U.S. patent application number 15/863534 was filed with the patent office on 2020-07-09 for manufacture modeling and monitoring.
This patent application is currently assigned to Etegent Technologies Ltd.. The applicant listed for this patent is Etegent Technologies Ltd.. Invention is credited to Brian Bahr, Gary E. Coyan, Chris M. Hodapp, Joseph M. Kesler, Uriah M. Ligget, Thomas D. Sharp.
Application Number | 20200218242 15/863534 |
Document ID | / |
Family ID | 67139873 |
Filed Date | 2020-07-09 |
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United States Patent
Application |
20200218242 |
Kind Code |
A9 |
Kesler; Joseph M. ; et
al. |
July 9, 2020 |
MANUFACTURE MODELING AND MONITORING
Abstract
Methods, apparatus, and computer program products for analyzing,
monitoring, and/or modeling the manufacture of a type of part by a
manufacturing process. Non-destructive evaluation data and/or
quality related data collected from manufactured parts of the type
of part may be aligned to a simulated model associated with the
type of part. Based on the aligned data, the manufacturing process
may be monitored to determine whether the manufacturing process is
operating properly; aspects of the manufacturing process may be
spatially correlated to the aligned data; and/or the manufacturing
process may be analyzed.
Inventors: |
Kesler; Joseph M.;
(Cincinnati, OH) ; Sharp; Thomas D.; (Terrace
Park, OH) ; Ligget; Uriah M.; (Independence, KY)
; Bahr; Brian; (Cincinnati, OH) ; Hodapp; Chris
M.; (Reading, OH) ; Coyan; Gary E.; (Terrace
Park, OH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Etegent Technologies Ltd. |
Cincinnati |
OH |
US |
|
|
Assignee: |
Etegent Technologies Ltd.
Cincinnati
OH
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20190212721 A1 |
July 11, 2019 |
|
|
Family ID: |
67139873 |
Appl. No.: |
15/863534 |
Filed: |
January 5, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
14211600 |
Mar 14, 2014 |
9864366 |
|
|
15863534 |
|
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|
|
61791139 |
Mar 15, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05B 19/41885 20130101;
G05B 2219/32385 20130101; G05B 2219/32368 20130101; G05B 2219/32359
20130101; G05B 2219/31444 20130101; G05B 19/4063 20130101 |
International
Class: |
G05B 19/418 20060101
G05B019/418 |
Claims
1-20. (canceled)
21. A method of modeling the manufacture of a type of part with a
manufacturing process that includes at least one manufacturing step
in a system including at least one processing unit and at least one
memory, the method comprising: receiving non destructive evaluation
(NDE) data associated with the type of part that corresponds to
data collected during non-destructive evaluation of at least one
particular part of the type of part; aligning the NDE data to at
least one corresponding simulated location on a simulated model of
at least a portion of a part of the type of part; and associating
manufacturing data with the simulated model, wherein the
manufacturing data includes at least one of data indicating a
manufacturing step of the manufacturing process, data indicating a
manufacturing apparatus utilized in the manufacturing process, data
indicating a manufacturing tool utilized in the manufacturing
process, data indicating a process parameter of the manufacturing
process, and data indicating evaluation equipment utilized in
collecting the NDE data for a part manufactured by the
manufacturing process; wherein associating the manufacturing data
with the simulated model includes associating the manufacturing
data with at least one corresponding simulated location on the
simulated model.
22. The method of claim 21, further comprising: associating the
manufacturing data with NDE data that is aligned to at least one
particular simulated location.
23. The method of claim 22 further comprising: analyzing NDE data
that is aligned to the at least one particular simulated location
and the associated manufacturing data and identifying a
manufacturing step of the manufacturing process that is associated
with the NDE data aligned to the at least one particular simulated
location.
24. The method of claim 22 further comprising: analyzing NDE data
that is aligned to the at least one particular simulated location
and the associated manufacturing data and identifying a
manufacturing apparatus that is utilized in the manufacturing
process associated with the NDE data aligned to the at least one
particular simulated location.
25. The method of claim 22 further comprising: analyzing NDE data
that is aligned to that at least one particular simulated location
and the associated manufacturing data and identifying a
manufacturing tool that is utilized in the manufacturing process
associated with the NDE data aligned to the at least one particular
simulated location.
26. The method of claim 21, wherein the manufacturing data includes
data indicating evaluation equipment that is utilized in collecting
the NDE data during non-destructive evaluation of the at least one
particular part, the method further comprising: analyzing the NDE
data and the aligned NDE data to determine whether the evaluation
equipment utilized in collecting the NDE data is operating
properly.
