U.S. patent application number 16/431297 was filed with the patent office on 2019-09-26 for sensor fusion for powder bed manufacturing process control.
The applicant listed for this patent is United Technologies Corporation. Invention is credited to Jesse R. Boyer.
Application Number | 20190291345 16/431297 |
Document ID | / |
Family ID | 53397782 |
Filed Date | 2019-09-26 |
United States Patent
Application |
20190291345 |
Kind Code |
A1 |
Boyer; Jesse R. |
September 26, 2019 |
SENSOR FUSION FOR POWDER BED MANUFACTURING PROCESS CONTROL
Abstract
A system for additive manufacturing a component includes an
additive manufacturing machine for building a component layer by
layer and a first sensor configured to collect a first physical
property data for each layer of the component as each layer is
formed by the additive manufacturing machine. The system also
includes a second sensor configured to collect a second physical
property data for each layer of the component as each layer is
formed by the additive manufacturing machine. A computing device is
operatively connected to the first and second sensors and
configured to receive the first physical property data and the
second physical property data of the component and configured to
compare the first physical property data with the second physical
property data to determine a potential failure mode in the
component.
Inventors: |
Boyer; Jesse R.;
(Middletown, CT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
United Technologies Corporation |
Farmington |
CT |
US |
|
|
Family ID: |
53397782 |
Appl. No.: |
16/431297 |
Filed: |
June 4, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14632857 |
Feb 26, 2015 |
10336007 |
|
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16431297 |
|
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|
|
61991041 |
May 9, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B22F 2003/1056 20130101;
G01J 2005/0077 20130101; B22F 2203/03 20130101; B29C 64/153
20170801; G01J 5/004 20130101; B22F 2003/1057 20130101; B22F 3/1055
20130101; Y02P 10/295 20151101; B33Y 50/02 20141201; B22F 2999/00
20130101; B33Y 10/00 20141201; B33Y 30/00 20141201; B29C 64/393
20170801; Y02P 10/25 20151101; B22F 2999/00 20130101; B22F
2003/1057 20130101; B22F 2203/03 20130101 |
International
Class: |
B29C 64/153 20170101
B29C064/153; B33Y 50/02 20150101 B33Y050/02; B29C 64/386 20170101
B29C064/386; B33Y 10/00 20150101 B33Y010/00; B22F 3/105 20060101
B22F003/105 |
Claims
1. A system for additive manufacturing a component, the system
comprising: an additive manufacturing machine for building a
component layer by layer; a first sensor configured to collect a
first physical property data for each layer of the component as
each layer is formed by the additive manufacturing machine; a
second sensor configured to collect a second physical property data
for each layer of the component as each layer is formed by the
additive manufacturing machine; and a computing device operatively
connected to the first and second sensors and configured to receive
the first physical property data and the second physical property
data of the component and configured to compare the first physical
property data with the second physical property data to determine a
potential failure mode in the component.
2. The system of claim 1, wherein the first sensor is selected from
the group consisting of high speed cameras, grayscale optical
cameras, thermal cameras, fluid flow detectors, gas analyzers,
ultrasonic emitters, and ultrasonic receivers.
3. The system of claim 1, wherein the second sensor is selected
from the group consisting of high speed cameras, grayscale optical
cameras, thermal cameras, fluid flow detectors, gas analyzers,
ultrasonic emitters, and ultrasonic receivers.
4. The system of claim 1, wherein the system further comprises: a
third sensor configured to collect a third physical property data
for each layer of the component.
5. The system of claim 1, wherein the third sensor is selected from
the group consisting of high speed cameras, grayscale optical
cameras, thermal cameras, fluid flow detectors, gas analyzers,
ultrasonic emitters, and ultrasonic receivers.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This application is a divisional of U.S. application Ser.
No. 14/632,857, titled "Sensor Fusion For Powder Bed Manufacturing
Process Control", filed Feb. 26, 2015. U.S. application Ser. No.
