U.S. patent application number 17/713709 was filed with the patent office on 2022-07-21 for in situ monitoring of stress for additively manufactured components.
The applicant listed for this patent is Hamilton Sundstrand Corporation. Invention is credited to Abhijit Chakraborty, Joseph V. Mantese.
Application Number | 20220227062 17/713709 |
Document ID | / |
Family ID | |
Filed Date | 2022-07-21 |
United States Patent
Application |
20220227062 |
Kind Code |
A1 |
Mantese; Joseph V. ; et
al. |
July 21, 2022 |
IN SITU MONITORING OF STRESS FOR ADDITIVELY MANUFACTURED
COMPONENTS
Abstract
A material deposition process including in situ sensor analysis
of a component in a formation state is provided. The material
deposition process is implemented in part by a sensor device of an
additive manufacturing machine producing the component. The
material deposition process includes sensing, by the sensing
device, in situ physical properties of an area of interest of the
component during a three-dimensional object production. Compliance
to specifications or defects are then detected in the in situ
physical properties with respect to pre-specified material
requirements. The defects are analyzed to determine corrective
actions, and an updated three-dimensional object production, which
includes the corrective actions, is implemented to complete the
component.
Inventors: |
Mantese; Joseph V.;
(Ellington, CT) ; Chakraborty; Abhijit; (West
Hartford, CT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Hamilton Sundstrand Corporation |
Charlotte |
NC |
US |
|
|
Appl. No.: |
17/713709 |
Filed: |
April 5, 2022 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
16166931 |
Oct 22, 2018 |
11292198 |
|
|
17713709 |
|
|
|
|
International
Class: |
B29C 64/393 20060101
B29C064/393; B29C 64/264 20060101 B29C064/264; G01L 1/25 20060101
G01L001/25; G01N 23/20008 20060101 G01N023/20008 |
Claims
1. A system for implementing a three-dimensional object production
of a component via an additive manufacturing, the system
comprising: an additive manufacturing machine comprising an X-ray
source and an X-ray detector; and a compute device comprising a
processor and a memory, the compute device being communicatively
coupled to the additive manufacturing machine and the X-ray source
and the X-ray detector, wherein the additive manufacturing machine
and the compute device provide in situ sensor analysis of the
component while in a formation state during a material deposition
process of the additive manufacturing by: sensing, by the X-ray
source and the X-ray detector, in situ physical properties at an
area of interest of the component during the three-dimensional
object production, the X-ray source and the X-ray detector being
axially offset from each other such that the X-ray source and the
X-ray detector are not axially aligned with respect to the area of
interest, wherein the in situ physical properties include hardness,
local strain, yield strength, crystallite size, defect density,
crystalline orientation, or texture; detecting compliance to
specifications or defects in the in situ physical properties with
respect to pre-specified material requirements; analyzing the
defects to determine corrective actions; implementing an updated
three-dimensional object production, which includes the corrective
actions, to complete the component.
2. The system of claim 1, wherein the three-dimensional object
production of the component is implemented according to a computer
design file.
3. The system of claim 1, wherein the compute device feeds forward
and back the corrective actions to the three-dimensional object
production in real time to generate the updated three-dimensional
object production.
4. The system of claim 1, wherein the X-ray source and the X-ray
detector that together acquire a full or partial X-ray diffraction
signal or pattern that is analyzed to determine the in situ
physical properties.
5. The system of claim 4, wherein the in situ physical properties
additionally include density, porosity, or compositional
variation.
6. The system of claim 1, wherein a compute device comprises a
processor executing software to provide one or more process
modeling, toolpath planning, defect detection, layer defect
detection, part defect detection, feedback control, scan path
planning, decision making, and process sensing operations for
detecting the defects.
7. The system of claim 1, wherein a compute device comprises a
database storing and providing the pre-specified material
requirements and a computer design file for detecting the defects
and implementing the three-dimensional object production.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a division of U.S. application Ser. No.
16/166,931 filed Oct. 22, 2018, issued as U.S. Pat. No. 11,292,198,
Issued Apr. 5, 2022, the disclosure of which is incorporated herein
by reference in its entirety.
BACKGROUND
[0002] Conventional additive manufacturing processes have limited
or no closed loop controls and, therefore, rely on final material
property assessments of a finished manufactured part or product.
Specifically, conventional additive manufacturing utilizes post
deposition analysis to provide these assessments.