27. A method of analyzing a manufactured part of a particular type
of part, wherein the manufactured part is manufactured using a
manufacturing process that includes a plurality of manufacturing
steps in a system including at least one processing unit and at
least one memory, the method comprising: aligning a non-destructive
evaluation (NDE) dataset associated with the manufactured part to a
simulated model of at least of a portion of a part of the
particular type of part, including aligning NDE data associated
with an area of interest on the manufactured part to at least one
corresponding simulated location on the simulated model; the NDE
dataset corresponding to data collected during non-destructive
evaluation of the associated manufactured part; and analyzing the
aligned NDE data associated with the area of interest on the
manufactured part and the at least one corresponding simulated
location to determine whether the manufactured part includes a
manufacturing defect that is associated with the area of
interest.
28. The method of claim 27, wherein analyzing the aligned NDE data
associated with the area of interest on the manufactured part and
the at least one corresponding simulated location includes
determining a spatially correlated statistic for the area of
interest for the manufactured part, and comparing the spatially
correlated statistic to baseline data associated with the area of
interest on the simulated model.
29. The method of claim 27 further comprising: in response to
determining that the manufactured part includes a manufacturing
defect associated with the area of interest, aligning an indication
of the manufacturing defect to at least one corresponding simulated
location on the simulated model.
30. The method of claim 29, further comprising: in response to
determining that the manufactured part includes a manufacturing
defect associated with the area of interest, analyzing the aligned
manufacturing defect to determine a root cause problem associated
with the at least one corresponding simulated location of the
aligned manufacturing defect on the simulated model.
31. The method of claim 30, wherein analyzing the aligned
manufacturing defect includes analyzing manufacturing data that is
associated with the at least one corresponding simulated location
of the aligned manufacturing defect on the simulated model; and the
associated manufacturing data of the at least one corresponding
simulated location of the aligned manufacturing defect including
historical data for the type of part indicating any manufacturing
defects associated with least one corresponding simulated location
of the aligned manufacturing defect and any root cause problems
that are associated with the manufacturing defects for the type of
part.
32. The method of claim 29, further comprising: in response to
determining that the manufactured part includes a manufacturing
defect associated with the area of interest, analyzing the aligned
NDE data associated with the area of interest, the at least one
corresponding simulated location of the aligned manufacturing
defect on the simulated model, and the aligned manufacturing defect
and determining a manufacturing step that is associated with the
manufacturing defect on the manufactured part.
33. The method of claim 32, further comprising: analyzing the
aligned NDE data, the at least one corresponding simulated location
and the identified manufacturing defect to identify at least one
manufacturing apparatus which is utilized in the at least one
manufacturing step that is associated with the identified
manufacturing defect on the manufactured part.
34. The method of claim 33, further comprising: analyzing the
aligned NDE data, the at least one corresponding simulated
location, and the identified manufacturing defect to identify at
least one manufacturing tool which is associated with the
manufacturing apparatus that is associated with the manufacturing
defect on the manufactured part.
35. A method of monitoring the manufacture of a composite aircraft
part of a particular type of composite aircraft part by a
manufacturing process that has a system including at least one
processing unit and at least one memory, the method comprising:
receiving a plurality of non-destructive evaluation (NDE) datasets,
wherein each NDE dataset is associated with at least a portion of a
manufactured composite aircraft part of the particular type; each
NDE dataset corresponding to data collected during non-destructive
evaluation of the associated at least a portion of the manufactured
composite aircraft part; aligning the plurality of NDE datasets to
a simulated model associated with the at least a portion of the
particular type of composite aircraft part; and analyzing each
aligned NDE dataset to determine at least one spatially correlated
statistic for each composite aircraft part.
36. The method of claim 35 further comprising: aligning each
spatially correlated statistic to at least one corresponding
simulated location on the simulated model.
37. The method of claim 36 further comprising: analyzing the
aligned spatially correlated statistics for at least a subset of
the composite aircraft parts to determine a manufacturing trend
associated with the manufacturing process based on the analyzed
spatially correlated statistics.
38. The method of claim 37 further comprising: analyzing the
manufacturing trend and base line data that is associated with the
at least one corresponding simulated location on the simulated
model to determine whether the manufacturing process is operating
properly.
39. The method of claim 38, further comprising: in response to
determining that the manufacturing process is not operating
properly, determining a root cause problem associated with the
manufacturing process based at least in part on the aligned NDE
datasets.