14/632,857 filed Feb. 26, 2015 claims priority to U.S. Provisional
Application No. 61/991,041, filed May 9, 2014 for "Sensor Fusion
For Powder Bed Manufacturing Process" by Jesse R. Boyer.
BACKGROUND
[0002] The present invention relates to additive manufactured parts
and more specifically to predicting potential failure modes in
additive manufactured parts.
[0003] Additive manufacturing is a process in which
three-dimensional parts are formed on a layer-by-layer basis.
Additive manufacturing can be used in place of other manufacturing
methods like casting, forging and machining. Additive manufacturing
can also be used to form parts having fine geometric features that
are difficult to produce by the other methods described above.
[0004] In some additive manufacturing methods, beds of powder are
used to create the layers of a part. A first powder bed is
deposited according to a three-dimensional model and a heat source
melts at least a portion of the first powder bed to form a starting
layer of the part. The heat source can be a laser or an electron
beam. An additional powder bed is then deposited on the first
powder bed and the heat source melts a portion of the additional
powder bed to form an additional layer of the part that is joined
with the starting layer of the part. This process is continued on a
layer-by-layer basis until the final part geometry is achieved.
[0005] The beds of powder and the part generated from the beds of
powder are typically housed within a sealed chamber with a
controlled atmosphere during the additive manufacturing process to
reduce oxidation in the beds of powder. Because the additive
manufacturing process typically occurs within a sealed chamber,
monitoring the part for possible defects as the part is
manufactured is difficult as there is limited space and access
within the sealed chamber for sensors. Furthermore, the part is
substantially covered by the beds of powder throughout the
manufacturing process, obstructing the part from visual inspection
as the part is manufactured.
SUMMARY
[0006] In one aspect, a method for additive manufacturing a
component includes forming a first layer of the component by
sintering at least a portion of a first bed of metal powder and
collecting first physical property data of the first layer with a
first sensor. Second physical property data of the first layer is
collected with a second sensor. The method also includes forming a
second layer of the component by sintering at least a portion of a
second bed of metal powder disposed proximate the first layer.
First physical property data of the second layer is collected with
the first sensor and second physical property data of the second
layer is collected with the second sensor. The method further
includes aggregating the first physical property data of the first
layer and the second layer of the component, and aggregating the
second physical property data of the first layer and the second
layer of the component. A model is calculated based on the
aggregated first physical property data and the aggregated second
physical property data from which to determine a potential failure
mode in the component.
[0007] In another aspect, a system for additive manufacturing a
component includes an additive manufacturing machine for building a
component layer by layer and a first sensor configured to collect a
first physical property data for each layer of the component as
each layer is formed by the additive manufacturing machine. The
system also includes a second sensor configured to collect a second
physical property data for each layer of the component as each
layer is formed by the additive manufacturing machine. A computing
device is operatively connected to the first and second sensors and
configured to receive the first physical property data and the
second physical property data of the component and configured to
compare the first physical property data with the second physical
property data to determine a potential failure mode in the
component.
[0008] In another aspect, a method for additive manufacturing a
component includes forming a first layer of the component by an
additive manufacturing machine and collecting first physical
property data of the first layer with a first sensor. Second
physical property data of the first layer is collected with a
second sensor. The first physical property data of the first layer
and the second physical property data of the first layer are
compared to inspect the first layer for a defect. The method
further comprises forming a second layer of the component by the
additive manufacturing machine and collecting first physical
property data of the second layer with the first sensor. Second
physical property data of the second layer is collected with the
second sensor. The first physical property data of the second layer
and the second physical property data of the second layer are
compared to inspect the second layer for a defect. The first
physical property data and the second physical property data of the
first layer is aggregated with the first physical property data and
the second physical property data of the second layer to form a
model of the first physical property and the second physical
property of the component. A potential failure mode in the
component is determined based upon the model of the first physical
property and the second physical property of the component.