BRIEF DESCRIPTION
[0003] In accordance with one or more embodiments, a material
deposition process including in situ sensor analysis of a component
in a formation state is provided. The material deposition process
is implemented in part by an X-ray source and an X-ray detector of
an additive manufacturing machine producing the component. The
material deposition process includes sensing, by the X-ray source
and the X-ray detector, in situ physical properties of an area of
interest of the component during a three-dimensional object
production. Compliance to specifications or defects are then
detected in the in situ physical properties with respect to
pre-specified material requirements. The defects are analyzed to
determine corrective actions, and an updated three-dimensional
object production, which includes the corrective actions, is
implemented to complete the component.
[0004] In accordance with one or more embodiments or the material
deposition process embodiment above, the material deposition
process can include implementing the three-dimensional object
production of the component according to a computer design
file.
[0005] In accordance with one or more embodiments or any of the
material deposition process embodiments above, the material
deposition process can include feeding forward and back the
corrective actions to the three-dimensional object production in
real time to generate the updated three-dimensional object
production.
[0006] In accordance with one or more embodiments or any of the
material deposition process embodiments above, the at least one
sensing device can include an X-ray source and X-ray detector that
together acquire a full or partial X-ray diffraction signal or
pattern that is analyzed to determine the in situ physical
properties.
[0007] In accordance with one or more embodiments or any of the
material deposition process embodiments above, the in situ physical
properties can potentially include: hardness, local strain, yield
strength, density, crystallite size, porosity, defect density,
crystalline orientation, texture, and compositional variation.
[0008] In accordance with one or more embodiments or any of the
material deposition process embodiments above, a compute device can
include a processor executing software to provide one or more
process modeling, toolpath planning, defect detection, layer defect
detection, part defect detection, feedback control, scan path
planning, decision making, and process sensing operations for
detecting the defects.
[0009] In accordance with one or more embodiments or any of the
material deposition process embodiments above, a compute device can
include a database storing and providing the pre-specified material
requirements and a computer design file for detecting the defects
and implementing the three-dimensional object production.
[0010] In accordance with one or more embodiments, a system for
implementing a three-dimensional object production of a component
via an additive manufacturing is provided. The system includes an
additive manufacturing machine including an X-ray source and an
X-ray detector. The system also includes a compute device including
a processor and a memory. The compute device is communicatively
coupled to the additive manufacturing machine and the X-ray source
and the X-ray detector. The additive manufacturing machine and the
compute device provide in situ sensor analysis of the component
while in a formation state during a material deposition process of
the additive manufacturing by sensing, by the X-ray source and the
X-ray detector, in situ physical properties of an area of interest
of the component during a three-dimensional object production.
Compliance to specifications or defects are then detected in the in
situ physical properties with respect to pre-specified material
requirements. The defects are analyzed to determine corrective
actions, and an updated three-dimensional object production, which
includes the corrective actions, is implemented to complete the
component.
[0011] In accordance with one or more embodiments or the system
embodiment above, the three-dimensional object production of the
component can be implemented according to a computer design
file.
[0012] In accordance with one or more embodiments or any of the
system embodiments above, the compute device can feed forward and
back the corrective actions to the three-dimensional object
production in real time to generate the updated three-dimensional
object production.
[0013] In accordance with one or more embodiments or any of the
system embodiments above, the at least one sensing device can
include an X-ray source and X-ray detector that together acquire a
full or partial X-ray diffraction signal or pattern that is
analyzed to determine the in situ physical properties.
[0014] In accordance with one or more embodiments or any of the
system embodiments above, the in situ physical properties can
include hardness, local strain, yield strength, density,
crystallite size, porosity, defect density and compositional
variation.
[0015] In accordance with one or more embodiments or any of the
system embodiments above, a compute device can include a processor
executing software to provide one or more process modeling,
toolpath planning, defect detection, layer defect detection, part
defect detection, feedback control, scan path planning, decision
making, and process sensing operations for detecting the
defects.
[0016] In accordance with one or more embodiments or any of the
system embodiments above, a compute device can include a database
storing and providing the pre-specified material requirements and a
computer design file for detecting the defects and implementing the
three-dimensional object production.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The following descriptions should not be considered limiting
in any way. With reference to the accompanying drawings, like
elements are numbered alike:
[0018] FIG. 1 depicts a system according to one or more
embodiments;
[0019] FIG. 2 depicts a process flow according to one or more
embodiments; and
[0020] FIG. 3 depicts a schematic flow according to one or more
embodiments.
