, the disclosed methods for adaptive, real-time
control of additive manufacturing or welding processes may be
implemented using a distributed system, e.g., where different
components or modules of the system are physically located in
different workspaces, at different work sites, or in different
geographical locations, and process simulation data, process
characterization data, in-process inspection data, post-build
inspection data, and/or adaptive process control instructions are
shared and exchanged between locations by means of a
telecommunications network or the internet.
[0031] As used herein, the terms "deposition process" and "free
form deposition process" may refer to any of a variety of
liquid-to-solid free form deposition processes, solid-to-solid free
form deposition processes, additive manufacturing processes,
welding processes, and the like. In some embodiments, the disclosed
methods and systems may be applied to any of a variety of additive
manufacturing processes, including, but not limited to, fused
deposition modeling (FDM), selective laser sintering (SLS), or
selective laser melting (SLM), as will be described in more detail
below. In some preferred embodiments, the additive manufacturing
process may comprise a liquid-to-solid free form deposition
process, e.g., a laser-metal wire deposition process, or a welding
process, e.g., a laser welding process.
[0032] In some embodiments, process simulation data may be
incorporated into the training data set used by the machine
learning algorithm that enables automated classification of object
defects, prediction of optimal sets or sequences of process control
parameters, adjustment of process control parameters in real-time,
or any combination thereof. For example, process simulation tools
such as finite element analysis (FEA) may be used to simulate the
process for fabricating an object or a specific portion thereof,
e.g., a feature, from any of a variety of fabrication materials as
a function of a specified set of process control parameters. In
some embodiments, process simulation tools may be used to predict
an optimal set or sequence of process control parameters for
fabricating a specified object or object feature.
[0033] In some embodiments, process characterization data may be
incorporated into the training data set used by the machine
learning algorithm that enables automated classification of object
defects, prediction of optimal sets or sequences of process control
parameters, adjustment of process control parameters in real-time,
or any combination thereof. For example, process characterization
data may be provided by any of a variety of sensors or machine
vision systems, as will be described in more detail below. In some
embodiments, process characterization data may be fed to the
machine learning algorithm in order to update the process control
parameters of an additive manufacturing apparatus in real-time.
[0034] In some embodiments, in-process or post-build inspection
data may be incorporated into the training data set used by the
machine learning algorithm that enables automated classification of
object defects, prediction of optimal sets or sequences of process
control parameters, adjustment of process control parameters in
real-time, or any combination thereof. For example, in-process or
post-build inspection data may include data from visual or machine
vision-based measurements of object dimensions, surface finish,
number of surface cracks or pores, etc., as will be described in
more detail below. In some embodiments, in-process inspection data
(e.g., automated defect classification data) may be used by the
machine learning algorithm to determine a set or sequence of
process control parameter adjustments that will implement a
corrective action, e.g., to adjust a layer dimension or thickness,
so as to correct the defect when first detected. In some
embodiments, in-process inspection data (e.g., automated defect
classification data) may be used by the machine learning algorithm
to send a warning or error signal to an operator, or optionally, to
automatically abort the deposition process, e.g., an additive
manufacturing process.
[0035] In some embodiments, the training data set is updated with
additional process simulation data, process characterization data,
in-process inspection data, post-build inspection data, or any
combination thereof, after each iteration of an additive
manufacturing process that is performed iteratively. In some
embodiments, the training data set further comprises process
characterization data, in-process inspection data, post-build
inspection data, or any combination thereof, that is generated by a
skilled operator while manually setting the input process control
parameters for an additive manufacturing process to produce a
specified set of objects or parts, or while manually adjusting the
process control parameters in response to changes in process
parameters or environmental variables to maintain a specified
quality of the objects or parts being produced. In some
embodiments, the training data set may comprise process simulation
data, process characterization data, in-process inspection data,
post-build inspection data, or any combination thereof that is
collected from a plurality of additive manufacturing apparatus
operating serially or in parallel.
[0036] A variety of different machine learning algorithms known to
those of skill in the art may be employed to implement the
disclosed methods for automated object defect classification and
adaptive control of additive manufacturing or welding processes.
Examples include, but are not limited to, artificial neural network
algorithms, Gaussian process regression algorithms, fuzzy
logic-based algorithms, decision tree algorithms, etc., as will be
described in more detail below. In some embodiments, more than one
machine learning algorithm may be employed. For example, automated
classification of object defects may be implemented using one type
of machine learning algorithm, and adaptive real-time process
control may be implemented using a different type of machine
learning algorithm. In some embodiments, hybrid machine learning
algorithms that comprise features and properties drawn from two,
three, four, five, or more different types of machine learning
algorithms may be employed to implement the disclosed methods and
systems.
[0037] In some embodiments, the disclosed methods for automated
classification of object defects and adaptive real-time control may
be implemented using components, e.g., additive manufacturing
and/or welding apparatus, process control monitors or sensors,
machine vision systems, and/or post-build inspection tools, which
are co-localized in a specific workspace and which have been
integrated to form stand-alone, self-contained systems. In some
embodiments, the disclosed methods may be implemented using modular
components, e.g., additive manufacturing and/or welding apparatus,
process control monitors or sensors, machine vision systems, and/or
post-build inspection tools, that are distributed over different
workspaces and/or different worksites, and that are linked via a
local area network (LAN), an intranet, an extranet, or the internet
so that process data (e.g., training data, process simulation data,
process control data, and post-build inspection data) and process
control instructions may be shared and exchanged between the
different modules. In some embodiments, a plurality of additive
manufacturing and/or welding apparatus are linked to the same
distributed system so that process data is shared amongst two or
more additive manufacturing and/or welding apparatus control
systems, and used to update the training data set for the entire
distributed system.
[0038] The disclosed methods and systems for automated object
defect classification and adaptive real-time control of additive
manufacturing and/or welding apparatus may provide for rapid
optimization and adjustment of the process control parameters used
in response to changes in process or environmental parameters, as
well as improved process yield, process throughput, and quality of
the parts that are produced. The methods and systems are applicable
to parts fabrication in a variety of different technical fields and
industries including, but not limited to, the automotive industry,
the aeronautics industry, the medical device industry, the consumer
electronics industry, etc.
Definitions:
[0039] Unless otherwise defined, all technical terms used herein
have the same meaning as commonly understood by one of ordinary
skill in the art in the field to which this disclosure belongs. As
used in this specification and the appended claims, the singular
forms "a", "an", and "the" include plural references unless the
context clearly dictates otherwise. Any reference to "or" herein is
intended to encompass "and/or" unless otherwise stated.
[0040] As used herein, the term "free form deposition process" may
refer to any of a variety of liquid-to-solid free form deposition
processes, solid-to-solid free form deposition processes, additive
manufacturing processes, welding processes, and the like.
[0041] As used herein, the term "joining process" may refer to any
of a variety of welding processes.
[0042] As used herein, the term "data stream" refers to a
continuous or discontinuous series or sequence of analog or
digitally-encoded signals (e.g., voltage signals, current signals,
image data comprising spatially-encoded light intensity and/or
wavelength data, etc.) used to transmit or receive information.
[0043] As used herein, the term "process window" refers to a range
of process control parameter values for which a specific
manufacturing process yields a defined result. In some instances, a
process window may be illustrated by a graph of process output
plotted as a function of multiple process control parameters, with
a central region indicating the range of parameter values for which
the process behaves well, and outer borders that define regions
where the process becomes unstable or returns an unfavorable
result.
[0044] As used herein, the term "machine learning" refers to any of
a variety of artificial intelligence or software algorithms used to
perform supervised learning, unsupervised learning, reinforcement
learning, or any combination thereof.
[0045] As used herein, the term "real-time" refers to the rate at
which sensor data is acquired, processed, and/or used in a feedback
loop with a machine learning algorithm to update a classification
of object defects or to update a set or sequence of process control
parameters in response to changes in one or more input process data
streams comprising process simulation data, process
characterization data, in-process inspection data, post-build
inspection data, or any combination thereof.
Additive Manufacturing Processes:
[0046] The term "additive manufacturing" refers to a collection of
versatile fabrication techniques for rapid prototyping and
manufacturing of parts that allow 3D digital models (CAD designs)
to be converted to three dimensional objects by depositing multiple
thin layers of material according to a series of two-dimensional,
cross-sectional deposition maps. Additive manufacturing may also be
referred to as "direct digital manufacturing", "solid free form
fabrication", "liquid solid free form fabrication", or "3D
printing", and may comprise deposition of material in a variety of
different states including liquid, powder, and as fused material. A
wide variety of materials can be processed using additive
manufacturing methods, including metals, alloys, ceramics,
polymers, composites, airy structures, and multi-phase materials.
One of the main advantages of additive manufacturing processes is
the reduced number of fabrication steps required to transform a
virtual design into a ready-to-use (or nearly ready-to-use) part.
Another major advantage is the ability to process complex shapes
that are not easy to fabricate using conventional machining,
extrusion, or molding techniques.
[0047] Specific examples of additive manufacturing techniques to
which the disclosed object defect classification and adaptive
process control methods may be applied include, but are not limited
to, stereolithography (SLA), digital light processing (DLP), fused
deposition modeling (FDM), selective laser sintering (SLS),
selective laser melting (SLM), or electronic beam melting (EBM)
processes.
[0048] Stereolithography (SLA): In stereolithography, a tank of
liquid ultraviolet curable resin is used in combination with a
scanned laser beam to cure one thin layer of resin at a time
according to a two-dimensional exposure pattern. When one layer is
done, the bed or base that it was cured on is lowered slightly into
the tank and another layer is cured. The build platform repeats the
cycle of layer curing and downward steps until the part is
complete. The amount of time required for each cycle of the process
depends on the cross-sectional area of the part and the spatial
resolution required. By the time that the part is complete, it is
completely submerged in the uncured resin. It is then pulled from
the tank and may optionally be further cured in an ultraviolet
oven.
[0049] Digital light processing (DLP): Digital light processing is
a variation of stereolithography in which a vat of liquid polymer
is exposed to light from a DLP projector (e.g., which uses one or
more digital micromirror array devices) under safelight conditions.
The DLP projector projects the image of a 3D model onto the liquid
polymer. The exposed liquid polymer hardens and the build plate
moves down and the liquid polymer is once more exposed to light.
The process is repeated until the 3D object is complete and the vat
is drained of liquid, revealing the solidified model. DLP 3D
printing is fast and may print objects with a higher resolution
than some other techniques.
[0050] Fused deposition modeling (FDM): Fused deposition modeling
is one of the most common forms of 3D printing, and is sometimes
also called Fused Filament Fabrication (FFF). FDM printers can
print in a variety of plastics or polymers, and typically print
with a support material. FDM printers use extruder heads that super
heat the input plastic filament so that it becomes a liquid, and
then push the material out in a thin layer to slowly fabricate an
object in a layer-by-layer process.
[0051] Selective laser sintering (SLS): Selective uses a laser to
fuse material together layer by layer. A layer of powder is pushed
onto the build platform and heated by a laser (and sometimes also
compressed) so that it fuses without passing through a liquid
state. Once that is done, another layer of powder is applied and
heated again. The process requires no support material as the
leftover material holds it upright. After the part is complete, one
removes it from the powder bed and clean off any excess
material.
[0052] Selective laser melting (SLM): Selective laser melting is a
variation of selective laser sintering and direct metal laser
sintering (DMLS) (Yap, et al. (2015), "Review of Selective Laser
Melting: Materials and Applications", Applied Physics Reviews
2:041101). A high power laser is used to melt and fuse metallic
powders. A part is built by selectively melting and fusing powders
within and between layers. The technique is a direct write
technique, and has been proven to produce near net-shape parts
(i.e., fabricated parts that are very close to the final (net)
shape, thereby reducing the need for surface finishing and greatly
reducing production costs) with up to 99.9% relative density. This
enables the process to build near full density functional parts.
Recent developments in the fields of fiber optics and high-powered
lasers have also enabled SLM to process different metallic
materials, such as copper, aluminum, and tungsten, and has opened
up research opportunities in SLM of ceramic and composite
materials.
[0053] Electronic beam melting (EBM): Electron beam melting is an
additive manufacturing technique, similar to selective laser
melting. EBM technology fabricates parts by melting metal powder
layer by layer with an electron beam under high vacuum. In contrast
to sintering techniques, both EBM and SLM achieve full melting of
the metal powder. This powder bed method produces fully dense metal
parts directly from a metal powder which have the characteristics
of the target material. The EBM deposition apparatus reads data
from a 3D CAD model and lays down successive layers of powdered
material. These layers are melted together utilizing a
computer-controlled electron beam to build parts layer by layer.
The process takes place under vacuum, which makes it suitable for
the manufacture of parts using reactive materials with a high
affinity for oxygen, e.g., titanium. The process operates at higher
temperatures than many other techniques (up to 1000.degree. C.),
which can lead to differences in phase formation though
solidification and solid-state phase transformation. The powder
feedstock is typically pre-alloyed, as opposed to being a mixture.