[0009] Persons of ordinary skill in the art will recognize that
other aspects and embodiments of the present invention are possible
in view of the entirety of the present disclosure, including the
accompanying figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1A depicts a schematic view of an additive
manufacturing system with a plurality of sensors, the additive
manufacturing system working a first layer of a powder bed.
[0011] FIG. 1B depicts another schematic view of the additive
manufacturing system of FIG. 1A working a second layer of a powder
bed.
[0012] FIG. 1C depicts another schematic view of the additive
manufacturing system of FIG. 1A working a subsequent layer of a
powder bed.
[0013] FIG. 2 depicts an isometric view of a model generated by the
plurality of sensors of the additive manufacturing system of FIG.
1A of a first physical property and a second physical property of a
component formed by the additive manufacturing system of FIG.
1A.
[0014] FIG. 3 depicts a flow chart of a method for forming the
three-dimensional model of FIG. 2.
[0015] While the above-identified drawing figures set forth one or
more embodiments of the invention, other embodiments are also
contemplated. In all cases, this disclosure presents the invention
by way of representation and not limitation. It should be
understood that numerous other modifications and embodiments can be
devised by those skilled in the art, which fall within the scope
and spirit of the principles of the invention. The figures may not
be drawn to scale, and applications and embodiments of the present
invention may include features and components not specifically
shown in the drawings. Like reference numerals identify similar
structural elements.
DETAILED DESCRIPTION
[0016] In at least some embodiments, the present invention relates
generally to a system and method for additive manufacturing a
component and determining a potential failure mode in the
component. The present system and method includes an additive
manufacturing machine with a plurality of sensors for collecting
multiple kinds of physical property data of each layer of the
component as each layer is formed by the additive manufacturing
machine. A computing device aggregates and compares the multiple
kinds of physical property data to generate a model of a potential
failure mode in the component. Persons of ordinary skill in the art
will recognize additional features and benefits in view of the
entirety of the present disclosure, including the accompanying
figures.
[0017] FIGS. 1A-1C will be discussed concurrently. FIGS. 1A-1C
depict a schematic view of system and method 10 for manufacturing
component 12 with additive manufacturing machine 14. As shown in
FIGS. 1A-1C, component 12 can include first layer 18, second layer
20, subsequent layers 21, and sintered metal 22. Additive
manufacturing machine 14 can include first bed 24 of metal powder,
second bed 26 of metal powder, heat source 28 with beam 30 for
forming melt pool 32, first sensor 34, second sensor 36, third
sensor 38, and computing device 40.
[0018] As shown in FIG. 1A, additive manufacturing machine 14 can
begin manufacturing component 12 by depositing first bed 24 of
metal powder. Once additive manufacturing machine 14 has deposited
first bed 24, beam 30 of heat source 28 engages first bed 24 and
melts a portion of the metal powder in first bed 24, thereby
forming melt pool 32. Beam 30 of heat source 28 and melt pool 32
progress across first bed 24 to form sintered metal 22 of first
layer 18 of component 12. Additive manufacturing machine 14 can be
a direct metal laser sintering machine and heat source 28 can be a
laser. Alternatively, additive manufacturing machine 14 can be an
electron beam sintering machine and heat source 28 can be an
electron beam emitter.
[0019] As heat source 28 melts and sinters portions of first bed 24
to form first layer 18 of component 12, first sensor 34 collects
first physical property data of first layer 18, second sensor 36
collects second physical property data of first layer 18, and third
sensor 38 collects third physical property data of first layer 18.
The position of first sensor 34, second sensor 36, and third sensor
38 can be fixed relative first bed 24 and heat source 28 can be
configured to move across first bed 24. Alternatively, heat source
28 can be fixed and mirrors can be used to move beam 30 across
first bed 24. Computing device 40 is operatively connected to first
sensor 34, second sensor 36, and third sensor 38 and is configured
to receive the first, second, and third physical property data of
first layer 18 of component 12. As discussed in greater detail with
reference to FIG. 2, computing device 40 is also configured to
compare the first physical property data, the second physical
property data, and the third physical property data with each other
to determine a potential failure mode in component 12.