40. The method of claim 35, wherein the spatially correlated
statistic is one of average porosity, average thickness or average
distance for a region of the at least a portion of a manufactured
composite aircraft part of the particular type.
41. The method of claim 35, further comprising: receiving
manufacturing data associated with the manufacturing process for
the particular type of part; the manufacturing data including at
least one of: data indicating a manufacturing step of the
manufacturing process associated with at least one physical
location on the particular type of part, data indicating a
manufacturing apparatus utilized in the manufacturing process
associated with at least one physical location on the particular
type of part, data indicating a manufacturing parameter of the
manufacturing process associated with at least one physical
location on the particular type of part, data indicating a
manufacturing tool utilized in the manufacturing process associated
with at least one physical location on the particular type of part,
and data indicating at least one possible root cause problem of the
manufacturing process associated with at least one physical
location on the particular type of part; and associating the
manufacturing data with the simulated model.
42. A method of monitoring the manufacture of a particular type of
part by a manufacturing process including a plurality of
manufacturing steps in a system including at least one processing
unit and a memory, the method comprising: receiving non-compliance
reports corresponding to the manufacture of parts of the type of
part, with at least one non-compliance report indicating at least
one visually detected defect corresponding to a particular location
on a particular part; aligning each visually detected defect to at
least one simulated location of a simulated model associated with
at least a portion of a part of the type of part, wherein the at
least one simulated location corresponds to the particular location
corresponding to the visually detected defect; and analyzing the
aligned visually detected defects to determine whether a
manufacturing problem is occurring in the manufacturing process.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a Continuation of U.S. application Ser.
No. 14/211,600 filed on Mar. 14, 2014 by Joseph M. Kesler et al.,
and that Application claims the benefit of U.S. Provisional
Application No. 61/791,139 filed on Mar. 15, 2013 by Joseph M.
Kesler et al., the entire disclosure of those Applications being
incorporated by reference herein in their entireties.
FIELD OF THE INVENTION
[0002] The present invention relates to computing systems, and more
particularly to the modeling and monitoring of part manufacture
with inspection data and/or non-destructive evaluation ("NDE")
data.
BACKGROUND OF THE INVENTION
[0003] Non-destructive Evaluation and Inspection ("NDE/I")
technologies generally provide ways to nondestructively scan,
image, sense or otherwise evaluate characteristics of materials
and/or components. In particular, NDE/I technologies may be used to
detect minute flaws and defects in those materials and/or component
parts. As such, NDE/I technologies have become increasingly used to
help assure structural and functional integrity, safety, and cost
effective sustainment of various assets, during both initial
manufacture and operational service.
[0004] Non-destructive evaluation ("NDE") data is often based on
raw data gathered from NDE data collection devices and may include
x-ray images of at least a portion of a part or asset, such as the
wing of an aircraft or some other type of part that may be
manufactured. NDE data is often large in size, associated with
merely a portion of the part, and also must be matched with a
particular location on the part. Such large data sets of NDE data
become increasingly difficult to manage, particularly if such NDE
datasets are collected for many parts manufactured in a
manufacturing process. In addition, other types of quality related
data, including for example visual inspection data from an
inspector, may further complicate management and analysis of NDE
data and/or quality related data on a large scale, such as in a
manufacturing environment.
[0005] To determine wear and tear, structural damage and/or other
irregularities of a part may require the analysis of tens (if not
hundreds) of individual datasets of NDE data and/or quality related
data. This may result in numerous datasets of NDE data and/or
quality related data for each manufactured part of a manufacturing
process, and thus even more datasets of NDE data and/or quality
related data for a plurality of parts manufactured by the
manufacturing process. As each dataset is analyzed, this results in
large amounts of data that are difficult to categorize and
otherwise analyze in whole. Moreover, the NDE data and/or other
such quality related data may be discarded after it has been
analyzed, and thus there is often little inspection data related to
the manufacture of parts over time.
[0006] To account for such data management issues, in some
conventional systems, NDE data and/or quality related data may be
discarded or ignored if such data does not correspond to a part on
which a manufacturing defect has been detected. Moreover, in
conventional systems, analysis of NDE data and/or quality related
data is time consuming due to the cumbersome nature of the data.
Hence, when utilizing NDE data and/or other such types of
inspection data for parts manufactured in a manufacturing process,
the usefulness of such NDE data and/or other such types of
inspection data is limited due to the inefficiencies associated
with management and analysis of such data.
[0007] Consequently, there is a continuing need to manage and
analyze inspection data for a manufacturing process.