Compared to SLM and DMLS, EBM generally has a faster build rate
because of its higher energy density and scanning method.
Laser-Metal Wire Deposition:
[0054] In one preferred embodiment, the additive manufacturing
processes and systems to which the disclosed defect classification
and adaptive control methods may be applied is laser-metal wire
deposition. The central process in laser-metal wire deposition is
the generation of beads of deposited material (a plurality of which
may be required to form a single layer) using a high-power laser
source and additive material in the form of metal wire (Heralie
(2012), Monitoring and Control of Robotized Laser Metal-Wire
Deposition, Ph.D. Thesis, Department of Signals and Systems,
Chalmers University of Technology, Goteborg, Sweden). The laser
generates a melt pool on the substrate material, into which the
metal wire is fed and melted, forming a metallurgical bound with
the substrate. By moving the laser processing head and the wire
feeder, i.e., the deposition (or welding) tool, relative to the
substrate a bead is formed during solidification. The relative
motion of the deposition tool and the substrate may be controlled,
for example, using a 6-axis industrial robot arm. The formation of
a deposited layer is illustrated in FIG. 2, as will be described in
more detail below.
[0055] Prior to beginning deposition, a set of process parameters
typically needs to be chosen and the equipment needs to be adjusted
accordingly. Important process control parameters for laser-metal
wire deposition include the laser power setting, the wire feed
rate, and the traverse speed. These control the energy input, the
deposition rate and the cross-section profile of the layer being
deposited, i.e., the width and the height of the layer. The height
(or thickness) of the deposited layer is determined by the amount
of wire that is fed into the melt pool in relation to the traverse
speed and the laser power. Once the nominal laser power, traverse
speed, and the wire feed rate have been specified, there may be
additional parameters to set, e.g., the relative orientation of the
wire feed to the laser beam and substrate for a given traverse
speed. Careful adjustment of these parameters is necessary in order
to attain stable deposition on a flat surface.
[0056] Examples of the process control parameters that may need to
be considered in order to achieve stable deposition of uniform
beads of material on a flat surface include, but are not limited
to:
[0057] Laser power: one of the main process control parameters, the
laser power setting determines the maximum energy input. Depending
on the laser beam size and the traverse speed, laser power also
controls the melt pool size and consequently the width of the
deposited bead.
[0058] Laser power distribution: affects the melt pool dynamics.
Non-limiting examples of different laser power (or beam profile)
distributions include step-function and Gaussian distributions.
[0059] Laser/wire or laser/substrate angle: affect the process
window and the true energy input. The angle between the laser beam
and the wire feed impacts the sensitivity of the deposition process
to changes in wire feed rate and variations in distance between the
wire nozzle and the substrate. The angle between the laser beam and
the substrate impacts the reflection of the laser beam from the
substrate surface, and hence the amount of absorbed energy.
[0060] Laser beam size and shape: control the size and the shape of
the melt pool (together with the laser power and the traverse
speed). The use of a circular beam shape is common, although
rectangular shapes are being used as well (e.g., with diode
lasers). The size is chosen to reflect the desired bead width.
[0061] Laser beam focal length: controls how collimated the laser
beam is at the substrate surface. Consequently, it impacts the
sensitivity of the deposition process to distance variations
between the focus lens and the substrate.
[0062] Laser wavelength: controls the absorbance of the laser beam
by the deposited material. For metals, the absorbance of laser
light varies with wavelength (and specific materials).
[0063] Wire feed rate: another one of the main process control
parameters, the wire feed rate impacts the amount of mass deposited
per unit time. The wire feed rate primarily impacts the bead
height, and needs to be chosen in relation to the laser power and
the traverse speed.
[0064] Wire diameter: should be chosen in relation to the laser
beam size to ensure proper melting and a flexible process.
[0065] Wire/substrate angle: affects the melting of the wire and
thereby also the stability of the deposition process. Under proper
conditions, the transfer of metal between the wire and the melt
pool is smooth and continuous. Use of an improper wire/substrate
angle may cause the metal transfer process to result in either
globular deposition, e.g., as a series of droplets on the substrate
surface, or the wire may still be solid as it enters the melt pool.
Use of a higher angle reduces sensitivity to deposition direction,
but at the same time results in a smaller process window of
allowable wire feed rates.
[0066] Wire tip position relative to the melt pool: also affects
the melting rate of the wire and thereby the stability of the
process.
[0067] Wire stick-out: typically not as critical as the wire angle
or the wire tip position, but the stick-out distance may need to be
adjusted depending on the expected deposition conditions. It
primarily affects the sensitivity of the process to variations in
height between the wire nozzle and the substrate.
[0068] Shield gas: use of a shield gas may impact the degree to
which contaminants and/or defects are introduced into the
deposition layer. Composition, flow rate, and/or angle of incidence
may be adjusted in some embodiments.
[0069] Feed direction: determines from which direction the wire
enters the melt pool, and thereby affects the melting of the wire,
and thus the metal transfer process. Different choices of feed
direction change the range of allowed wire feed rates that may be
used. In some cases, it can also affect the shape of the deposited
bead.
[0070] Traverse speed: another one of the main process control
parameters, the traverse speed impacts the amount of material
deposited per unit length and the input energy per unit length. At
lower traverse speeds the deposition process is typically more
stable, unless the temperature of the deposited material becomes
too high. At high traverse speeds, lower energy inputs can be
obtained for the same amount of material deposited per unit length.
However, the motion control system's acceleration and path accuracy
become more critical.
[0071] Process stability: Proper tuning of the process control
parameters described above influences the rate of transfer of metal
between the solid wire and the melt pool, which is important for
the stability of the deposition process. In general, there are
three ways that the metal wire can be deposited: by globular
(droplet-like) transfer, smooth transfer, or by plunging (i.e.,
incomplete melting of the wire prior to entering the melt pool).
Only smooth transfer results in a stable deposition process.
[0072] If the deposition apparatus is set-up so that the wire tip
spends too much time in the laser beam (e.g., by choosing a feed
angle that is too high in relation to the other process control
parameters), it will reach the melting temperature somewhere prior
to entering the melt pool. The transfer of metal between the solid
wire and the melt pool might then be stretched to a point where
surface tension can no longer maintain the flow of metal, resulting
in the formation and separation of surface tension-induced
spherical droplets. This type of deposition gives rise to highly
irregular bead shapes and a poor deposition process. Once globular
transfer starts, it is typically hard to abort. The physical
contact between the molten wire tip and the melt pool must be
re-established, and the process control parameters must be adjusted
to appropriate values.
[0073] Alternatively, if the wire feed angle is carefully adjusted
so that the wire is melted close to the intersection with the melt
pool, there will be a smooth transfer of metal from the solid wire
to the liquid metal of the melt pool. The resulting beads of
deposited metal will have a smooth surface and a stable
metallurgical bond to the substrate.
[0074] Another way to melt the wire is by heat conduction from the
melt pool, i.e., by plunging the wire into the melt pool.
Precautions must be taken to adjust the wire feed rate to a value
sufficiently low relative to the melting rate provided by the heat
energy in the melt pool that the wire melts completely. Incomplete
melting can result in, for example, lack of fusion (LOF) defects.
Note that LOF defects may occur even at low wire feed rates for
which the resulting beads are more or less indistinguishable from
normal bead depositions.
[0075] Adjustment of process parameters: The process control
parameters described above are adjusted depending on the choice of
material and the energy input required to melt the material, which
in turn is determined based on the desired deposition rate,
deformation restrictions, the material's viscosity, and the
available laser power and beam spot sizes. These factors put a
requirement on the laser power, the traverse speed, and the wire
feed rate settings. The laser beam should preferably be as
orthogonal to the melt pool as possible to minimize reflection
while avoiding back reflection into the optical system. The wire
tip position relative to the melt pool should be adjusted with
regard to the chosen amount of material deposited per time unit. If
a front feed configuration is used and the deposition rate is low,
the wire should enter the melt pool closer to the leading edge.
Changing this parameter mainly affects the maximum and minimum wire
feed rate for the chosen laser power and traverse speed. A closely
related parameter to the wire tip position is the wire/substrate
angle. If the angle is low, high wire feed rates might be possible
since plunging can be exploited in a better way. However, for
extreme wire feed rates, only front feeding is feasible. This then
limits the choice of complex deposition paths, such as zig-zag or
spiral patterns. To decrease the sensitivity of the deposition
process to feed direction and thereby allow for arbitrary
deposition patterns, the angle between the wire and the substrate
should be increased. However, increased flexibility in terms of
allowable deposition patterns is often achieved at the cost of a
smaller process window.
[0076] Multi-layered deposition: Obtaining stable deposition of a
single bead of material on a flat substrate requires careful
adjustment of the process control parameters, as discussed above.
Ultimately, however, the goal is to deposit three-dimensional
parts, i.e., to deposit several adjacent beads in a layer, and to
repeat the deposition for a number of layers. The transition from
deposition of a single bead to deposition of a three-dimensional
part is often not straightforward. The precise shape of the
individual layers is influenced by several additional factors,
e.g., the deposition pattern, the distance between adjacent beads,
and the motion control system's speed and path accuracy. The
relationship between these factors and their impact of the
resulting layer are complex and hard to predict, which complicates
the adjustment of process control parameters required to achieve a
given deposition design feature, e.g., the layer height. Another
example of a factor that complicates the deposition of
three-dimensional parts is the potential increase in local
temperature of the part due to heat accumulation, which needs to be
considered during multi-layered deposition. Heat may be accumulated
in the deposited part, for example, due to the use of overly short
pauses between deposition of adjacent layers.
[0077] The additional uncertainties that arise in three-dimensional
deposition may create a problem from a process stability point of
view. For example, if the estimate of layer height to be achieved
is incorrect, the relationship between the wire tip and the
substrate will be different from what was expected for the process
parameters as originally set. As a result, the deposition process
might transition from a smooth transfer of the molten wire to
either a globular deposition mode or a wire plunging mode.
Consequently, as long as the deposition process is not sufficiently
understood and/or tightly controlled that the dimensions of the
individual layers can be accurately predicted, three-dimensional
deposition may require continuous on-line monitoring and/or process
control parameter adjustment.
Difficulties in Optimizing Additive Manufacturing Processes:
[0078] Some of the difficulties discussed above in the context of
laser-metal wire deposition are also applicable to other additive
manufacturing processes (Guessasma, et al., (2015) "Challenges of
Additive Manufacturing Technologies from an Optimisation
Perspective", Int. J. Simul. Multisci. Des. Optim. 6, A9).
Generation of the toolpaths from three-dimensional CAD models
represents the first challenge. Most additive manufacturing
technologies rely on a successive layer-by-layer fabrication
process, so starting from a three-dimensional representation of the
part (i.e., a tessellated version of the part's actual surface) and
ending with a two-dimensional build strategy may introduce errors.
The problem is particularly prevalent in droplet-based 3D printing
approaches, as discontinuities in the fused material may appear in
all build directions as a result of the layer-by-layer deposition
process, and may lead to dimensional inaccuracy, unacceptable
finish state, and structural and mechanical anisotropies.
Anisotropy may also arise in the development of particular grain
texture, for example, in laser melting deposition or arc welding of
metals. Reduction of anisotropy may sometimes be achieved by
selecting the appropriate build orientation of the virtual
design.
[0079] In addition, the differences between a virtual design and
the as-fabricated object may sometimes be significant due to the
finite spatial resolution available with the additive manufacturing
tooling used, or due to part shrinkage during solidification of the
deposited material, which can cause both changes in dimension as
well as deformation of the part. Consider, for example, fused
deposition modelling for which the toolpath comprises a collection
of filament paths of finite dimension. This has three main
consequences on the fabricated object: (i) internal structural
features may not be well captured depending on their size; (ii)
discontinuities may appear depending on local curvature; and (iii)
the surface finish state may be limited due to rough profiles
arising from the fusing of multiple filaments.
[0080] One consequence of the discontinuous fabrication process and
other issues related to additive manufacturing process errors is
porosity. Many technical publications have been directed to the
evaluation of the effect of porosity in printed parts. One
particular consequence is that porosity may reduce the mechanical
performance of the part, e.g., through a decrease of stiffness with
increased porosity level, or through lower mechanical strength
under tension because of the development of porosity-enhanced
damage in the form of micro-cracks. It should be noted that
porosity may not always be viewed as a negative consequence of
additive manufacturing processes, as it can be used, for example,
to increase permeability in some applications.
[0081] Another type of defect encountered with some additive
manufacturing processes is the presence of support material trapped
between internal surfaces. Support material is sometimes needed to
reinforce fragile printed structures during the printing process.
Although these materials are typically selected to exhibit limited
adhesion to the deposited materials, incomplete removal resulting
in residual amounts of support material in the part may contribute
to, for example, increased weight of the part and a modified load
bearing distribution, which in turn may alter the performance of
the part relative to that expected based on the original design. In
addition, non-optimized support deposition may affect the finish
state of the part, material consumption, fabrication time, etc.