[0020] Once first layer 18 of component 12 has been formed, heat
source 28 is deactivated, first bed 24 is incrementally lowered,
and additive manufacturing machine 14 deposits second bed 26 of
metal powder on top of first bed 24 and first layer 18. After
additive manufacturing machine 14 has deposited second bed 26 on
top of first bed 24 and first layer 18, heat source 28 is activated
and engages second bed 26 to form second layer 20 of component 12,
as shown in FIG. 1B. As heat source 28 forms second layer 20 of
component 12 in second bed 26, first sensor 34 collects the first
physical property data of second layer 20, second sensor 36
collects the second physical property data of second layer 20, and
third sensor 38 collects the third physical property data of second
layer 20, and computing device 40 receives the first, second, and
third physical property data. After source 28 has completed forming
second layer 20 of component 12 in second bed 26, additive
manufacturing machine 14 can incrementally lower first bed 24 and
second bed 26 so that additional beds 27 of metal powder and
subsequent layers 21 of component 12 can be formed, as shown in
FIG. 1C. First sensor 34, second sensor 36, and third sensor 38
continue to collect first physical property data, second physical
property data, and third physical property data respectively with
each additional layer added to component 12 by additive
manufacturing machine 14. As discussed below with reference to FIG.
2, computing device 40 can use first, second, and third physical
property data of all of the layers of component 12 to generate
model 16 to determine a possible failure mode of component 12.
[0021] FIG. 2 depicts an isometric view of model 16 of component 12
calculated by computing device 40 based upon the first, second, and
third physical property data collected by first sensor 34, second
sensor 36, and third sensor 38 respectively from each of the layers
of component 12. To improve the likelihood of determining a
specific type of failure mode of component 12 through model 16,
first sensor 34, second sensor 36, and third sensor 38 can be
different kinds of sensors and can be selected such that the first
physical property data, the second physical property data, and the
third property data share interdependencies and can be analyzed
together to determine the specific type of failure mode in
component 12. First sensor 34, second sensor 36, and third sensor
38 can be selected from the group of sensors including high speed
cameras, grayscale optical cameras, thermal cameras, fluid flow
detectors, gas analyzers, ultrasonic emitters, and ultrasonic
receivers. For example, as shown in FIG. 2, first sensor 34 can be
a high speed camera and the first physical property data collected
by first sensor 34 can be an intensity of heat source 28 as it
formed each layer of component 12. Second sensor 36 can be a
grayscale optical camera and the second physical property data
collected by second sensor 36 can include a geometry of sintered
metal 22 created by beam 30 as each layer was formed. Third sensor
36 can be a thermal camera and the third physical property data can
include a thermal gradient of each layer that indicates a size and
shape of melt pool 32 formed by beam 30 as it moved across each bed
to form each layer of component 12.
[0022] In this specific example, the first physical property data
containing the intensity of heat source 28 collected by first
sensor 34 for each layer can be aggregated by computing device 40
and used to calculate a submodel of the location of low intensity
spots 42 within component 12 where the intensity of heat source 28
was low or uneven, thereby possibly creating pockets of unsintered
metal powder or partially unsintered metal powder within component
12. The second physical property data containing the geometry of
sintered metal 22 collected by second sensor 36 for each layer can
be aggregated by computing device 40 and used to calculate a
submodel of the physical three-dimensional geometry 44 of component
12. The third physical property data containing the size and shape
of melt pool 32 collected by third sensor 38 for each of layer can
be aggregated by computing device 40 to calculate a submodel of the
size and location of thermal cold spots 46 within component 12. In
this example, computing device 40 can overlay or aggregate the
three submodels to generate model 16 and determine the
interdependencies between the first physical property data, the
second physical property data, and the third physical property
data. As illustrated in FIG. 2, physical three-dimensional geometry
44 from the second physical property data forms an outline of model
16 of component 12, and low intensity spots 42 from the first
physical property data and thermal cold spots 46 from the third
physical property data can overlap one another within geometry 44
of component 12 to indicate the position and size of potential
voids or cracks within component 12.