SUMMARY OF THE INVENTION
[0008] Embodiments of the invention provide for a method,
apparatus, and program product to manage and analyze
non-destructive evaluation ("NDE") data and/or other types of
quality related data corresponding to parts manufactured by a
manufacturing process to thereby monitor and model the
manufacturing process.
[0009] Consistent with embodiments of the invention, a manufacture
of a type of part may be monitored. In these embodiments, an NDE
dataset associated with a particular part of the type of part may
be received, where each NDE dataset for the part includes NDE data,
where such NDE data may be referred to herein as one or more NDE
data points, and each NDE dataset may correspond to data (i.e., raw
data) collected during non-destructive evaluation of the particular
part. The NDE dataset may be aligned to a simulated model
associated with the type of part, where such aligning may include
aligning NDE data points of the dataset to corresponding simulated
locations on the simulated model. Respective NDE data points of the
aligned NDE data points may be analyzed to determine a spatially
correlated statistic corresponding to the particular part based at
least in part on the respective NDE data points and the
corresponding simulated locations of the respective NDE data points
for the particular part. The spatially correlated statistic may be
determined for a group of proximate (i.e., proximately aligned on
the simulated model) NDE data points, where the spatially
correlated statistic may be based at least in part on a measurement
value of each NDE data point. Output data may be generated based at
least in part on the spatially correlated statistic.
[0010] These and other advantages will be apparent in light of the
following figures and detailed description.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate embodiments of
the invention and, together with a general description of the
invention given above and the detailed description of the
embodiments given below, serve to explain the principles of the
invention.
[0012] FIG. 1 is a diagrammatic illustration of a computing system,
user device, and NDE/I collection devices configured to collect and
analyze non-destructive evaluation ("NDE") data consistent with
embodiments of the invention to analyze, model, and/or monitor a
manufacturing process.
[0013] FIG. 2 is a block diagram of that illustrates data
components of manufacturing data that may be generated and/or
processed by the computing system and/or user device of FIG. 1 to
analyze, model, and/or monitor a manufacturing process.
[0014] FIG. 3 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to monitor the manufacturing process.
[0015] FIG. 4 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 3.
[0016] FIG. 5 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 3.
[0017] FIG. 6 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 3.
[0018] FIG. 7 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 3.
[0019] FIG. 8 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 3.
[0020] FIG. 9 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 3.
[0021] FIG. 10 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 3.
[0022] FIG. 11 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 3.
[0023] FIG. 12 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 3.
[0024] FIG. 13 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to monitor the manufacture of a type of part
by a manufacturing process.
[0025] FIG. 14 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 13.
[0026] FIG. 15 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 13.
[0027] FIG. 16 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 13.
[0028] FIG. 17 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to monitor the manufacture of a type of part
by a manufacturing process.
[0029] FIG. 18 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to model the manufacture of a type of part by
a manufacturing process.
[0030] FIG. 19 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to analyze a part manufactured by a
manufacturing process.
[0031] FIG. 20 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to monitor the manufacture of a type of
composite aircraft of part by a manufacturing process.
[0032] FIG. 21 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to analyze manufacture of a type of part by a
manufacturing process.
[0033] FIG. 22 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to analyze manufacture of a type of part by a
manufacturing process.
[0034] FIG. 23 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the analysis of the manufacturing
process illustrated in FIG. 22.
[0035] FIG. 24 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the analysis of the manufacturing
process illustrated in FIG. 22.
[0036] FIG. 25 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the analysis of the manufacturing
process illustrated in FIG. 22.
[0037] FIG. 26 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to analyze manufacture of a type of part by a
manufacturing process.
[0038] FIG. 27 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the analysis of the manufacturing
process illustrated in FIG. 26.
[0039] FIG. 28 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the analysis of the manufacturing
process illustrated in FIG. 26.
[0040] FIG. 29 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to model the manufacture of a type of part by
a manufacturing process.
[0041] FIG. 30 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the modeling of the manufacturing
process illustrated in FIG. 29.
[0042] FIG. 31 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the modeling of the manufacturing
process illustrated in FIG. 29.
[0043] FIG. 32 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to monitor the manufacture of a type of part
by a manufacturing process.
[0044] FIG. 33 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 to monitor the manufacture of a type of part
by a manufacturing process.
[0045] FIG. 34 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 33.
[0046] FIG. 35 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 33.
[0047] FIG. 36 is a flowchart that illustrates a sequence of
operations that may be performed by the computing system and/or
user device of FIG. 1 during the monitoring of the manufacturing
process illustrated in FIG. 33.