Various strategies have been described in the literature to reduce
the dependence of additive manufacturing processes on the use of
support materials. The strategies may vary depending on the
geometry of the part and the choice of material to be
deposited.
Welding Processes:
[0082] In some embodiments, the disclosed defect classification and
process control methods and systems may be applied to welding
processes and apparatus instead of, or in combination with,
additive manufacturing processes and apparatus. Examples of welding
processes and apparatus that may be employed with the disclosed
process control methods and systems include, but are not limited
to, laser beam welding processes and apparatus, MIG (metal inert
gas) welding processes and apparatus (also referred to as gas metal
arc welding), TIG (tungsten inert gas) welding processes and
apparatus, and the like.
[0083] Laser beam welding (LBW): a welding technique used to join
metal components that need to be joined with high welding speeds,
thin and small weld seams and low thermal distortion. The laser
beam provides a focused heat source, allowing for narrow, deep
welds and high welding rates. The high welding speeds, automated
operation, and capability to implement feedback control of weld
quality during the process make laser welding a common joining
method in modern industrial production. Examples of automated, high
volume applications include use in the automotive industry for
welding car bodies. Other applications include the welding of fine,
non-porous seams in medical technology, precision spot welding in
the electronics or jewelry industries, and welding in tool and
mold-making.
[0084] MIG welding: an arc welding process in which a continuous
solid wire electrode is fed through a welding gun and into the weld
pool, joining the two base materials together. A shielding gas is
also sent through the welding gun and protects the weld pool from
contamination, hence the name "metal inert gas" (MIG) welding. MIG
welding is typically used to join thin to medium thick sheets of
metal.
[0085] TIG welding: TIG welding (technically called gas tungsten
arc welding (GTAW)) is a process that uses a non-consumable
tungsten electrode to deliver the current to the welding arc. The
tungsten and weld puddle are protected and cooled with an inert
gas, typically argon. TIG welding typically produces a somewhat
neater and more controlled weld than MIG welding.
Conversion of 3D CAD Files to Layers and Tool Paths:
[0086] Computer-aided design: The first step in a typical free form
deposition process, such as an additive manufacturing process, is
to create a three-dimensional model of the object to be fabricated
using a computer-aided design (CAD) software package. Any of a
variety of commercially-available CAD software packages may be used
including, but not limited to, SolidWorks (Dassault Systemes
SolidWorks Corporation, Waltham, Mass.), Autodesk Fusion 360
(Autodesk, Inc., San Rafael, Calif.), Autodesk Inventor (Autodesk,
Inc., San Rafael, Calif.), PTC Creo Parametric (Needham, Mass.),
and the like.
[0087] Conversion to STL file format: Once the CAD model is
completed, it is typically converted to the standard STL
(stereolithography) file format (also known as the "standard
triangle language" or "standard tessellation language" file format)
that was originally developed by 3D Systems (Rock Hill, S.C).. This
file format is supported by many other software packages and is
widely used for rapid prototyping, 3D printing, and computer-aided
manufacturing. STL files describe only the surface geometry of a
three-dimensional object without any representation of color,
texture or other common CAD model attributes. In an ASCII STL file,
the CAD model is represented using triangular facets, which are
described by the x-, y-, and z-coordinates of the three vertices
(ordered according to the right-hand rule) and a unit vector to
indicate the normal direction that points outside of the facet
(Ding, et al. (2016), "Advanced Design for Additive Manufacturing:
3D Slicing and 2D Path Planning", Chapter 1lin New Trends in 3D
Printing, I. Shishkovsky, Ed., Intech Open).
[0088] Slicing the STL model to create layers: Once the STL file
has been created unidirectional or multidirectional slicing
algorithms are used to slice the STL model into a series of layers
according to the build direction. Uniform slicing methods create
layers having a constant thickness. The accuracy of additively
manufactured parts may sometimes be improved by altering the layer
thickness. Typically, the smaller the layer thickness, the higher
the achieved accuracy will be. The material deposition rate is also
highly relevant to the sliced layer thickness. Adaptive slicing
approaches thus slice the STL model with a variable thickness.
Based on the surface geometry of the model, this approach
automatically adjusts the layer thickness to improve the accuracy
of the fabricated part or to improve the build time.
[0089] As noted above, many additive manufacturing processes
utilize slicing a 3D CAD model into a set of two-dimensional layers
having either a constant or adaptive thickness, where the layers
are stacked in a single build direction. However, when fabricating
parts with complex shapes unidirectional slicing strategies are
generally limited by the need to include support structures for
fabrication of overhanging features. The need to deposit support
structures results in longer build times, increased material waste,
and increased (and sometimes costly) post-processing for the
removal of the supports. Some additive manufacturing techniques are
capable of depositing material along multiple build directions. The
use of multi-directional deposition helps to eliminate or
significantly decrease the requirement for support structures in
the fabrication of complex objects. A key challenge in
multi-directional additive manufacturing is to develop robust
algorithms capable of automatically slicing any 3D model into a set
of layers which satisfy the requirements of support-less and
collision-free layered deposition. A number of strategies for
achieving this have been described in the technical literature
(Ding, et al. (2016), "Advanced Design for Additive Manufacturing:
3D Slicing and 2D Path Planning", Chapter lin New Trends in 3D
Printing, I. Shishkovsky, Ed., Intech Open).
[0090] Tool path planning: Another important step in free form
deposition or additive manufacturing is the development of tool
path strategies based on the layers identified by the slicing
algorithm. Tool path planning for powder-based additive
manufacturing processes that utilize fine,
statistically-distributed particles is somewhat independent of
geometric complexity. However, tool path planning for additive
manufacturing processes that utilize larger, sometimes coarse beads
of deposited material may be directly influenced by geometric
complexity. In addition, the properties of the deposited material
(height and width of the bead, surface finish, etc.) may be
influenced by the deposition tool path trajectory. A variety of
tool path planning strategies have been described in the technical
literature including, but not limited to, the use of raster tool
paths, zigzag tool paths, contour tool paths, tool paths, hybrid
tool paths, continuous tool paths, hybrid and continuous tool
paths, medial axis transformation (MAT) tool paths, and adaptive
MAT tool paths.
[0091] Raster tool paths: The raster scanning tool path technique
is based on planar ray casting along one direction. Using this tool
path approach, two-dimensional regions of a given layer are filled
in by depositing a set of material beads having finite width.
Commonly employed in commercial additive manufacturing systems, it
features simple implementation and is suitable for use with almost
any arbitrary boundary.
[0092] Zigzag tool paths: Derived from the raster approach, zigzag
tool path generation is the most popular method used in commercial
additive manufacturing systems. Compared to the raster approach,
the zigzag approach significantly reduces the number of tool path
passes (and hence the build time) required to fill in the geometry
line-by-line by combining the separate parallel lines into a single
continuous zigzag pass. As with the raster tool path approach, the
outline accuracy of the part is sometimes poor due to
discretization errors on any edge that is not parallel to the tool
motion direction.
[0093] Contour tool paths: Contour tool paths, another frequently
used tool path method, help address the geometrical outline
accuracy issue noted above by following the part's boundary
contours. Various contour map patterns have been described in the
literature for developing optimal tool path patterns for parts
comprising primarily convex shapes that may also include openings
or `islands" (isolated sections of a model within a given
layer).
[0094] Spiral tool paths: Spiral tool paths have been widely
applied in computer numerically controlled (CNC) machining, e.g.,
for two-dimensional pocket milling (i.e., removal of material
inside of an arbitrarily closed boundary on a flat surface of a
work piece to a specified depth). This method can also be used with
additive manufacturing processes to overcome the boundary problems
of zigzag tool paths, but is typically only suitable for certain
special geometrical models.
[0095] Hybrid tool paths: Hybrid tool paths share some of the
features of more than one approach. For example, a combination of
contour and zigzag tool path patterns is sometimes developed to
meet both the geometrical accuracy requirements of a part and to
improve the overall build efficiency.
[0096] Continuous tool paths: The goal of continuous tool path
approaches is to fill in a deposition layer using one continuous
path, i.e., a tool path that is capable of filling in an entire
region without intersecting itself. This approach has been found to
be particularly useful in reducing shrinkage during some additive
manufacturing fabrication processes. However, the approach often
necessitates frequent changes in path direction that may not be
suitable for some deposition processes. Furthermore, when the area
to be filled is large and the accuracy requirement is high, the
processing time required may be unacceptably long. In addition,
highly convoluted tool paths may result in excess accumulation of
heat in certain regions of the part, thereby inducing unacceptable
distortion of the part.
[0097] Hybrid continuous tool paths: Tool path strategies have been
developed which combine the merits of zigzag and continuous tool
path patterns. In these approaches, the two-dimensional geometry is
first decomposed into a set of monotone polygons. For each monotone
polygon, a closed zigzag curve is then generated. Finally, a set of
closed zigzag curves are combined together into an integrated
continuous tortuous path. Recently, another continuous path pattern
which combines the advantages of zigzag, contour, and continuous
tool path patterns has been developed.
[0098] Medial axis transformation (MAT) tool paths: An alternative
methodology for generating tool paths uses the medial axis
transformation (MAT) of the part geometry to generate offset curves
by starting at the inside and working toward the outside, instead
of starting from the layer boundary and filling toward the inside.
The medial axis of an object is the set of all points having more
than one closest point on the object's boundary. In two dimensions,
for example, the medial axis of a subset S of circles which are
bounded by planar curve C is the locus of the centers of all
circles within S that tangentially intersect with curve C at two or
more points. The medial axis of a simple polygon is a tree-like
skeleton whose branches are the vertices of the polygon. The medial
axis together with an associated radius function of maximally
inscribed circles is called the medial axis transform (MAT). The
medial axis transform is a complete shape descriptor that can be
used to reconstruct the shape of the original domain.
[0099] This approach is useful for computing tool paths which can
entirely fill the interior region of the layer geometry, and avoids
producing gaps by depositing excess material outside the boundary
which can subsequently be removed through post-processing.
Traditional contour tool path patterns which run from outside to
inside are often used for machining, whereas MAT tool paths
starting from the inside and working toward the outside are often
more suitable for additive manufacture of void-free parts. The main
steps for generating MAT-based tool paths are: (i) computation of
the medial axis; (ii) decomposition of the geometry into one or
more regions or domains, where each domain is bounded by a portion
of the medial axis and a boundary loop; (iii) generation of the
tool path for each domain by offsetting from the medial axis loop
toward the corresponding boundary loop with an appropriate
step-over distance. The offsetting is repeated until the domain is
fully covered; and (iv) repeating step (iii) for each domain to
generate a set of closed-loop paths, preferably without start/stop
sequences. MAT path planning is frequently used, for example, with
arc welding systems, and is particularly preferred for void-free
additive manufacturing.
[0100] Adaptive MAT tool paths: Traditional contour tool paths
frequently generate gaps or voids. MAT tool path planning was
introduced to avoid generation of internal voids during deposition,
and has been extended to handle complex geometries. As noted above,
MAT tool paths are generated by offsetting the medial axis of the
geometry from the center toward the layer boundary. Although MAT
tool paths reduce the occurrence of internal voids, this is
achieved at the cost of creating path discontinuities and extra
material deposition at the layer boundary. Post-process machining
to remove the extra materials and improve the dimensional accuracy
of the part requires extra time and adds to the cost. For both
traditional contour tool paths and MAT tool paths, the step-over
distance, i.e., the distance between the next deposition path and
the previous deposition path, is held constant. For some part
geometries, it is not possible to achieve both high dimensional
accuracy and void-free deposition using tool paths with constant
step-over distance. However, some additive manufacturing processes,
such as wire feed additive manufacturing processes, are capable of
producing different deposited bead widths within a layer by varying
process control parameters like travel speed and wire feed rate,
while maintaining constant deposition height. Adaptive MAT tool
path planning uses continuously varying step-over distances by
adjusting the process parameters to deposit beads with variable
width within a given tool path. Adaptive MAT path planning
algorithms are able to automatically generate path patterns with
varying step-over distances by analyzing the part geometry to
achieve better part quality (void-free deposition), accuracy at the
boundary, and efficient use of material.
[0101] Tool path generation software: Examples of toolpath
generation software include Repetier (Hot-World, GmbH, Germany) and
CatalystEx (Stratasys Inc. Eden Prairie Minn., USA).