[0023] In other examples not illustrated in FIG. 2, second sensor
36 or third sensor 38 can be a fluid flow detector and the second
physical property data can comprise pressure differentials of a gas
flow over first bed 24 of metal powder and second bed 26 of metal
powder. Gas flow can be passed over first bed 24 and second bed 26
during the manufacture of component 12 to remove airborne
particulate that might interfere with heat source 28 and reduce its
intensity. Based on the pressure differentials of the gas flow over
first bed 24 and second bed 26 in the second physical property
data, computing device 40 can determine a presence of airborne
particulate over first bed 24 and second bed 26 during the
manufacture of component 12.
[0024] In other examples not illustrated in FIG. 2, second sensor
36 or third sensor 38 can include an ultrasonic emitter and an
ultrasonic receiver and the second physical property data can
include a density of first bed 24 of metal powder and a density of
second bed 26 of metal powder. The density of first bed 24 and
second bed 26 in the second physical property data can be used by
computing device 40 to detect the presence of cracks, the
propagation of cracks, or combinations thereof in component 12.
[0025] In addition to determining the presence of a potential
failure mode in component 12, first sensor 34, second sensor 36,
and third sensor 38 can be used by additive manufacturing machine
14 to reduce the likelihood of failure modes propagating throughout
component 12. Before first layer 18 of component 12 is formed by
additive manufacturing machine 14, an operator can input at least
one control parameter into additive manufacturing machine 14. The
at least one control parameter can be selected from the group
including laser power of additive manufacturing machine 14,
electron beam power of additive manufacturing machine 14, powder
grain size distribution of first bed 24 of metal powder, powder
chemistry of first bed 24, gas flow across a surface of first bed
24, purity of the gas flow, speed of the laser of additive
manufacturing machine 14, speed of the electron beam of additive
manufacturing machine 14, and combinations thereof. As additive
manufacturing machine 14 forms first layer 18, and as computing
device 40 receives the first, second, and third physical property
data from first sensor 34, second sensor 36, and third sensor 38
respectively, computing device 40 can compare the first, second and
third physical property data of the first with each other to
inspect first layer 18 for a defect. If computing device 40 finds a
defect in first layer 18, computing device 40 can adjust the at
least one control parameter prior to completing first layer 18 and
before forming second layer 20 so as to correct the defect in first
layer 18 and prevent the defect from forming in second layer 20.
Once computing device 40 has adjusted the at least one control
parameter, additive manufacturing machine 14 can proceed with
forming second layer 20 of component 12.
[0026] FIG. 3 depicts a flow chart of method 10 for forming model
16 of component 12. Method 10 can include the steps of: forming
first layer 18 of component 12 by sintering at least a portion of
first bed 24 of metal powder [step 48]; collecting first physical
property data of first layer 18 with first sensor 34 [step 50]; and
collecting second physical property data of first layer 18 second
sensor 36 [step 52]. Method 10 can also include: forming second
layer 20 of component 12 by sintering at least a portion of second
bed 26 of metal powder disposed proximate first layer 18 [step 54];
collecting the first physical property data of second layer 20 with
first sensor 34 [step 56]; and collecting second physical property
data of second layer 20 with second sensor 36 [step 58]. Method 10
can further include: aggregating the first physical property data
of first layer 18 and second layer 20 of component 12 [step 60];
and aggregating the second physical property data of first layer 18
and second layer 20 of component 12 [step 62]. Finally, model 10
can be calculated based on the aggregated first physical property
data and the aggregated second physical property data from which to
determine a potential failure mode in component 12 [step 64]. While
the invention has been described above with reference to the
embodiments disclosed in FIGS. 1A-3, other configurations and
arrangements can be used in alternative embodiments.