[0048] FIG. 37 is a diagrammatic illustration of an example
graphical user interface that includes a display representation of
a simulated model of a type of part that may be output on a display
associated with the user device and/or computing system of FIG.
1.
[0049] FIG. 38 is a diagrammatic illustration of the example
graphical user interface of FIG. 37 where the display
representation of the simulated model includes a visual
representation of aligned NDE data on the simulated model.
[0050] FIGS. 39A-C are diagrammatic illustrations of the example
graphical user interface of FIG. 38 where the display
representation of the simulated model includes a visual
representation of aligned indications of potential problems on the
simulated model, and FIG. 39B is an enlarged view of a portion of
FIG. 39A.
[0051] FIG. 40 is a diagrammatic illustration of the example
graphical user interface of FIG. 37 where the display
representation of the simulated model includes a visual
representation of aligned indications of potential problems and a
highlighted area selected by user input that indicates an area of
interest.
[0052] FIGS. 41A-B are diagrammatic illustrations of an example
graphical interface that includes a display representation of a
simulated model of a type of part including a visual representation
of indications of potential problems associated with a first part
of the type of part that may be output on a display associated with
the user device and/or computing system of FIG. 1.
[0053] FIGS. 42A-B are diagrammatic illustrations of the example
graphical interface of FIGS. 41A-B, where the display
representation of the simulated model includes a visual
representation of indications of potential problems associated with
a second part of the type of part.
[0054] FIG. 43 is an example control chart for a manufacturing
process that may be generated by the computing system and/or user
device based on manufacturing data, NDE data, and/or quality
related data associated with the manufacturing process.
[0055] It should be understood that the appended drawings are not
necessarily to scale, presenting a somewhat simplified
representation of various preferred features illustrative of the
basic principles of the invention. The specific design features of
the sequence of operations as disclosed herein, including, for
example, specific dimensions, orientations, locations, and shapes
of various illustrated components, will be determined in part by
the particular intended application and use environment. Certain
features of the illustrated embodiments may have been enlarged or
distorted relative to others to facilitate visualization and clear
understanding.
DETAILED DESCRIPTION
[0056] Embodiments of the invention provide for a method,
apparatus, and program product to model and/or monitor a
manufacturing process using NDE data and/or quality related data
collected from parts manufactured by the manufacturing process.
Furthermore, embodiments of the invention organize and align such
data by aligning the data to a simulated model of a type of part
associated with the manufactured parts. In some embodiments, NDE
data that corresponds to raw data collected by one or more NDE/I
devices during non-destructive evaluation of one or more of the
manufactured parts. In some embodiments, other types of quality
related data may be utilized. For example, quality related data may
comprise indication data collected during inspection by one or more
personnel tasked with inspecting parts manufactured in the
manufacturing process (e.g., quality control
engineers/technicians). The indication data may comprise
indications of potential problems at locations on parts of the type
of part. For example, such quality related data may include
visually detected defects indicated on non-compliance reports
generated during inspection of one or more of the manufactured
parts.
[0057] In general, some embodiments of the invention may be
described with respect to NDE datasets; however, the invention is
not so limited. Quality related data, not necessarily corresponding
to raw data collected by NDE/I devices may be utilized consistent
with some embodiments of the invention. For example, some
embodiments of the invention may analyze and/or manage information
derived from non-compliance reports corresponding to a
manufacturing process. These non-compliance reports may comprise
indication data that includes one or more indications of one or
more visually detected defects on parts of a type of part
manufactured by the manufacturing process. In general, such
non-compliance reports may be generated by a quality inspector
trained to inspect parts manufactured by the manufacturing process.
Moreover, other types of relevant quality related data may be
included in a non-compliance report in addition to or in place of
indications of visually detected defects depending on the type of
part and the manufacturing process. As another example,
defects/indications may be detected via ultrasonic scanning/testing
and may be included in a non-compliance report and/or input
directly to a simulated model via user input, where an operator may
manually enter such defects/indications.
[0058] In general, embodiments of the invention align one or more
NDE datasets comprising NDE data points and/or one or more quality
related datasets comprising quality related data points (i.e.,
indications of potential problems) to a simulated model associated
with a type of manufactured part. For example, a portion of a type
of part may be represented by the simulated model, and NDE data
points collected during non-destructive evaluation of a
manufactured part of the type of part may be aligned to
corresponding simulated locations on the simulated model.
Therefore, aligning the NDE dataset and/or quality related dataset
to the simulated model comprises aligning at least one data point
of the dataset to a corresponding location on the simulated model.
In general, at least a subset of data points of the dataset may be
aligned to a corresponding location on the simulated model.