[0102] FIGS. 3A-C provide schematic illustrations of the conversion
of a CAD design for a three-dimensional object to a continuous,
spiral wound "two-dimensional" layer (of finite thickness) and
associated helical tool path (FIG. 3A), or a stacked series of
"two-dimensional" layers and associated circular, layer-by-layer
tool paths (FIG. 3B) for deposition of material using an additive
manufacturing process. FIG. 3C provides an illustration of the tool
path for a robotically manipulated deposition tool and a simulation
of the resulting object fabricated using an additive manufacturing
process. Tool path and part simulation using a software package
such as Octopuz (Jupiter, Fla.) is performed before running the
deposition process on an actual deposition system. In some
instances, the predicted optimal tool path may be locally modified
during the deposition process in response to closed-loop feedback
control. In some instances, the tool path may be reconstructed
based on the as-built part geometry after the deposition process is
complete.
Process Simulation Tools:
[0103] In some embodiments of the disclosed adaptive process
control methods and systems, process simulation tools may be used
to simulate the free form deposition process (or joining process)
and/or to provide estimates of optimal sets (and/or sequences) of
process control parameter settings (and adjustments). Any of a
variety of process simulation tools known to those of skill in the
art may be used including, but not limited to finite element
analysis (FEA), finite volume analysis (FVA), finite difference
analysis (FDA), computational fluid dynamics (CFD), and the like,
or any combination thereof. In some embodiments of the disclosed
methods and system, process simulation data from past fabrication
runs is used as part of a training data set used to "teach" the
machine learning algorithm used to run the process control.
[0104] Finite element analysis (FEA): Finite element analysis (also
referred to as the finite element method (FEM)) is a numerical
method for solving engineering and mathematical physics problems,
e.g., for use in structural analysis, or studies of heat transfer,
fluid flow, mass transport, and electromagnetic potential.
Analytical solution of these types of problems generally requires
the solution to boundary value problems involving partial
differential equations, which may or may not solvable. The
computerized finite element approach allows one to formulate the
problem as a system of algebraic equations, the solution for which
yields approximate values of the unknown parameters at a discrete
number of points over the geometry or domain of interest. The
problem to be solved is subdivided (discretized) into smaller,
simpler components (i.e., the finite elements) to simplify the
equations governing the behavior of the system. The relatively
simple equations that model the individual finite elements are then
assembled into a larger system of equations that models the entire
problem. Numerical methods drawn from the calculus of variations
are used to approximate a solution to the system of equations by
minimizing an associated error function. FEA is often used for
predicting how a product will react when subjected to real-world
forces, e.g., stress (force per unit are or per unit length),
vibration, heat, fluid flow, or other physical effects.
[0105] As noted above, in some embodiments of the disclosed
adaptive process control methods, FEA may be used to simulate a
deposition process and/or to provide estimates of optimal sets
and/or sequences of process control parameter settings and
adjustments thereof. Examples of deposition process parameters that
may be estimated using FEA analysis (or other simulation
techniques) include, but are not limited to, a prediction of a bulk
or peak temperature of a deposited material, a cooling rate of a
deposited material, a chemical composition of a deposited material,
a segregation state of constituents in a deposited material, a
geometrical property of a deposited material, an angle of overhang
in a deposited geometry, an intensity of heat flux out of a
material during deposition, an electromagnetic emission from a
deposition material, an acoustic emission from a deposition
material, or any combination thereof, as a function of a set of
specified input process control parameters. Because the process
control parameters used as input for the calculation may be
adjusted to determine how they impact the simulated deposition
process, iterative use of process simulation may be used to provide
estimates of optimal sets and/or sequences of process control
parameter settings and adjustments thereof.
[0106] Finite volume analysis (FVA): Finite volume analysis (also
referred to as the finite volume method (FVM)) is another numerical
technique related to finite element analysis that is used for
solving partial differential equations, especially those that arise
from physical conservation laws. FVM uses a volume integral
formulation of the problem with a finite set of partitioning
volumes to discretize the equations representing the original
problem. FVA is, for example, commonly used for discretizing
computational fluid dynamics equations.
[0107] Finite difference analysis (FDA): Finite difference analysis
(also referred to as the finite difference method (FDM)) is another
numerical method for solving differential equations by
approximating them with difference equations, in which finite
differences approximate the derivatives.
[0108] Computational fluid dynamics (CFD): Computational fluid
dynamics refers to the use of applied mathematics, physics, and
computational software (e.g., finite volume analysis software) to
visualize how a gas or liquid flows in response to applied
pressure, or to visualize how the gas or liquid affects objects as
it flows past. Computational fluid dynamics is based on solution of
Navier-Stokes equations, which describe how the velocity, pressure,
temperature, and density of a moving fluid are related. CFD-based
analysis is used in a variety of industries and applications, for
example, computational fluid dynamics has been used to model
predictive control for controlling melt temperature in plastic
injection molding.
[0109] FIGS. 4A-C provide examples of FEA simulation data for
modeling of a laser-metal wire deposition melt pool. FIG. 4A:
isometric view of color-encoded three-dimensional FEA simulation
data for the liquid fraction of material in the melt pool being
deposited by a laser-metal wire deposition process. The metal is in
a completely liquid state at the position where the wire tip merges
with the melt pool, and transitions to increasingly lower liquid
fractions as it solidifies downstream from the position of the
wire. FIG. 4B: cross-sectional view of the FEA simulation data for
the liquid fraction of material in the melt pool. FIG. 4C:
cross-sectional view of color-encoded three-dimensional FEA
simulation data for the static temperature of the material in the
melt pool. The temperature is at a maximum value (approximately
2,900.degree. K in this example) at the point where the laser beam
impinges on the wire tip, and is asymmetrically distributed along
the motion path of the deposition apparatus with higher
temperatures exhibited by the material immediately downstream from
the wire tip.
Process Control Parameters:
[0110] In some embodiments of the disclosed adaptive process
control methods, one or more free form deposition process control
parameters (or joining process control parameters) may be set
and/or adjusted in real-time through the use of a machine learning
algorithm that processes real-time deposition or welding process
monitoring data, e.g., data from a machine vision system or laser
interferometry measurement system, and uses that information to
adjust the one or more process control parameters to improve the
efficiency of the process and/or the quality of the part being
fabricated.
[0111] In general, the types of process control parameters that may
be set and/or adjusted by the adaptive process control system will
vary depending on the specific type of free form deposition,
additive manufacturing, or welding process being used. Examples of
process control parameters that may be set and/or adjusted include,
but are not limited to, the rate of material deposition, the rate
of displacement for a deposition apparatus, the rate of
acceleration for a deposition apparatus, the direction of
displacement for a deposition apparatus, the location of a
deposition apparatus as a function of time (i.e., a tool path), the
angle of a deposition apparatus with respect to a deposition
direction, the angle of overhang in an intended geometry, the
intensity of heat flux into a material during deposition, the size
and shape of a heat flux surface, the flow rate and angle of a
shielding gas flow, the temperature of a baseplate on which
material is deposited, the ambient temperature during a deposition
process, the temperature of a deposition material prior to
deposition, a current or voltage setting in a resistive heating
apparatus, a voltage frequency or amplitude in an inductive heating
apparatus, the choice of deposition material, the ratio by volume
or the ratio by weight of deposition materials if more than one
deposition material is used, or any combination thereof.
[0112] As indicated above, examples of process control parameters
for a laser-metal wire deposition process that may be set and/or
adjusted by the adaptive process control systems of the present
disclosure include, but are not limited to, laser power, laser
power distribution (or beam profile), laser/wire or laser/substrate
angle, laser beam size and shape, laser beam focal length, laser
wavelength, wire feed rate, wire diameter, wire/substrate angle,
wire tip position relative to the melt pool, wire stick-out, shield
gas settings, feed direction, and traverse speed.
[0113] In some embodiments of the disclosed adaptive process
control methods and system, one or more process control parameters
may be set and/or adjusted by the machine learning algorithm used
to run the control process. In some embodiments, the number of
different process control parameters to be set and/or adjusted may
be at least 1, at least 2, at least 3, at least 4, at least 5, at
least 10, at least 15, or at least 20. Those of skill in the art
will recognize that the number of different process control
parameters to be set and/or adjusted by the disclosed process
control methods and systems may have any value within this range,
e.g., 12 process control parameters.
Process Monitoring Tools:
[0114] In some embodiments of the disclosed adaptive process
control methods and systems, one or more process monitoring tools
may be used to provide real-time data on process parameters or
properties of the object being fabricated, both of which will be
referred to herein as "process characterization data". In some
embodiments of the disclosed methods and system, process
characterization data from past fabrication runs is used as part of
a training data set used to "teach" the machine learning algorithm
used to run the process control. In some embodiments, real-time (or
"in-process") process characterization data is fed to the machine
learning algorithm so that it may adaptively adjust one or more
process control parameters in real-time.
[0115] Any of a variety of process monitoring tools known to those
of skill in the art may be used including, but not limited to,
temperature sensors, position sensors, motion sensors,
touch/proximity sensors, accelerometers, profilometers,
goniometers, image sensors and machine vision systems, electrical
conductivity sensors, thermal conductivity sensors, strain gauges,
durometers, X-ray diffraction or imaging devices, CT scanning
devices, ultrasonic imaging devices, Eddy current sensor arrays,
thermographs, deposition apparatus status indicators, or any
combination thereof. In some embodiments, the process
characterization sensors may comprise one or more sensors that
detect electromagnetic radiation that is reflected, scattered,
absorbed, transmitted, or emitted by the object. In some
embodiments, the process characterization sensors may comprise one
or more sensors that provide data on acoustic energy or mechanical
energy that is reflected, scattered, absorbed, transmitted, or
emitted by the object.
[0116] Any of a variety of process parameters may be monitored
(i.e., to generate process characterization data) using appropriate
sensors, measurement tools, and/or machine vision systems
including, but not limited to, measurement of a bulk or peak
temperature of a deposited material, a cooling rate of a deposited
material, a chemical composition of a deposited material, a
segregation state of constituents in a deposited material, a
geometrical property of a deposited material (e.g., a local
curvature of a printed part), a rate of material deposition, a rate
of displacement for a deposition apparatus, a location (tool path)
of a deposition apparatus, an angle of a deposition apparatus with
respect to a deposition direction, a deposition apparatus status
indicator, an angle of overhang in a deposited geometry, an angle
of overhang in an intended geometry, an intensity of heat flux into
a material during deposition, an intensity of heat flux out of a
material during deposition, an electromagnetic emission from a
deposition material, an acoustic emission from a deposition
material, an electrical conductivity of a deposition material, a
thermal conductivity of a deposition material, a defect in the
geometry of an object being fabricated, or any combination
thereof.
[0117] The disclosed methods and systems for adaptive process
control may comprise the use of any number and any combination of
sensors or process monitoring tools. For example, in some
embodiments, an adaptive deposition process control system of the
present disclosure may comprise at least 1, at least 2, at least 3,
at least 4, at least 5, at least 6, at least 7, at least 8, at
least 9, or at least 10 sensors or process monitoring tools. In
some embodiments, the one or more sensors or process monitoring
tools may provide data to the process control algorithm at an
update rate of at least 0.1 Hz, 1 Hz, 5 Hz, 10 Hz, 20 Hz, 30 Hz, 40
Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100 HZ, 250 Hz, 500 Hz, 750
Hz, 1,000 Hz, 2,500 Hz, 5,000 Hz, 10,000 Hz, or higher. Those of
skill in the art will recognize that the one or more sensors or
process monitoring tools may provide data at an update rate having
any value within this range, e.g., about 225 Hz.
[0118] Laser interferometry: One specific example of a free form
deposition or joining process monitoring tool that may be used
with, for example, a laser-metal wire deposition system is a laser
interferometer for accurate, in-process measurement of part
dimensions, refractive index changes, and/or surface
irregularities. Laser light from a single source is split into two
beams that follow separate optical paths until they are re-combined
following the transmission or reflection of one of the beams by a
sample, e.g., the part being fabricated, to produce interference.
The resulting interference fringes provide precise information
about the difference in optical path length for the two beams, and
hence provide precise measurements of part dimensions,
displacements, surface irregularities, etc. Interferometers are
capable of measuring dimensions or displacements with nanometer
precision.
[0119] FIG. 5 illustrates one non-limiting example of a laser-metal
wire deposition system that comprises a robotic controller, a laser
power unit, a wire feed and shield gas module, a wire pre-heater,
and environmental controller, a telemetry database (for
transmitting and recording process control instructions sent to and
process monitoring data read from the deposition system), and a
programmable logic controller (which coordinates the overall
operation of the system components), as well as a laser
interferometer. The laser interferometer provides real-time
feedback on melt pool properties. In some embodiments, the
deposition system may further comprise a processor programmer to
utilize a machine learning algorithm, e.g., an artificial neural
network, for real-time, adaptive control of the metal deposition
process. In some embodiments, the deposition system may also
include machine vision systems or other inspection tools monitor
process parameters and/or to provide for automated classification
of object defects (post-build or in-process), and may incorporate
such process monitoring or defect classification for use by the
machine algorithm in predicting next action(s) by the deposition
process.