[0027] Persons of ordinary skill in the art will recognize that
system and method 10 can provide numerous advantages and benefits.
Some examples of those advantages and benefits are as follows.
Based on the first, second, and third physical property data
collected by first sensor 34, second sensor 36, and third sensor 38
respectively, computing device 40 can calculate model 16 of
component 12 formed by additive manufacturing machine 14. Model 16
allows an operator to determine the existence of a potential
failure mode in component 12, such as an interior void or crack,
which may not be easily detected through visual inspection of the
component. Because model 16 allows an operator to determine the
existence of a potential failure mode in component 12, the operator
can perform further steps to repair or remedy the failure mode in
component 12 before component 12 enters operation. First sensor 34,
second sensor 36, and third sensor 38 of system and method 10 can
also be used to analyze each layer of component 12 as each layer is
being formed by additive manufacturing machine 14. Should first
sensor 34, second sensor 36, and third sensor 38 sense a defect in
a layer of component 12, computing device 40 can adjust at least
one control parameter of additive manufacturing machine 14 so as to
correct the defect in the layer and reduce the likelihood of the
defect propagating in subsequent layers of component 12.
[0028] The following are non-exclusive descriptions of possible
embodiments of the present invention.
[0029] In one embodiment, a method for additive manufacturing a
component incudes forming a first layer of the component by
sintering at least a portion of a first bed of metal powder and
collecting first physical property data of the first layer with a
first sensor. Second physical property data of the first layer is
collected with a second sensor. The method also includes forming a
second layer of the component by sintering at least a portion of a
second bed of metal powder disposed proximate the first layer.
First physical property data of the second layer is collected with
the first sensor and second physical property data of the second
layer is collected with the second sensor. The method further
includes aggregating the first physical property data of the first
layer and the second layer of the component, and aggregating the
second physical property data of the first layer and the second
layer of the component. A model is calculated based on the
aggregated first physical property data and the aggregated second
physical property data from which to determine a potential failure
mode in the component.
[0030] The method of the preceding paragraph can optionally
include, additionally and/or alternatively, any one or more of the
following features, configurations and/or additional
components:
[0031] the first sensor is a high speed camera and the first
physical property data is an intensity of a heat source that forms
the first layer and the second layer;
[0032] the second sensor is a grayscale optical camera and the
second physical property data comprises a geometry of sintered
metal created by a laser beam or an electron beam during the
formation of the first and second layers;
[0033] the second sensor is a thermal camera and the second
physical property data comprises a size and shape of a melt pool
formed by a laser beam or a electron beam in the first and second
layers;
[0034] the second sensor is a fluid flow detector and the second
physical property data comprises pressure differentials of a gas
flow over both the first bed of metal powder and the second bed of
metal powder;
[0035] a presence of airborne particulate over the first bed of
metal powder and over the second bed of metal powder is determined
from the pressure differentials of the gas flow of the second
physical property data;
[0036] the second sensor comprises an ultrasonic emitter and
ultrasonic receiver and the second physical property data comprises
a density of the first bed of metal powder and a density of the
second bed of metal powder; and/or
[0037] the second physical property data is selected from the group
consisting of crack presence, crack propagation, and combinations
thereof.
[0038] In another embodiment, a system for additive manufacturing a
component includes an additive manufacturing machine for building a
component layer by layer and a first sensor configured to collect a
first physical property data for each layer of the component as
each layer is formed by the additive manufacturing machine. The
system also includes a second sensor configured to collect a second
physical property data for each layer of the component as each
layer is formed by the additive manufacturing machine. A computing
device is operatively connected to the first and second sensors and
configured to receive the first physical property data and the
second physical property data of the component and configured to
compare the first physical property data with the second physical
property data to determine a potential failure mode in the
component.