[0059] According to embodiments of the invention, NDE data and/or
quality related data may be aligned to a simulated model. Methods
and apparatus for aligning NDE data and/or quality related data to
a simulated model is described in further detail in U.S. Pat. No.
8,108,168 to Sharp et al., entitled "MANAGING NON-DESTRUCTIVE
EVALUATION DATA," filed Mar. 12, 2009, which is incorporated by
reference herein in its entirety.
[0060] In some embodiments, the NDE data and/or quality related
data may be associated with inspection information. The inspection
information may associate the NDE data and/or quality related data
with particular information that may be useful to align the NDE
data, indicate potential problems, and/or otherwise provide data
about the type of part. In some embodiments, the inspection
information may include data associated with a location of a
particular part to which the associated data corresponds, an
identification of the particular part, a history of the particular
part, a time at which the NDE data was captured, a date at which
the NDE data was captured, an identification of an NDE session
associated with the NDE data, an annotation associated with the NDE
data (e.g., such as an annotation that includes an indication of a
potential problem), an identification of an inspector associated
with the NDE data, an identification of a series of NDE data in
which the NDE data was captured, an identification of the location
of the NDE data in the series of NDE data, an orientation
associated with the NDE data, a unique identification of the NDE
data, an identification of the modality of NDE data collection
device used to capture the NDE data, and/or combinations thereof.
The inspection information may be determined automatically, and/or
captured by a computer, during, or after the capture of the NDE
data.
[0061] In some embodiments, inspection information may include at
least one indication of a potential problem and a location thereof
on the NDE data, such that the indication may be aligned to a
corresponding simulated location on the simulated model. In some
embodiments, the at least one indication aligned to the simulated
model may be included in a display representation associated with
the type of part and based on the simulated model. For example, the
display representation may comprise a three dimensional
representation of the type of part that may be output to a computer
display or other such viewing device. In this example, an
indication of the potential problem associated with the inspection
information may be a visual indicator located at the corresponding
location on the three-dimensional representation.
[0062] In some embodiments, a plurality of datasets of NDE data
(e.g., a plurality of individual instances of NDE data), at least
some of which may be associated with inspection information, may be
aligned to the simulated model. As such, indications in turn
associated with the inspection information of the plurality of
datasets may be viewed for trends of indications, where such trends
may correspond to manufacturing trends associated with the
manufacture of the type of part by the manufacturing process.
[0063] Based on aligned NDE data some embodiments of the invention
may monitor a manufacturing process. In these embodiments, a
dataset of NDE data may be received for each of a plurality of
manufactured parts of a type of part manufactured by the
manufacturing process. Embodiments of the invention may align the
received data for each manufactured part to the simulated model. A
spatially correlated statistic may be determined for each part
based on the aligned NDE data, and a manufacturing trend may be
determined based on the spatially correlated statistics and
monitored for the manufacturing process.
[0064] A spatially correlated statistic may generally correspond to
a value associated with an area, region, volume, and/or other such
spatially related feature of the type of part. In general, the
spatially correlated statistic may define a value for such
spatially related feature that is based at least in part on NDE
data and/or quality related data collected for the spatially
related feature. For example, each part of a type of part may
include a particular portion for which NDE data collected for the
part indicates a measured value of the porosity at a plurality of
locations corresponding to the particular portion. Embodiments of
the invention may determine an average porosity for the particular
portion of each part based on the NDE data collected for each part
at the plurality of locations. Other types of spatially correlated
statistics may be determined depending on the type of NDE data
collected and/or the type of part, including for example, average
thickness, average distance between specified features, average
amplitude, average quantity of indications of potential problems,
density of indications of potential problems, a standard deviation
of any of the previously mentioned values, and/or other such types
of statistical data that may be determined based on the types of
collected NDE data.
[0065] For example, based on the spatially correlated statistics,
the manufacturing trend may indicate that while the manufacturing
process is presently producing acceptable parts, the manufacturing
trend indicates that the manufacturing process will begin producing
unacceptable parts in the future. Hence, based on the spatially
correlated statistics, embodiments of the invention may determine
whether the manufacturing process is operating properly, and if the
manufacturing trend indicates that a problem is likely to develop,
actions may be taken prior to the manufacturing process possibly
manufacturing unacceptable parts.