[0120] FIG. 2 provides a schematic illustration of an example
set-up for a material deposition process, e.g., a laser-metal wire
deposition process, according to some embodiments of the present
disclosure. The laser beam impinges on the metal wire to create a
melt pool at the point of intersection and deposit material on a
substrate. The melt pool material subsequently hardens to form a
new layer as the laser and wire feed (i.e., the print head) are
moved relative to the substrate. The wire is shielded from
air-borne contaminants with the use of a sheath of shield gas. As
indicated by the example of FEA simulation date presented in FIG.
4C, heat propagates from the position of the melt pool through the
underlying substrate (or previously deposited layers) in an
asymmetric fashion due to the translational motion of the print
head relative to the substrate. The newly deposited layer forms a
metallurgical bond with the substrate (or previously deposited
layers) in a region referred to as the fusion zone. The propagation
of heat through the newly deposited layer to the substrate (or
previously deposited layers) may in some instances affect material
properties within a region referred to as the heat affected zone.
The solidification process may also cause metallurgical defects
such as pores and cracks to form in the deposited layer. The
quantity and type of defects that arise are dependent on the amount
of heat input, the time spent at elevated temperatures, the
geometry of the printed part, and the presence of contaminants near
the melt pool.
[0121] FIGS. 6A-B illustrate the use of laser interferometry to
monitor melt pool and deposition layer properties in a laser-metal
wire deposition process. FIG. 6A shows a micrograph of the
deposition process at the location where the laser beam impinges on
the metal wire. The vertical lines indicate the position of the
interferometer probe beam as it is used to monitor the height
profile of the wire feed and previously deposited layer and
resulting melt pool. FIG. 6B provides examples of cross-sectional
profiles (i.e., height profiles across the width of the deposition)
of the wire feed, previously deposited layer, and melt pool as
measured using laser interferometry at the position of the wire
feed (solid line; the peak indicates the wire, while the shoulders
indicate the height of the previously deposited layer) and the melt
pool (dashed line). The x-axis (width) dimension is plotted in
arbitrary units. The y-axis (height) dimension is plotted in units
of millimeters relative to a fixed reference point below the
deposition layer. In some embodiments of the disclosed adaptive
process control methods, such real-time process monitoring data may
be used by a processor running a machine learning algorithm to make
adjustment(s) to one or more process control parameters in order to
improve, for example, the dimensional accuracy of the layer, layer
surface finish and/or adhesion properties, and/or the overall
efficiency of the deposition process.
[0122] In some embodiments, laser interferometry may be used to
monitor the dimensions and/or properties of the melt pool, the
deposited layer downstream from the melt pool, or other features of
the part being fabricated at one or more positions on the part. In
some embodiment, laser interferometry may be used to monitor the
dimensions and/or properties of the part being fabricated at at
least 1, at least 2, at least 3, at least 4, at least 5, at least
6, at least 7, at least 8, at least 9, or at least 10 different
positions on the part. In some embodiment, the laser interferometry
data for dimensions and/or other properties of the part may be
updated at a rate of at least 0.1 Hz, 1 Hz, 5 Hz, 10 Hz, 20 Hz, 30
Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100 HZ, 250 Hz, 500
Hz, 750 Hz, 1,000 Hz, 2,500 Hz, 5,000 Hz, 10,000 Hz, 25,000 Hz,
50,000 Hz, 100,000 Hz, 150,000 Hz, 200,000 Hz, 250,000 Hz, or
higher. Those of skill in the art will recognize that the rate at
which the interferometry data may be updated may have any value
within this range, e.g., about 800 Hz.
[0123] Machine vision systems: Another specific example of a free
form deposition or joining process monitoring tool that may be used
with, for example, a laser-metal wire deposition system is machine
vision. Machine vision systems provide imaging-based automatic
inspection and analysis for a variety of industrial inspection,
process control, and robot guidance applications, and may comprise
any of a variety of image sensors or cameras, light sources or
illumination systems, and additional imaging optical components, as
well as processors and image processing software.
[0124] FIGS. 7A-C illustrate in-process feature extraction from
images of a laser-metal wire deposition process obtained using a
machine vision system. FIG. 7A shows a raw image (e.g., one image
frame grabbed from a video rate data stream) of the melt pool
adjacent to the tip of the wire. FIG. 7B shows the processed image
after de-noising, filtering, and edge detection algorithms have
been applied. FIG. 7C shows the processed image after application
of a feature extraction algorithm used to identify, for example,
the angel of the wire relative to the build plate and the height
(thickness) of the new layer. Machine vision systems and the
associated image processing capability allow one to monitor details
of the deposition process in real-time.
[0125] In some embodiments, one or more machine vision systems may
be used with the disclosed adaptive process control methods and
systems to acquire and process single images. In some embodiments,
one or more machine vision systems may be used with the disclosed
adaptive process control methods and systems to acquire and process
a series of one or more images at defined time intervals. In many
embodiments, one or more machine vision systems may be used with
the disclosed adaptive process control methods and systems to
acquire and process video rate image data. In general, image data
supplied by the one or more machine vision systems may be acquired
and/or processed at a rate of at least 0.1 Hz, 1 Hz, 5 Hz, 10 Hz,
20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100 HZ, 250
Hz, 500 Hz, 750 Hz, 1,000 Hz, 2,500 Hz, 5,000 Hz, or higher. Those
of skill in the art will recognize that the rate at which image
data may be acquired and/or processed may have any value within
this range, e.g., 95 Hz.
[0126] In some embodiments, one or more machine vision systems used
with the disclosed adaptive process control methods and systems may
be configured to acquire images at specific wavelengths (or within
specific wavelength ranges) or in different imaging modes. For
example, in some embodiments, one or more machine vision system may
be configured to acquire images in the x-ray region, ultraviolet
region, visible region, near infrared region, infrared region,
terahertz region, microwave region, or radiofrequency region of the
electromagnetic spectrum, or any combination thereof. In some
embodiments, one or more machine vision systems may be configured
to acquire fluorescence images (e.g., where the wavelength range
for the excitation light is different than that for the collected
fluorescence emission light). In some embodiments, one or more
machine vision systems may be configured to acquire coherent Raman
scattering (CRS) images (e.g., stimulated Raman scattering (SRS) or
anti-Stokes Raman scattering (CARS) images) to provide label-free
chemical imaging of the deposition layer or part being
fabricated.
Post-Build Inspection Tools and Automated Defect
Classification:
[0127] Disclosed herein are automated object defect classification
methods and systems used to identify and characterize defects in
fabricated parts. The approach is based on the use of a machine
learning algorithm for detection and classification of defects,
where the machine learning algorithm is trained using a training
dataset that comprises post-build inspection data provided by a
skilled operator and/or inspection data provided by any of a
variety of automated inspection tools known to those of skill in
the art. The disclosed automated object defect classification
methods and systems may be applied to any of a variety of free form
deposition or joining processes known to those of skill in the art.
In some embodiments, the disclosed automated object defect
classification methods and systems may be used strictly for
post-build inspection of new parts. In some embodiments, they may
be used in-process to provide real-time process characterization
data to a machine learning algorithm used to run the process
control, so that one or more process control parameters may be
adjusted in real-time. In some embodiments, the disclosed automated
object defect classification methods and systems may be used both
in-process to provide real-time process characterization data and
for post-build inspection. In some embodiments, in-process
automated defect classification data may be used by the machine
learning algorithm to determine a set or sequence of process
control parameter adjustments that will implement a corrective
action, e.g., to adjust a layer dimension or thickness, so as to
correct a defect when first detected. In some embodiments,
in-process automated defect classification may be used by the
machine learning algorithm to send a warning or error signal to an
operator, or optionally, to automatically abort the deposition
process, e.g., an additive manufacturing process. In some
embodiments, once trained, the automated defect classification
system requires no further user input (e.g., no further input from
a skilled operator or inspector) to detect and classify defects
either in-process and/or post-build.
[0128] The automated object defect classification methods will
generally comprise: a) providing a training data set, wherein the
training data set comprises fabrication process simulation data,
fabrication process characterization data, and/or post-build
inspection data, or any combination thereof, for a plurality of
design geometries that are the same as or different from that of
the object; b) providing one or more sensors, wherein the one or
more sensors provide real-time data for one or more object
properties; c) providing a processor programmed to provide a
classification of detected object defects using a machine learning
algorithm that has been trained using the training data set of step
(a), wherein the real-time data from the one or more sensors is
provided as input to the machine learning algorithm and allows the
classification of detected object defects to be adjusted in
real-time.
[0129] Training data sets: As noted above, the training data set
may comprise fabrication process simulation data, fabrication
process characterization data, post-build inspection data
(including inspection data provided by a skilled operator and/or
inspection data provided by any of a variety of automated
inspection tools), or any combination thereof, for past fabrication
runs of a plurality of design geometries that are the same as or
different from that of the object currently being fabricated. One
or more training data sets may be used to train the machine
learning algorithm used for object defect detection and
classification. In some cases, the type of data included in the
training data set may vary depending on the specific type of
machine learning algorithm employed, as will be discussed in more
detail below. For example, in the case that an expert system (or
expert learning system) the training data set may comprise
primarily defect classification data provided by a skilled operator
or technician in visually identifying and classifying object
defects for the same type of part or for a variety of different
parts that share some common set of features. In some instances,
the training data set may be updated in real-time with object
defect and object classification date as it is performed on a given
system. In some instances, the training data may be updated with
object defect data and object classification data drawn from a
plurality of automated defect classification systems.
[0130] In some embodiments, the training data set may comprise
process simulation data, process characterization data, in-process
inspection data, post-build inspection data, or any combination
thereof. In some embodiments, the training data set may comprise a
single type of data selected from the group consisting of process
simulation data, process characterization data, in-process
inspection data, and post-build inspection data. In some
embodiments, the training data set may comprise a combination of
any two or any three types of data selected from the group
consisting of process simulation data, process characterization
data, in-process inspection data, and post-build inspection data.
In some embodiments, the training data set may comprise all of
these types of data, i.e., process simulation data, process
characterization data, in-process inspection data, and post-build
inspection data.
[0131] Object property measurement: Any of a variety of sensors or
other inspection tools may be used, including some of those listed
above for process monitoring in general. In some embodiments, the
one or more sensors (e.g., image sensors or machine vision systems)
provide data on electromagnetic radiation that is reflected,
scattered, absorbed, transmitted, or emitted by the object. In some
embodiments, the electromagnetic radiation is x-ray, ultraviolet,
visible, near-infrared, or infrared light. In some embodiments, the
one or more sensors provide data on acoustic energy that is
reflected, scattered, absorbed, transmitted, or emitted by the
object. In some embodiments, the one or more sensors provide data
on an electrical conductivity or a thermal conductivity of the
object. In some embodiments, the one or more sensors may provide
data to the processor programmed to provide a classification of
detected object defects using a machine learning algorithm at an
update rate of at least 0.1 Hz, 1 Hz, 5 Hz, 10 Hz, 20 Hz, 30 Hz, 40
Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100 HZ, 250 Hz, 500 Hz, 750
Hz, 1,000 Hz, 2,500 Hz, 5,000 Hz, 10,000 Hz, or higher. Those of
skill in the art will recognize that the one or more sensors or
process monitoring tools may provide data at an update rate having
any value within this range, e.g., about 400 Hz.
[0132] In a preferred embodiment the automated object defect
classification methods and systems of the present disclosure may be
implemented using image sensors and/or machine vision systems.
Automated image processing of the captured images may then be used
to monitor any of a variety of object properties, e.g., dimensions
(overall dimensions, or dimensions of specific features), feature
angles, feature areas, surface finish (e.g., degree of light
reflectivity, number of pits and/or scratches per unit area), and
the like. In some embodiments, object properties such as local,
excessively high temperatures that may be correlated with defects
or defect generation in printed or welded parts may be monitored
using infrared or visible wavelength cameras.
[0133] Noise removal from sensor data: In some embodiments, the
automated defect classification methods may further comprise
removing noise from the object property data provided by the one or
more sensors prior to providing it to the machine learning
algorithm. Examples of data processing algorithms suitable for use
in removing noise from the object property data provided by the one
or more sensors include, but are not limited to, signal averaging
algorithms, smoothing filter algorithms, Kalman filter algorithms,
nonlinear filter algorithms, total variation minimization
algorithms, or any combination thereof.
[0134] Subtraction of reference data sets: In some embodiments of
the disclosed automated defect classification methods, subtraction
of a reference data set from the sensor data may be used to
increase contrast between normal and defective features of the
object, thereby facilitating defect detection and classification.
For example, a reference data set may comprise sensor data recorded
by one or more sensors for an ideal, defect-free example of the
object to be fabricated. In the case that an image sensor or
machine vision system is used for defect detection, the reference
data set may comprise an image (or set of images, e.g.,
representing different views) of an ideal, defect-free object.