[0039] The system of the preceding paragraph can optionally
include, additionally and/or alternatively, any one or more of the
following features, configurations and/or additional
components:
[0040] the first sensor is selected from the group consisting of
high speed cameras, grayscale optical cameras, thermal cameras,
fluid flow detectors, gas analyzers, ultrasonic emitters, and
ultrasonic receivers;
[0041] the second sensor is selected from the group consisting of
high speed cameras, grayscale optical cameras, thermal cameras,
fluid flow detectors, gas analyzers, ultrasonic emitters, and
ultrasonic receivers;
[0042] a third sensor configured to collect a third physical
property data for each layer of the component; and/or
[0043] the third sensor is selected from the group consisting of
high speed cameras, grayscale optical cameras, thermal cameras,
fluid flow detectors, gas analyzers, ultrasonic emitters, and
ultrasonic receivers.
[0044] In another embodiment, a method for additive manufacturing a
component includes forming a first layer of the component by an
additive manufacturing machine and collecting first physical
property data of the first layer with a first sensor. Second
physical property data of the first layer is collected with a
second sensor. The first physical property data of the first layer
and the second physical property data of the first layer are
compared to inspect the first layer for a defect. The method
further comprises forming a second layer of the component by the
additive manufacturing machine and collecting first physical
property data of the second layer with the first sensor. Second
physical property data of the second layer is collected with the
second sensor. The first physical property data of the second layer
and the second physical property data of the second layer are
compared to inspect the second layer for a defect. The first
physical property data and the second physical property data of the
first layer is aggregated with the first physical property data and
the second physical property data of the second layer to form a
model of the first physical property and the second physical
property of the component. A potential failure mode in the
component is determined based upon the model of the first physical
property and the second physical property of the component.
[0045] The method of the preceding paragraph can optionally
include, additionally and/or alternatively, any one or more of the
following steps, features, and/or configurations:
[0046] inputting into the additive manufacturing machine at least
one control parameter prior to forming the first layer;
[0047] the at least one control parameter is selected from the
group comprising laser power of the additive manufacturing machine,
electron beam power of the additive manufacturing machine, powder
grain size distribution of a powder bed formed by the additive
manufacturing machine, powder chemistry of the powder bed, gas flow
across a surface of the powder bed, purity of the gas flow, speed
of the laser of the additive manufacturing machine, and speed of
the electron beam of the additive manufacturing machine;
[0048] adjusting the at least one control parameter based upon both
the first physical property data of the first layer collected by
the first sensor and the second physical property data of the first
layer collected by the second sensor prior to forming the second
layer;
[0049] forming the second layer of the component based on the
adjusting of the at least one control parameter; and/or
[0050] the additive manufacturing machine is selected from the
group consisting of direct metal laser sintering machines and
electron beam sintering machines.
[0051] Any relative terms or terms of degree used herein, such as
"substantially", "essentially", "generally" and the like, should be
interpreted in accordance with and subject to any applicable
definitions or limits expressly stated herein. In all instances,
any relative terms or terms of degree used herein should be
interpreted to broadly encompass any relevant disclosed embodiments
as well as such ranges or variations as would be understood by a
person of ordinary skill in the art in view of the entirety of the
present disclosure, such as to encompass ordinary manufacturing
tolerance variations, incidental alignment variations, transitory
vibrations and sway movements, temporary alignment or shape
variations induced by operational conditions, and the like.
[0052] While the invention has been described with reference to an
exemplary embodiment(s), 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 invention. For example, while system and method 10 has been
described in the Specification as including first sensor 34, second
sensor 36, and third sensor 38, system and method 10 can include
additional sensors. In addition, many modifications may be made to
adapt a particular situation or material to the teachings of the
invention without departing from the essential scope thereof.
Therefore, it is intended that the invention not be limited to the
particular embodiment(s) disclosed, but that the invention will
include all embodiments falling within the scope of the appended
claims.
* * * * *