[0066] In some embodiments, a manufacturing process may be modeled
based at least in part on NDE data collected for one or more parts
of a type of part manufactured by the manufacturing process. In
these embodiments, at least one NDE dataset may be received, where
each NDE dataset comprises NDE data points of NDE data that
corresponds to data collected during non-destructive evaluation of
the a respective part of the type of part. The NDE data points may
be aligned to corresponding simulated locations on a simulated
model associated with the type of part. In these embodiments, the
NDE data may include associated inspection information that
indicates one or more potential problems detected on the particular
part. In addition, manufacturing data may be associated with the
simulated model, where the manufacturing data may indicate various
information associated with the manufacturing process and one or
more corresponding simulated locations on the simulated model. For
example, the manufacturing data may indicate a manufacturing step
of the manufacturing process associated with one or more
corresponding simulated locations on the simulated model. In this
example, if a manufacturing step of the manufacturing process
involved applying an adhesive to a particular location on each
manufactured part, the manufacturing data may indicate at a
corresponding simulated location on the simulated model the
adhesive application step. Hence, in this example, if a potential
problem were indicated at a corresponding simulated location
associated with the adhesive application step as indicated in the
manufacturing data, the modeling of the manufacturing process may
indicate that a problem is potentially occurring in the adhesive
application step.
[0067] Therefore, as illustrated by this example, NDE data and/or
inspection information may be organized spatially on the simulated
model, and manufacturing data may also be organized spatially on
the simulated model, and as a result, the manufacturing process may
be modeled on the spatially simulated model such that NDE data or
other such data may be correlated to aspects of the manufacturing
process. The manufacturing data may include for example, data that
indicates a manufacturing step of the manufacturing process, data
that indicates a manufacturing apparatus utilized in the
manufacturing process, data indicating a manufacturing tool
utilized in the manufacturing process, data indicating a process
parameter of the manufacturing process, data indicating evaluation
equipment utilized in collecting raw data corresponding to the NDE
data for parts manufactured by the manufacturing process, and/or
other such types of information related to the manufacturing
process.
Hardware and Software Environment
[0068] Turning to the drawings, wherein like numbers denote like
parts throughout the several views, FIG. 1 illustrates a hardware
and software environment for one or more computing systems 10, one
or more user devices 12 and one or more NDE/I collection devices 14
consistent with some embodiments of the invention. In general,
embodiments of the invention may be described in the context of a
single computing system 10 and/or user device 12, but as shown in
FIG. 1, the invention is not so limited. In particular, embodiments
of the invention may be implemented in distributed processing
systems, including for example, a plurality of interconnected
computing systems 10 and/or user devices 12 that are configured to
perform operations consistent with embodiments of the invention in
a distributed manner (i.e., across a plurality of distributed
processors using data stored to and read from a plurality of
distributed memory locations on a plurality of memory devices).
[0069] In general, the NDE/I collection devices 14 may comprise
devices configured to collect non-destructive evaluation/inspection
data. Such NDE/I collection devices may comprise one or more
cameras (e.g., to capture still images for visualization, videos
for visualization, and/or for sherography, etc.), thermograpic
cameras (e.g., to capture a thermographic image), borescopes,
fiberscopes, x-ray machines (e.g., to capture still images, to use
with computed radiography, to use with direct and/or digital
radiography, etc.), ultrasound machines, CT scanners, MRI machines,
eddy current detectors, liquid penetrant inspection systems,
magnetic-particle inspection systems, coordinate measuring
machines, and/or other such types of non-destructive
evaluation/inspection devices. As such, the types of NDE data
included in NDE datasets may vary, and embodiments of the invention
may model and/or monitor manufacture of a type of part by
processing various types of NDE data.
[0070] Computing system 10 and/or user device 12, for purposes of
this invention, may represent any type of computer, computing
system, server, disk array, or programmable device such as a
multi-user computer, single-user computer, handheld device,
networked device, mobile phone, gaming system, etc. Computing
system 10 and/or user device 12 may be implemented using one or
more networked computers, e.g., in a cluster or other distributed
computing system. Hence, it should be appreciated that the
computing system 10 and/or user device 12 may also include other
suitable programmable electronic devices consistent with the
invention
[0071] With reference to FIG. 1, as shown, the computing system 10
may comprise at least one processing unit (CPU') 16 and memory 18.