[0135] Machine learning algorithms for defect detection and
classification: Any of a variety of machine learning algorithms may
be used in implementing the disclosed automated object defect
detection and classification methods. The machine learning
algorithm employed may comprise a supervised learning algorithm, an
unsupervised learning algorithm, a semi-supervised learning
algorithm, a reinforcement learning algorithm, a deep learning
algorithm, or any combination thereof. In preferred embodiments,
the machine learning algorithm employed for defect identification
and classification may comprise a support vector machine (SVM), an
artificial neural network (ANN), or a decision tree-based expert
learning system, some of which will be described in more detail
below. In some preferred embodiments, object defects may be
detected as differences between an object property data set and a
reference data set that are larger than a specified threshold, and
may be classified using a one-class support vector machine (SVM) or
autoencoder algorithm. In some preferred embodiments, object
defects may be detected and classified using an unsupervised
one-class support vector machine (SVM), autoencoder, clustering, or
nearest neighbor (e.g., kNN) machine learning algorithm and a
training data set that comprises object property data for both
defective and defect-free objects.
Adaptive, Real-Time Deposition Process Control Using a Machine
Learning Algorithm:
[0136] Disclosed herein are methods and systems for providing
real-time adaptive control of deposition processes, e.g., additive
manufacturing or welding processes. In general, the disclosed
methods comprise a) providing an input design geometry for an
object (e.g., a 3D CAD model); b) providing a training data set,
wherein the training data set comprises process simulation data,
process characterization data, post-build inspection data, or any
combination thereof, for a plurality of design geometries or
portions thereof that are the same as or different from the input
design geometry of step (a); c) providing a predicted optimal set
or sequence of one or more process control parameters for
fabricating the object, wherein the predicted optimal set of one or
more process control parameters are derived using a machine
learning algorithm that has been trained using the training data
set of step (b); and d) performing the deposition process, e.g., an
additive manufacturing process, to fabricate the object, wherein
real-time process characterization data is provided by one or more
sensors as input to the machine learning algorithm to adjust one or
more process control parameters in real-time. In some embodiments,
steps (b)-(d) are performed iteratively and the process
characterization data, post-build inspection data, or any
combination thereof for each iteration is incorporated into the
training data set. The disclosed process control methods may be
used for any of a variety of deposition processes, including
additive manufacturing processes, known to those of skill in the
art, for example, stereolithography (SLA), digital light processing
(DLP), fused deposition modeling (FDM), selective laser sintering
(SLS), selective laser melting (SLM), electronic beam melting (EBM)
process, laser beam welding, MIG (metal inert gas) welding, TIG
(tungsten inert gas) welding, and the like. In a preferred
embodiment, the disclosed process control methods are applied to a
liquid-to-solid free form deposition process, for example, to a
laser metal-wire deposition process.
[0137] Training data sets: As with the automated defect
classification methods described above, the training data set(s)
used in teaching the process control machine learning algorithm may
comprise fabrication process simulation data, fabrication process
characterization data, post-build inspection data (including
inspection data provided by a skilled operator and/or inspection
data provided by any of a variety of automated inspection tools),
or any combination thereof, for past fabrication runs of a
plurality of design geometries that are the same as or different
from that of the object currently being fabricated. One or more
training data sets may be used to train the machine learning
algorithm used for adaptive, real-time deposition process control.
In some cases, the type of data included in the training data set
may vary depending on the specific type of machine learning
algorithm employed, as will be discussed in more detail below. For
example, in some cases the training data set may comprise primarily
process control settings provided by a skilled operator or
technician in successfully fabricating a number of the same type of
part or for a variety of different parts that share some common set
of features. In some instances, the training data set may be
updated in real-time using process simulation data, process control
data, process characterization data, in-process inspection data,
and/or post-build inspection data as fabrication is performed on a
given system. In some instances, the training data may be updated
using process simulation data, process control data, process
characterization data, in-process inspection data, and/or
post-build inspection data as fabrication is performed on a
plurality of deposition and/or welding systems.
[0138] In some embodiments, the training data set may comprise
process simulation data, process characterization data, in-process
inspection data, post-build inspection data, or any combination
thereof. In some embodiments, the training data set may comprise a
single type of data selected from the group consisting of process
simulation data, process characterization data, in-process
inspection data, and post-build inspection data. In some
embodiments, the training data set may comprise a combination of
any two or any three types of data selected from the group
consisting of process simulation data, process characterization
data, in-process inspection data, and post-build inspection data.
In some embodiments, the training data set may comprise all of
these types of data, i.e., process simulation data, process
characterization data, in-process inspection data, and post-build
inspection data.
[0139] Process characterization data: Any of a variety of sensors,
measurement tools, or inspection tools may be used for monitoring
various process parameters in real-time, including those listed
above. In some embodiments, for example, laser interferometers are
used to monitor the dimensions of the melt pool (in the case of
laser-metal wire deposition) or other part dimensions as the part
is being fabricated. In some embodiments, the one or more sensors
(e.g., image sensors or machine vision systems) provide data on
electromagnetic radiation that is reflected, scattered, absorbed,
transmitted, or emitted by the object. In some embodiments, the
electromagnetic radiation is x-ray, ultraviolet, visible,
near-infrared, or infrared light. In some embodiments, real-time
image acquisition and processing is used to monitor, for example,
the angle of the wire feed relative to a baseplate or previously
deposited layer, or the thickness of a deposited layer. In some
embodiments, the one or more sensors provide data on acoustic
energy that is reflected, scattered, absorbed, transmitted, or
emitted by the object. In some embodiments, the one or more sensors
provide data on an electrical conductivity or a thermal
conductivity of the object. In some embodiments, the one or more
sensors may provide process characterization data to the processor
programmed to run the machine learning algorithm may be updated at
a rate of at least 0.1 Hz, 1 Hz, 5 Hz, 10 Hz, 20 Hz, 30 Hz, 40 Hz,
50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100 HZ, 250 Hz, 500 Hz, 750 Hz,
1,000 Hz, 2,500 Hz, 5,000 Hz, 10,000 Hz, or higher. Those of skill
in the art will recognize that the one or more process
characterization sensor may provide data at an update rate having
any value within this range, e.g., about 8,000 Hz.
[0140] In a preferred embodiment, the real-time process
characterization data that is fed to the machine learning algorithm
used to run process control may comprise data supplied by an
automated object defect classification system as described above,
so that the deposition process control parameters may be adjusted
in real-time to compensate or correct for part defects as they
arise during the build process. The machine learning algorithm used
to run the automated process control may be configured to adjust
the process control parameters in real-time as necessary to
maximize a reward function (or to minimize a loss function), as
will be discussed in more detail below.
[0141] Machine learning algorithms for automated deposition process
control: Any of a variety of machine learning algorithms may be
used in implementing the disclosed process control methods, and may
be the same or different from those used to implement the automated
object defect classification methods described above. The machine
learning algorithm employed may comprise a supervised learning
algorithm, an unsupervised learning algorithm, a semi-supervised
learning algorithm, a reinforcement learning algorithm, a deep
learning algorithm, or any combination thereof. In preferred
embodiments, the machine learning algorithm employed may comprise
an artificial neural network algorithm, a Gaussian process
regression algorithm, a logistical model tree algorithm, a random
forest algorithm, a fuzzy classifier algorithm, a decision tree
algorithm, a hierarchical clustering algorithm, a k-means
algorithm, a fuzzy clustering algorithm, a deep Boltzmann machine
learning algorithm, a deep convolutional neural network algorithm,
a deep recurrent neural network, or any combination thereof, some
of which will be described in more detail below.
[0142] Reward functions and loss functions: As noted above, in some
embodiments the machine learning algorithm used to run the
automated process control may be configured to adjust the process
control parameters in real-time as necessary to maximize a reward
function (or to minimize a loss function) in order to optimize the
deposition process. As used herein, a reward function (or
conversely, a loss function (sometimes also referred to as a cost
function or error function)) refers to a function that maps the
values of one or more additive manufacturing process variables
and/or fabrication event outcomes to a real number that represents
the "reward" associated with a given fabrication event (or the
"cost" in the case of a loss function). Examples of process
parameters and fabrication event outcomes that may be used in
defining a reward (or loss) function include, but are not limited
to, process throughput (e.g. number of parts fabricated per unit
time), process yield (e.g., the percentage of parts produced that
meet a specified set of quality criteria), production quality
(e.g., mean squared deviation in part dimension(s) between the
parts produced and an ideal, defect-free reference part, or the
average number of defects detected per part produced), production
cost (e.g., the cost per part produced), and the like. In some
cases, the definition of the reward function (or loss function) to
be maximized (or minimized) may be dependent on the choice of
machine learning algorithm used to run the process control method,
and vice versa. For example, if the objective is to maximize a
total reward/value function, a reinforcement learning algorithm may
be chosen. If the objective is to minimize a mean squared error
cost (or loss) function, a decision tree regression algorithm or
linear regression algorithm may be chosen. In general, the machine
learning algorithm used to run the process control method will seek
to optimize the reward function (or minimize the loss function) by
(i) identifying the current "state" of the part under fabrication
(e.g., based on the real-time stream of process characterization
data supplied by one or more sensors), (ii) comparing the current
"state" to the design target (or reference "state"), and (iii)
adjusting one or more process control parameters in order to
minimize the difference between the two states (e.g., based on past
"learning" provided by the training data set).
[0143] FIG. 8 illustrates an action prediction--reward loop for a
reinforcement learning algorithm according to some embodiments of
the disclosed deposition or welding process control methods. In the
case of a deposition process, for example, at any point in time
during or following completion of layer deposition (action
a.sub.j), the part being fabricated is monitored using any of a
variety of sensors, measuring tools, inspection tools, and/or
machine vision systems as described above to determine the current
build "state" of the part (state s.sub.j). In a preferred
embodiment, the part is monitored in real-time using an automated
object defect classification system as disclosed herein. Once the
current build state of the part has been determined, a
reinforcement learning algorithm uses the current state
information, s.sub.j, and the model developed using past training
data to predict a proposed action, a.sub.j+1, (e.g., a set or
sequence of process control parameter adjustments) that will
maximize a reward function. If the current build state, s.sub.j, is
relatively poor (i.e., associated with a low value of the reward
function), it may not be desirable to simply take the set of
actions that produces the highest reward in the next build state,
s.sub.j+1, because that may not produce the maximum reward in the
long run. In some cases, maximizing the reward for the immediate
next build state, s.sub.j+1, may force a decision between very low
reward states for next few build states, e.g., s.sub.j+2,
s.sub.j+3, s.sub.+4, thereafter. By using the learned process model
to look a bit further into the future, one can optimize the process
control parameter adjustments for the next N build states as
opposed to just the immediate next state. Each set of "next N
states" starting from state s.sub.i has a corresponding reward
(i.e., the reward space for the next N actions) that can be
predicted using the previously trained model that predicts the
correlation between actions and their resulting state. Thus, the
learned model may be used to determine a sequence of actions that
optimizes the sum (or weighted sum) of reward values for the next N
states. The loop is repeated until the part is complete, and
provides adaptive control of the deposition process to provide for
rapid optimization and adjustment of the process control parameters
used in response to changes in process or environmental parameters,
as well as improved process yield, process throughput, and quality
of the parts.
[0144] FIG. 9 illustrates reward function construction where the
training data used to generate the reward function-based state
prediction model is acquired by monitoring the actions that a human
operator chooses during a manually-controlled deposition process.
In some embodiments, the machine learning algorithm may be wholly
or partially self-trained. For example, in some embodiments, as
part of the training of the machine learning algorithm, the machine
learning algorithm may randomly choose values within a specified
range for each of a set of one or more process control parameters,
and incorporate the resulting process simulation data, process
characterization data, in-process inspection data, post-build
inspection data, or any combination thereof, into the training data
set to improve a learned model that maps process control parameter
values to process outcomes.
[0145] In general, the methods and systems for adaptive, real-time
control of deposition processes that are disclosed herein do not
rely on static data look-up operations (e.g., looking up process
control parameters or process characterization data from previous
runs). Rather, a machine learning algorithm is used to explore a
range of input values for one or more process control parameters
during process simulation and/or actual part fabrication, and
generates a learned model that maps input process control
parameters to process outcomes under a variety of different process
and environmental conditions.
[0146] Process control parameter update rates: In some embodiments,
the one or more sensors may provide data to the processor
programmed to run a machine learning algorithm so that one or more
process control parameters may be adjusted at an update rate of at
least 0.1 Hz, 1 Hz, 5 Hz, 10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz,
70 Hz, 80 Hz, 90 Hz, 100 HZ, 250 Hz, 500 Hz, 750 Hz, 1,000 Hz,
2,500 Hz, 5,000 Hz, 10,000 Hz, or higher. Those of skill in the art
will recognize that the one or more process control parameters may
be adjusted or updated at a rate having any value within this
range, e.g., about 8,000 Hz.
Machine Learning Algorithms for Adaptive Process Control:
[0147] As noted above, the machine learning algorithm(s) employed
in the disclosed automated defect classification and additive
manufacturing process control methods may comprise a supervised
learning algorithm, an unsupervised learning algorithm, a
semi-supervised learning algorithm, a reinforcement learning
algorithm, a deep learning algorithm, or any combination
thereof.