DETAILED DESCRIPTION
[0021] A detailed description of one or more embodiments of the
disclosed apparatus and method are presented herein by way of
exemplification and not limitation with reference to the
Figures.
[0022] Turning now to an overview of technologies that are more
specifically relevant to aspects of the invention, as discussed
above, conventional additive manufacturing is rapidly emerging
means of flexible manufacturing. However, part-to-part variation,
non-uniformity of properties across finished manufactured parts or
products, and local or extended defects are significant concerns in
utilizing conventional additive manufacturing for high volume
production. Most conventional additive manufacturing processes have
limited or no closed loop control. Therefore, post deposition
analysis is employed to assess only final material properties of
the finished manufactured part or product relative to
pre-determined materials requirements. Further, post deposition
analysis does not allow a manufacturer to change or adapt
properties during manufacturing.
[0023] Turning now to an overview of the aspects of the invention,
one or more embodiments of the invention address the
above-described shortcomings of the conventional additive
manufacturing by providing, via a system, a method, and/or an
apparatus (referred to as a system, herein, for brevity), material
deposition processes including in situ sensor analysis. The in situ
sensor analysis of the material deposition processes extracts
physical properties of a component in a formation state during its
additive manufacturing. The material deposition processes, then,
feed forward and back these physical properties to the additive
manufacturing for continuous adaptability. The technical effects
and benefits of embodiments of the material deposition processes
herein include determining these physical properties during the
formation state of the component and, thus, enabling corrective
actions, such as altering additive manufacturing depositions, to
achieve pre-specified material requirements.
[0024] Turning now to FIG. 1, a system 100 for implementing the
teachings herein is shown in according to one or more embodiments.
The system 100 implements material deposition processes including
in situ sensor analysis.
[0025] In this embodiment, the system 100 includes a compute device
101. The compute device 101 can be an electronic, computer
framework comprising and/or employing any number and combination of
computing device and networks utilizing various communication
technologies, as described herein. The compute device 101 can be
easily scalable, extensible, and modular, with the ability to
change to different services or reconfigure some features
independently of others.
[0026] The compute device 101 has a processor 102, which can
include one or more central processing units (CPUs). The processor
102, also referred to as a processing circuit, microprocessor,
computing unit, is coupled via a system bus 103 to a system memory
104 and various other components. The system memory 104 includes
read only memory (ROM) and random access memory (RAM). The ROM is
coupled to the system bus 103 and may include a basic input/output
system (BIOS), which controls certain basic functions of the system
100. The RAM is read-write memory coupled to the system bus 103 for
use by the processor 102.
[0027] The compute device 101 includes a hard disk 107, which is an
example of a tangible storage medium readable executable by the
processor 102. The hard disk 107 stores software 108 and database
109. The software 108 is stored as instructions for execution on
the system 100 by the processor 102 (to perform process, such as
the process flows of FIGS. 2-3). The database 109 includes a set of
values of qualitative or quantitative variables organized in
various data structures to support and be used by operations of the
software 108. Examples of operations provided by the software 108
include process modeling, toolpath planning, defect detection,
layer defect detection, part defect detection, feedback control,
scan path planning, decision making, and process sensing. Examples
of items stored on the database 109 include computer design files,
pre-specified material requirements, assessment models, assessment
algorithms, and the like.
[0028] The compute device 101 includes one or more adapters (e.g.,
hard disk controllers, network adapters, graphics adapters, etc.)
that interconnect and support communications between the processor
102, the system memory 104, the hard disk 107, and other components
of the translation system 100 (e.g., peripheral and external
devices). In one or more embodiments of the present invention, the
one or more adapters can be connected to one or more I/O buses that
are connected to the system bus 103 via an intermediate bus bridge,
and the one or more I/O buses can utilize common protocols, such as
the Peripheral Component Interconnect (PCI).
[0029] The compute device 101 includes an interface adapter 110
interconnecting a keyboard, a mouse, a speaker, a microphone, etc.
to the system bus 103. The compute device 101 includes a display
adapter 111 interconnecting the system bus 103 to a display. The
display adapter 111 (and/or the processor 102) can include a
graphics controller to provide graphics performance, such as a
display and management of a graphic user interface. A
communications adapter 113 interconnects the system bus 103 with a
network 120 enabling the translation system 100 to communicate with
other systems, devices, data, and software, such as an additive
manufacturing machine 130.