[0148] Supervised learning algorithms: In the context of the
present disclosure, supervised learning algorithms are algorithms
that rely on the use of a set of labeled training data to infer the
relationship between a set of one or more defects identified for a
given object and a classification of the object according to a
specified set of quality criteria, or to infer the relationship
between a set of input additive manufacturing process control
parameters and a set of desired fabrication outcomes. The training
data comprises a set of paired training examples, e.g., where each
example comprises a set of defects detected for a given object and
the resultant classification of the given object, or where each
example comprises a set of process control parameters that were
used in a fabrication process that is paired with the known outcome
of the fabrication process.
[0149] Unsupervised learning algorithms: In the context of the
present disclosure, unsupervised learning algorithms are algorithms
used to draw inferences from training datasets consisting of object
defect datasets that are not paired with labeled object
classification data, or input additive manufacturing process
control parameter data that are not paired with labeled fabrication
outcomes. The most commonly used unsupervised learning algorithm is
cluster analysis, which is often used for exploratory data analysis
to find hidden patterns or groupings in process data.
[0150] Semi-supervised learning algorithms: In the context of the
present disclosure, semi-supervised learning algorithms are
algorithms that make use of both labeled and unlabeled object
classification or additive manufacturing process data for training
(typically using a relatively small amount of labeled data with a
large amount of unlabeled data).
[0151] Reinforcement learning algorithms: In the context of the
present disclosure, reinforcement learning algorithms are
algorithms which are used, for example, to determine a set of
additive manufacturing process steps (or actions) that should be
taken so as to maximize a specified fabrication process reward
function. In machine learning environments, reinforcement learning
algorithms are often formulated as Markov decision processes.
Reinforcement learning algorithms differ from supervised learning
algorithms in that correct training data input/output pairs are
never presented, nor are sub-optimal actions explicitly corrected.
These algorithms tend to be implemented with a focus on real-time
performance through finding a balance between exploration of
possible outcomes based on updated input data and exploitation of
past training.
[0152] Deep learning algorithms: In the context of the present
disclosure, deep learning algorithms are algorithms inspired by the
structure and function of the human brain called artificial neural
networks (ANNs), and specifically large neural networks comprising
many layers, that are used to map object defect data to object
classification decisions, or to map input additive manufacturing
process control parameters to desired fabrication outcomes.
Artificial neural networks will be discussed in more detail
below.
[0153] Decision tree-based expert systems: In the context of the
present disclosure, expert systems are one example of supervised
learning algorithms that are designed to solve object defect
classification problems or additive manufacturing process control
problems by applying a series of if--then rules. Expert systems
typically comprise two subsystems: an inference engine and a
knowledge base. The knowledge base comprises a set of facts (e.g.,
a training data set comprising object defect data for a series of
fabricated parts, and the associated object classification data
provided by a skilled operator, technician, or inspector) and
derived rules (e.g., derived object classification rules). The
inference engine then applies the rules to data for a current
object classification problem or process control problem to
determine a classification of the object or a next set of process
control adjustments.
[0154] Support vector machines (SVMs): In the context of the
present disclosure, support vector machines are supervised learning
algorithms used for classification and regression analysis of
object defect classification date or additive manufacturing process
control. Given a set of training data examples (e.g., object defect
data), each marked as belonging to one or the other of two
categories (e.g., good or bad, pass or fail), an SVM training
algorithm builds a model that assigns new examples (e.g., defect
data for a newly fabricated object) to one category or the
other.
[0155] Autoencoders: In the context of the present disclosure, an
autoencoder (also sometimes referred to as an autoassociator or
Diabolo network) is an artificial neural network used for
unsupervised, efficient mapping of input data, e.g., object defect
data, to an output value, e.g., an object classification.
Autoencoders are often used for the purpose of dimensionality
reduction, i.e., the process of reducing the number of random
variables under consideration by deducing a set of principal
component variables. Dimensionality reduction may be performed, for
example, for the purpose of feature selection (i.e., a subset of
the original variables) or feature extraction (i.e., transformation
of data in a high-dimensional space to a space of fewer
dimensions).
[0156] Artificial neural networks (ANNs): In some cases, the
machine learning algorithm used for the disclosed automated object
defect classification or adaptive process control methods may
comprise an artificial neural network (ANN), e.g., a deep machine
learning algorithm. The automated object classification methods of
the present disclosure may, for example, employ an artificial
neural network to map object defect data to object classification
data. The additive manufacturing process control systems of the
present disclosure may, for example, employ an artificial neural
network (ANN) to determine an optimal set or sequence of process
control parameter settings for adaptive control of an additive
manufacturing process in real-time based on a stream of process
monitoring data and/or object defect classification data provided
by one or more sensors. The artificial neural network may comprise
any type of neural network model, such as a feedforward neural
network, radial basis function network, recurrent neural network,
or convolutional neural network, and the like. In some embodiments,
the automated object defect classification and additive
manufacturing process control methods and systems of the present
disclosure may employ a pre-trained ANN architecture. In some
embodiment, the automated object defect classification and additive
manufacturing process control methods and systems of the present
disclosure may employ an ANN architecture wherein the training data
set is continuously updated with real-time object classification
data or real-time deposition process control and monitoring data
from a single local system, from a plurality of local systems, or
from a plurality of geographically distributed systems.
[0157] As used throughout this disclosure, the term "real-time"
refers to the rate at which sensor data (e.g. process control data,
process monitoring data, and/or object defect identification and
classification data) is acquired, processed, and/or used by a
machine learning algorithm, e.g., an artificial neural network or
deep machine learning algorithm, to update a prediction of object
classification or a prediction of optimal process control
parameters in response to changes in one or more of the input
sensor data streams. In general, the update rate for the object
classification or process control parameters provided by the
disclosed object defect classification and additive manufacturing
process control methods and systems may range from about 0.1 Hz to
about 10,000 Hz. In some embodiments, the update rate may be at
least 0.1 Hz, at least 1 HZ, at least 10 Hz, at least 50 Hz, at
least 100 Hz, at least 250 Hz, at least 500 Hz, at least 750 Hz, at
least 1,000 Hz, at least 2,000 Hz, at least 3,000 Hz, at least
4,000 Hz, at least 5,000 Hz, or at least 10,000 Hz. In some
embodiments, the update rate may be at most 10,000 Hz, at most
5,000 Hz, at most 4,000 Hz, at most 3,000 Hz, at most 2,000 Hz, at
most 1,000 Hz, at most 750 Hz, at most 500 Hz, at most 250 Hz, at
most 100 Hz, at most 50 Hz, at most 10 Hz, at most 1 Hz, or at most
0.1 Hz. Those of skill in the art will recognize that the update
rate may have any value within this range, for example, about 8,000
Hz.
[0158] Artificial neural networks generally comprise an
interconnected group of nodes organized into multiple layers of
nodes (see FIG. 10). For example, the ANN architecture may comprise
at least an input layer, one or more hidden layers, and an output
layer. The ANN may comprise any total number of layers, and any
number of hidden layers, where the hidden layers function as
trainable feature extractors that allow mapping of a set of input
data to a preferred output value or set of output values. Each
layer of the neural network comprises a number of nodes (or
neurons). A node receives input that comes either directly from the
input data (e.g., sensor data, image data, object defect data,
etc., in the case of the presently disclosed methods) or the output
of nodes in previous layers, and performs a specific operation,
e.g., a summation operation. In some cases, a connection from an
input to a node is associated with a weight (or weighting factor).
In some cases, the node may sum up the products of all pairs of
inputs, x.sub.i, and their associated weights, w.sub.i (FIG. 11).
In some cases, the weighted sum is offset with a bias, b, as
illustrated in FIG. 11. In some cases, the output of a neuron may
be gated using a threshold or activation function, f, which may be
a linear or non-linear function. The activation function may be,
for example, a rectified linear unit (ReLU) activation function or
other function such as a saturating hyperbolic tangent, identity,
binary step, logistic, arcTan, softsign, parameteric rectified
linear unit, exponential linear unit, softPlus, bent identity,
softExponential, Sinusoid, Sinc, Gaussian, or sigmoid function, or
any combination thereof.
[0159] The weighting factors, bias values, and threshold values, or
other computational parameters of the neural network, can be
"taught" or "learned" in a training phase using one or more sets of
training data. For example, the parameters may be trained using the
input data from a training data set and a gradient descent or
backward propagation method so that the output value(s) (e.g., a
set of predicted adjustments to process control parameter settings)
that the ANN computes are consistent with the examples included in
the training data set. The parameters may be obtained from a back
propagation neural network training process that may or may not be
performed using the same hardware as that used for automated object
defect classification or adaptive, real-time deposition process
control.
[0160] Other specific types of deep machine learning algorithms,
e.g., convolutional neural networks (CNNs) (e.g., for the
processing of image data from machine vision systems) may also be
used by the disclosed methods and systems. CNN are commonly
composed of layers of different types: convolution, pooling,
upscaling, and fully-connected node layers. In some cases, an
activation function such as rectified linear unit may be used in
some of the layers. In a CNN architecture, there can be one or more
layers for each type of operation performed. A CNN architecture may
comprise any number of layers in total, and any number of layers
for the different types of operations performed. The simplest
convolutional neural network architecture starts with an input
layer followed by a sequence of convolutional layers and pooling
layers, and ends with fully-connected layers. Each convolution
layer may comprise a plurality of parameters used for performing
the convolution operations. Each convolution layer may also
comprise one or more filters, which in turn may comprise one or
more weighting factors or other adjustable parameters. In some
instances, the parameters may include biases (i.e., parameters that
permit the activation function to be shifted). In some cases, the
convolutional layers are followed by a layer of ReLU activation
function. Other activation functions can also be used, for example
the saturating hyperbolic tangent, identity, binary step, logistic,
arcTan, softsign, parameteric rectified linear unit, exponential
linear unit, softPlus, bent identity, softExponential, Sinusoid,
Sinc, Gaussian, the sigmoid function and various others. The
convolutional, pooling and ReLU layers may function as learnable
features extractors, while the fully connected layers may function
as a machine learning classifier.
[0161] As with other artificial neural networks, the convolutional
layers and fully-connected layers of CNN architectures typically
include various computational parameters, e.g., weights, bias
values, and threshold values, that are trained in a training phase
as described above.
[0162] In general, the number of nodes used in the input layer of
the ANN (which enable input of data from multiple sensor data
streams and/or, for example, sub-sampling of an image frame) may
range from about 10 to about 10,000 nodes. In some instances, the
number of nodes used in the input layer may be at least 10, at
least 50, at least 100, at least 200, at least 300, at least 400,
at least 500, at least 600, at least 700, at least 800, at least
900, at least 1000, at least 2000, at least 3000, at least 4000, at
least 5000, at least 6000, at least 7000, at least 8000, at least
9000, or at least 10,000. In some instances, the number of nodes
used in the input layer may be at most 10,000, at most 9000, at
most 8000, at most 7000, at most 6000, at most 5000, at most 4000,
at most 3000, at most 2000, at most 1000, at most 900, at most 800,
at most 700, at most 600, at most 500, at most 400, at most 300, at
most 200, at most 100, at most 50, or at most 10. Those of skill in
the art will recognize that the number of nodes used in the input
layer may have any value within this range, for example, about 512
nodes.
[0163] In some instance, the total number of layers used in the ANN
(including input and output layers) may range from about 3 to about
20. In some instance the total number of layers may be at least 3,
at least 4, at least 5, at least 10, at least 15, or at least 20.
In some instances, the total number of layers may be at most 20, at
most 15, at most 10, at most 5, at most 4, or at most 3. Those of
skill in the art will recognize that the total number of layers
used in the ANN may have any value within this range, for example,
8 layers.
[0164] In some instances, the total number of learnable or
trainable parameters, e.g., weighting factors, biases, or threshold
values, used in the ANN may range from about 1 to about 10,000. In
some instances, the total number of learnable parameters may be at
least 1, at least 10, at least 100, at least 500, at least 1,000,
at least 2,000, at least 3,000, at least 4,000, at least 5,000, at
least 6,000, at least 7,000, at least 8,000, at least 9,000, or at
least 10,000. Alternatively, the total number of learnable
parameters may be any number less than 100, any number between 100
and 10,000, or a number greater than 10,000. In some instances, the
total number of learnable parameters may be at most 10,000, at most
9,000, at most 8,000, at most 7,000, at most 6,000, at most 5,000,
at most 4,000, at most 3,000, at most 2,000, at most 1,000, at most
500, at most 100 at most 10, or at most 1. Those of skill in the
art will recognize that the total number of learnable parameters
used may have any value within this range, for example, about 2,200
parameters.
Integrated and Distributed Additive Manufacturing Systems:
[0165] In some embodiments, the adaptive, real-time process control
methods of the present disclosure may be used for integrated
additive manufacturing and/or welding systems (i.e., free form
deposition or joining systems) that reside at a single
physical/geographical location. FIG. 12 provides a schematic
illustration of an integrated additive manufacturing system
comprising a deposition apparatus, one or more machine vision
systems and/or other process monitoring tools, process simulation
tools, post-build inspection tools, and one or more processors for
running a machine learning algorithm that utilizes data from the
process simulation tools, machine vision and/or process monitoring
tools (including in-process inspection and/or defect classification
tools), post-build inspection tools, or any combination thereof, to
provide real-time adaptive control of the deposition process, where
the components of the system are located in the same
physical/geographical location. In these embodiments, the processor
may communicate with the individual system components through
direct, hard-wired connections and/or via short-range communication
links such as Bluetooth or Wi-Fi connections. In some embodiments,
two or more of the system components may be housed within an
enclosure or housing (dashed line) that enables tighter control of
fabrication environmental parameters such as temperature, pressure,
atmospheric composition, etc.
[0166] FIG. 13 provides a schematic illustration of a distributed
free form deposition system, e.g., an additive manufacturing
system, comprising one or more deposition apparatus, process
simulation tools, machine vision systems and/or other process
monitoring tools, in-process inspection tools, post-build
inspection tools, and one or more processors for running a machine
learning algorithm that utilizes data from the machine vision
and/or process monitoring tools, the process simulation tools, the
post-build inspection tools, or any combination thereof, to provide
real-time adaptive control of the deposition process, where the
different components or modules of the system may be physically
located in different workspaces and/or worksites (i.e. different
physical/geographical locations), and may be linked via a local
area network (LAN), an intranet, an extranet, or the internet so
that process data (e.g., training data, process simulation data,
process control data, in-process inspection data, and/or post-build
inspection data) and process control instructions may be shared and
exchanged between the different modules. In some embodiments, some
of the co-localized system components (e.g., a deposition apparatus
and a process monitoring tool) may be housed within a local
enclosure or housing (not shown) that enables tighter control of
fabrication environmental parameters such as temperature, pressure,
atmospheric composition, etc.
[0167] For distributed systems, the sharing of data between one or
more deposition apparatus, one or more process monitoring sensors,
machine vision systems, and/or in-process inspection tools may be
facilitated through the use of a data compression algorithm, a data
feature extraction algorithm, or a data dimensionality reduction
algorithm. FIG. 14 illustrates one non-limiting example of an
unsupervised ANN-based approach to image feature extraction and
data compression, whereby image data is conveniently compressed,
transmitted, and reconstructed at a different physical/geographical
location from that at which it was acquired.
Processors & Computer Systems:
[0168] One or more processors may be employed to implement the
machine learning algorithms, automated object defect classification
methods, and additive manufacturing process control methods
disclosed herein. The one or more processors may comprise a
hardware processor such as a central processing unit (CPU), a
graphic processing unit (GPU), a general-purpose processing unit,
or computing platform. The one or more processors may be comprised
of any of a variety of suitable integrated circuits,
microprocessors, logic devices and the like. Although the
disclosure is described with reference to a processor, other types
of integrated circuits and logic devices may also be applicable.
The processor may have any suitable data operation capability. For
example, the processor may perform 512 bit, 256 bit, 128 bit, 64
bit, 32 bit, or 16 bit data operations. The one or more processors
may be single core or multi core processors, or a plurality of
processors configured for parallel processing.
[0169] The one or more processors, or the automated additive
manufacturing deposition apparatus and control system itself, may
be part of a larger computer system and/or may be operatively
coupled to a computer network (a "network") with the aid of a
communication interface to facilitate transmission of and sharing
of data and predictive results. The network may be a local area
network, an intranet and/or extranet, an intranet and/or extranet
that is in communication with the Internet, or the Internet. The
network in some cases is a telecommunication and/or data network.
The network may include one or more computer servers, which in some
cases enables distributed computing, such as cloud computing. The
network, in some cases with the aid of the computer system, may
implement a peer-to-peer network, which may enable devices coupled
to the computer system to behave as a client or a server.
[0170] The computer system may also include memory or memory
locations (e.g., random-access memory, read-only memory, flash
memory), electronic storage units (e.g., hard disks), communication
interfaces (e.g., network adapters) for communicating with one or
more other systems, and peripheral devices, such as cache, other
memory, data storage and/or electronic display adapters. The
memory, storage units, interfaces and peripheral devices may be in
communication with the one or more processors, e.g., a CPU, through
a communication bus, e.g., as is found on a motherboard. The
storage unit(s) may be data storage unit(s) (or data repositories)
for storing data.
[0171] The one or more processors, e.g., a CPU, execute a sequence
of machine-readable instructions, which are embodied in a program
(or software). The instructions are stored in a memory location.
The instructions are directed to the CPU, which subsequently
program or otherwise configure the CPU to implement the methods of
the present disclosure. Examples of operations performed by the CPU
include fetch, decode, execute, and write back. The CPU may be part
of a circuit, such as an integrated circuit. One or more other
components of the system may be included in the circuit. In some
cases, the circuit is an application specific integrated circuit
(ASIC).
[0172] The storage unit stores files, such as drivers, libraries
and saved programs. The storage unit stores user data, e.g.,
user-specified preferences and user-specified programs. The
computer system in some cases may 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.
[0173] Some aspects of the methods and systems provided herein,
such as the disclosed object defect classification or additive
manufacturing process control algorithms, are implemented by way of
machine (e.g., processor) executable code stored in an electronic
storage location of the computer system, such as, for example, in
the memory or electronic storage unit. The machine executable or
machine readable code is provided in the form of software. During
use, the code is executed by the one or more processors. In some
cases, the code is retrieved from the storage unit and stored in
the memory for ready access by the one or more processors. In some
situations, the electronic storage unit is precluded, and
machine-executable instructions are stored in memory. The code may
be pre-compiled and configured for use with a machine having one or
more processors adapted to execute the code, or may be compiled at
run time. The code may be supplied in a programming language that
is selected to enable the code to execute in a pre-compiled or
as-compiled fashion.
[0174] Various aspects of the technology may be thought of as
"products" or "articles of manufacture" typically in the form of
machine (or processor) executable code and/or associated data that
is stored in a type of machine readable medium. Machine-executable
code may be stored in an optical storage unit comprising an
optically readable medium such as an optical disc, CD-ROM, DVD, or
Blu-Ray disc. Machine-executable code may be stored in an
electronic storage unit, such as memory (e.g., read-only memory,
random-access memory, flash memory) or on a hard disk. "Storage"
type media include any or all of the tangible memory of the
computers, processors or the like, or associated modules thereof,
such as various semiconductor memory chips, optical drives, tape
drives, disk drives and the like, which may provide non-transitory
storage at any time for the software that encodes the methods and
algorithms disclosed herein.
[0175] All or a portion of the software code may at times be
communicated via the Internet or various other telecommunication
networks. Such communications, for example, 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, other types of media that
are used to convey the software encoded instructions include
optical, electrical and electromagnetic waves, such as those used
across physical interfaces between local devices, through wired and
optical landline networks, and over various atmospheric links. The
physical elements that carry such waves, such as wired or wireless
links, optical links, or the like, are also considered media that
convey the software encoded instructions for performing the methods
disclosed herein. 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.
[0176] The computer system typically includes, or may be in
communication with, an electronic display for providing, for
example, images captured by a machine vision system. The display is
typically also capable of providing a user interface (UI). Examples
of UI's include, but are not limited to, graphical user interfaces
(GUIs), web-based user interfaces, and the like.
Applications:
[0177] The disclosed automated object defect classification and
adaptive, real-time free form deposition or joining (including
additive manufacturing and welding) process control methods and
systems may be used in any of a variety of industrial applications
including but not limited to, the fabrication of parts and
assemblies in the automotive industry, the aeronautics industry,
the medical device industry, the consumer electronics industry,
etc. For example, high volume applications for welding processes
include use in the automotive industry for welding car bodies, as
well as use in the oil and gas industry for construction of wells
and refineries, and in the marine (shipbuilding) industry.
EXAMPLES
[0178] These examples are provided for illustrative purposes only
and not intended to limit the scope of the claims provided
herein.
Prophetic Example 1
Automated Object Defect Classification
[0179] The machine learning algorithm-based automated object defect
classification methods and systems disclosed herein provide a key
component for enabling adaptive, real-time additive manufacturing
(or welding) process control. The methods comprise the use of a
machine learning algorithm to analyze in-process or post-build
inspection data for the purpose of identifying object defects and
classifying them according to a specified set of fabrication
quality criteria, and in some embodiments, further provide input
data for real-time adaptive process control.
[0180] FIG. 15 provides a schematic illustration of the expected
outcome for an unsupervised machine learning process for
classification of object defects. One or more automated inspection
tools, e.g., machine vision systems coupled with automated image
processing algorithms, are used to monitor and measure feature
dimensions, angles, surface finishes, and/or other properties of
fabricated parts both in-process and post-build. Defects may be
identified, e.g., by removing noise from the inspection data and
subtracting a reference data set (e.g., a reference image of a
defect-free part in the case that machine vision tools are being
utilized for inspection), and classified using an unsupervised
machine learning algorithm such as cluster analysis or an
artificial neural network, to classify individual objects as either
meeting or failing to meet a specified set of decision criteria
(e.g., a decision boundary) in the feature space in which defects
are being monitored. Tracking of the process control parameters and
process monitoring data that were used to fabricate a set of
objects (including both those that met the decision criteria and
those that did not) provides training data for the machine learning
algorithm used to run fabrication process control.
Prophetic Example 2
Adaptive, Real-Time Additive Manufacturing Process Control
[0181] FIG. 10 shows one non-limiting example of an ANN
architecture used for real-time, adaptive process control of an
additive manufacturing (or welding) process. In FIG. 10, the input
layer comprises one or more real-time streams of process and/or
object property data that provide an indication of the current
state of the fabrication process and/or the part being fabricated.
Examples of suitable input data streams include, but are not
limited to, process simulation data (e.g., FEA simulation data),
process monitoring or characterization data, in-process inspection
data, post-build inspection data, or any combination thereof, as
well as a list of process control parameters that may be adjusted
to implement next step actions to achieve a target (or future)
fabrication state. This data is fed to the ANN, which in many cases
has been previously trained using one or more training data sets
comprising process simulation data, process monitoring or
characterization data, in-process inspection data, post-build
inspection data, or any combination thereof, from previous
fabrication runs of the same or different types of parts. The
hidden or intermediate layers of the ANN act as trained feature
extractors, while the output layer in the example of FIG. 10
provides a determination of a predicted future build state. As
noted above, the ANN model is trained to predict future build state
based on current build state and a set of actions. Once the ANN
model has been developed (i.e., the model can map current state and
process parameters to a future state) its use can be extended to
the determination of a set of process control parameter adjustments
for the next N states. The ANN model is a first step in creating an
action-value function, and determining the next sequence of actions
for a given build step (as depicted in FIG. 8) is a second step in
developing adaptive, real-time process control.
[0182] In some embodiments, a neural network model may be used
directly to determine adjustments to process control parameters.
This will typically involve a more difficult "training" or
"learning" process. Initially, the machine is allowed to choose
randomly from a range of values for each input process control
parameter or action. If the sequence of process control parameter
adjustments or actions leads to a flaw or defect, it is scored as
leading to an undesirable (or negative) outcome. Repetition of the
process using different sets of randomly chosen values for each
process control parameter or action leads to reinforcement of those
sequences that least to desirable (or positive) outcomes.
Ultimately, the neural network model "learns" what adjustments to
make to a set or sequence of deposition process control parameters
or actions in order to achieve the target outcome, i.e., a
defect-free printed part.
Example 3
Post-Process Image Feature Extraction and Correlation with
Build-Time Actions
[0183] FIGS. 16A-C provide an example of in-process and
post-process image feature extraction and correlation of part
features with build-time actions. FIG. 16A: image of the part after
the build process has been completed. FIG. 16B: example of
post-build inspection output (in this case, a computerized
tomography (CT) scan of the part). FIG. 16C: image obtained using a
feature extraction algorithm to process the CT scan shown in FIG.
16B. In some embodiments, automated feature extraction allows one
to correlate part features with build-time actions. During the
build (e.g., when printing), in addition to building a machine
learning model that correlates process control parameters (e.g.,
laser power, feed rate, travel speed, etc.) and result of the
deposition process (e.g., the shape of melt pool, defects in the
melt pool, etc.), one may also create a mapping between the process
control parameters and a specific location in the part. This allows
one to subsequently index post-build inspection data on the part
and correlate findings from post-build inspection with process
control parameters that are specific to a region of interest,
thereby expanding the machine learning model to include post-build
inspection data.
[0184] 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. Numerous variations, changes, and substitutions will
now 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 may be employed in
any combination in practicing the invention. 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.
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