[0030] The system 100 includes the additive manufacturing machine
130, which further comprises at least one sensor device 131, along
with a processor, a memory, tool/feeder, and other machining parts
that are not shown for brevity. Note that while shown as separate
mechanisms communicating across the network 120, in accordance with
one or more embodiment, the compute device 101 and the additive
manufacturing machine 130 can be integrated into a single
apparatus.
[0031] The additive manufacturing machine 130 is configured to
manufacture a component 140 via the material deposition processes
including in situ sensor analysis. In general, additive
manufacturing is a three-dimensional object production process
utilizing computer design file. In this regard, a variety of
materials, ranging from polymer composites, metals, ceramics, food,
foams, gels, alloys, and the like, are deposited by a tool or
feeder according to the computer design file and heated by an
electric beam to set the material in place. The location of the
deposited materials as the tool or feeder moves according to the
computer design file is referred to as a tool path.
[0032] The at least one sensor device 131 can be any device
including transducer and/or a generator. In general, the transducer
of the sensor device 131 can be any detector converts variations in
a physical quantity into an electrical signal. Examples of physical
quantities can include such as local strain, yield strength,
density, crystallite size, porosity, defect density, crystalline
orientation, texture, compositional variation, temperature, local
porosity, optical density, reflectance (e.g., note that because
some of these quantities are difficult to extract, the sensor
device 131 provides added benefits for in situ analysis). The
generator (also known as a source) of the sensor device 131 can be
any mechanism that, in response to electrical signals, generates a
wave, which itself is detectable or a reflection thereof is
detectable by the transducer. The at least one sensor device 131
can also communicate via any interface, such as a controller area
network (CAN), a local interconnect network (LIN), a direct I/O
interface, an analog to digital (A/D) interface, a digital to
analog (D/A) interface, or any other interface specific to the
input, to the compute device 101 via the network 130, along with a
processor, a memory, and machining parts of the additive
manufacturing machine 130. Note that the at least one sensor device
131 is representative of one or more sensors of the same or varying
type, each of which is capable of extracting physical properties of
the component 140 in a formation state during its additive
manufacturing. Example of the at least one sensor device 131
include, but are not limited to, an X-ray, ultra-violet, visible
light, near-infrared, short-wave infrared, mid-wavelength infrared,
long-wavelength infrared, and terahertz sensors, cameras, and
detectors. In accordance with one or more embodiments, the at least
one sensor device 131 includes an X-ray source and X-ray detector
that together acquire a full or partial X-ray diffraction signal or
pattern that is analyzed to determine the in situ physical
properties. Further, the X-ray source and the X-ray detector can be
directed to detect a small portion of the full X-ray diffraction
pattern, such that a single peak with a particular intensity and
width representing the detection.
[0033] Thus, as configured in FIG. 1, the operations of the
software 108, the database 109, and the additive manufacturing
machine 130 (e.g., the system 100) are necessarily rooted in the
computational ability of the processors therein to overcome and
address the herein-described shortcomings of the conventional
additive manufacturing. In this regard, the software 108 and the
data 109 improve manufacturing operations of the additive
manufacturing machine 130 by reducing and eliminating errors in
manufacturing, part-to-part variation, non-uniformity of
properties, and local or extended defects for high volume
production.
[0034] FIG. 2 depicts a process flow 200 of according to one or
more embodiments. The process flow 200 is an example operation of
implementing material deposition processes including in situ sensor
analysis of the component 140 in a formation state during its
additive manufacturing by the system 100.
[0035] The process flow 200 being at block 210, where the system
100 implements a material deposition process to form the component
140 according to a computer design file. In this regard, the
additive manufacturing machine 130 can receive the computer design
file from the database 109 of the compute device 101 and begin
three-dimensional object production of the component 140.
[0036] At block 220, the system 100 senses in situ physical
properties of the component 140 during the material deposition
process. In accordance with one or more embodiments, the at least
one sensor device 131 is an X-ray detector that acquires an X-ray
diffraction (XRD) pattern while the component 140 is in a formation
state (prior to completion). Various parameters of the XRD pattern
are analyzed by the software 108 of the compute device 101 to
determine the in situ physical properties or material parameters,
such as hardness, local strain, yield strength, density,
crystallite size, porosity, defect density and compositional
variation (among other properties). The XRD pattern can be taken
from any area of interest of the component 140, as directed by the
compute device 101.
[0037] At block 230, the system 100 detects compliance to
specifications or defects of the in situ physical properties with
respect to pre-specified material requirements. In this regard, the
compute device 101 can compare the pre-specified material
requirements of the database 109 to the in situ physical properties
and determine if any defects are present. At block 240, all defects
are analyzed by the system 100 (e.g., by the software 108 of the
compute device 101) to determine whether corrective actions need to
be taken and what those corrective action should be.
[0038] At block 250, the system 100 feeds forward and back the
corrective actions to the material deposition process in real time
for continuous adaptability, thereby updating the material
deposition process (e.g., altering additive manufacturing
depositions) to account for the defects and achieve pre-specified
material requirements. At block 260, the system 100 implements the
material deposition process with the corrective actions to complete
the manufacturing of the component 140.
[0039] Turning now to FIG. 3, a schematic flow 300 is depicted
according to one or more embodiments. The schematic flow 300 is an
example operation of implementing in situ monitoring of stress for
a component (including in situ and post situ process controls) by a
system. The schematic flow 300 is executed by an additive
manufacturing machine 301 comprising an X-ray source 302 and an
X-ray detector 303 (e.g., an example of the sensor device 131 of
FIG. 1) and a computing device 304. To the extent that these items
overlap with the above system 100, further description is not
provided for the sake of brevity.
[0040] In general, the schematic flow 300 depicts a model 305 and a
toolpath planning being received by the additive manufacturing
machine 301 and utilized in a production operation 315 to produce a
component. Due to any number of factors during the production
operation 315, the additive manufacturing machine 301 may produce a
trending component 320. The trending component 320 is note desired
as a final component.
[0041] As shown in FIG. 3, the computing device 304 executes a
sensing phase 330 through a process sensing 322. The process
sensing 322 includes receiving physical properties of the component
while the component is in a formation state. The X-ray source 302
generates X-rays so that an XRD pattern can be taken from any area
of interest by the X-ray detector 303. The physical properties are
communicated by the X-ray detector 303 of the additive
manufacturing machine 301, which is performing the in situ
monitoring. The process sensing 322 further include comparing
pre-specified material requirements to the in situ physical
properties to provide comparison information. The sensing phase 330
and the process sensing 322 can be implemented by software of the
computing device 304.
[0042] Next, the computing device 304 executes a detecting phase
340, which includes a process defect detection 342, a layer defect
detection 344, and a part defect detection 346. The detecting phase
340 identifies defects with respect to errors in the process (e.g.,
the process defect detection 342), defect within one or more layers
(e.g., the layer defect detection 344), and defects across the
component itself (e.g., the part defect detection 346). The
detecting phase 340 and operations therein can be implemented by
software of the computing device 304.
[0043] The computing device 304 also executes a reacting phase 350,
which includes a feedback control 352, a scan path planning 354,
and a decision making 356. The reacting phase 350 and operations
therein can be implemented by software of the computing device 304.
The results of the reacting 350 phase include corrective actions
that are provided to the production operation 315. The corrected
actions can include adjusting an area of interest to determine
where to perform the in situ monitoring (e.g., by the feedback
control 352), adjusting a scan path to accommodate or correct
defects in the trending component 320 (e.g., by the scan path
planning 354), and determining material deposit amounts to
accommodate or correct defects in the trending component 320 (e.g.,
by the decision making 356). The production operation 315 is
improved by the corrective actions from the computing device, such
that the additive manufacturing machine 301 may now produce a
desired component 350.
[0044] The term "about" is intended to include the degree of error
associated with measurement of the particular quantity based upon
the equipment available at the time of filing the application.
[0045] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the present disclosure. As used herein, the singular forms "a",
"an" and "the" are intended to include the plural forms as well,
unless the context clearly indicates otherwise. It will be further
understood that the terms "comprises" and/or "comprising," when
used in this specification, specify the presence of stated
features, integers, steps, operations, elements, and/or components,
but do not preclude the presence or addition of one or more other
features, integers, steps, operations, element components, and/or
groups thereof.
[0046] While the present disclosure has been described with
reference to an exemplary embodiment or embodiments, it will be
understood by those skilled in the art that various changes may be
made and equivalents may be substituted for elements thereof
without departing from the scope of the present disclosure. In
addition, many modifications may be made to adapt a particular
situation or material to the teachings of the present disclosure
without departing from the essential scope thereof. Therefore, it
is intended that the present disclosure not be limited to the
particular embodiment disclosed as the best mode contemplated for
carrying out this present disclosure, but that the present
disclosure will include all embodiments falling within the scope of
the claims.
* * * * *