Each processor 16 may be one or more microprocessors,
micro-controllers, field programmable gate arrays, or ASICs, while
memory 18 may include random access memory (RAM), dynamic random
access memory (DRAM), static random access memory (SRAM), flash
memory, and/or another digital storage medium. As such, memory 14
may be considered to include memory storage physically located
elsewhere in computer 10, e.g., any cache memory in the at least
one CPU 16, as well as any storage capacity used as a virtual
memory, e.g., as stored on a mass storage device, a computer, or
another controller in communication with the computing system. In
addition, the computing system 10 may comprise a user interface 20,
where the user interface 20 generally comprises one or more
input/output devices for interfacing with a user, such as a
display, a keyboard, a mouse, speakers, a microphone, a video
camera, a touch input based device (e.g., a touchscreen), and/or
other such devices. Furthermore, the computing system 10 may
comprise a network interface 22, where the network interface 22 is
generally configured to communicate data over a communication
network 24. Network 24 generally comprises one or more
interconnected communication networks, including for example, a
cellular communication network, a local area network, a wide area
network, public networks (e.g., the Internet), an enterprise
private network, high speed data communication interconnects,
and/or other such communication networks.
[0072] The memory 18 stores at least one application 26 and/or an
operating system 28, where the application 26 and/or operating
system generally comprise program code in the form of instructions
that may be executed by the processor 16 to cause the processor to
perform one or more operations consistent with embodiments of the
invention. For example, the application 26 and/or operating system
28 may include program code in the form of executable instructions
that may cause the processor to monitor and/or model a
manufacturing process based on data received at the computing
system 10 and/or processor 16. It will be appreciated that various
applications, components, programs, objects, modules, etc. may also
execute on one or more processors in another networked device
coupled to computing system 10 via the network 24, e.g., in a
distributed or client-server computing environment
[0073] In general, the memory 18 of the computing system 10 may
store data utilized by embodiments of the invention. For example,
the CPU 16 may read from and/or write data to the memory 18 when
performing one or more operations consistent with some embodiments
of the invention. As discussed above, the memory 18 may generally
represent memory accessible by the computing system 10, such as
accessible databases connected over the communication network 24
and/or other such data communication networks. Furthermore, the
memory 18 includes a database management system in the form of a
computer program that, when executing as instructions on the
processor 16, is used to read from and/or write to accessible data
structures (e.g., databases) of the memory. As shown in FIG. 1, the
memory 18 may store a manufacturing database 30, that stores
manufacturing data 32 associated with a manufacturing process that
manufactures a type of part. In addition, the memory 18 may store
model data 34 associated with the type of part manufactured in the
manufacturing process, NDE data 36 associated with the type of part
and/or manufacturing process, quality related data 38 associated
with the type of part and/or manufacturing process, and/or
inspection information 40 associated with the type of part and/or
manufacturing process.
[0074] While in FIG. 1, the manufacturing database 30, model data
34, NDE data, quality related data 36, and inspection information
40 are illustrated as separate data structures, the invention is
not so limited. The computing system 10 may comprise one or more
data structures configured as database structures storing the data
described herein. Such one or more databases may be configured in
any database organization and/or structure, including for example,
relational databases, hierarchical databases, network databases,
and/or combinations thereof.
[0075] As shown in FIG. 1, each user device 12 generally comprises
a processor (CPU') 42 and a memory 44. In general, the user device
12 may be a personal computer, laptop computer, hand-held computing
device, tablet computer, and/or other such types of computing
devices. As shown, the user device 12 may comprise a user interface
46 configured to receive input data from a user and output data to
a user via one or more input/output devices. Such input/output
devices, include, for example a keyboard, mouse, display, touch
screen, speakers, microphone, etc. Such input/output devices are
generically represented by a human machine interface (HMI') 48 in
FIG. 1. Furthermore, the user device 12 may include a network
interface 50, where, as described above with respect to the
computing system 10, the network interface is configured to
transmit data to and receive data from the communication network
24. For example, the computing system 10 and the user device 12 may
communicate data therebetween over the communication network 24 via
the network interfaces 22, 50. Furthermore, the user device 12 may
be under the control of an operating system (`OS`) 52 stored in the
memory 44. As described previously, the operating system 52 and/or
an application 54 stored in the memory 44 may comprise program code
in the form of executable instructions, that, when executed by the
processor 42 may cause the processor to perform or cause to be
performed one or more operations consistent with embodiments of the
invention.
[0076] FIG. 2 is a diagrammatic illustration of one embodiment of a
plurality of components of manufacturing data 32 consistent with
embodiments of the invention. As will be described in further
detail, herein the manufacturing data may generally comprise data
associated with one or more aspects of a manufacturing process
and/or a type of part. As mentioned previously, the manufacturing
data 32 may be stored in a manufacturing database 30. As such, in
some embodiments of the invention, the data illustrated as a
component of the manufacturing data 32 may be stored relationally,
such that the relationship(s) between the different types of data
of the manufacturing data 32 may be stored. As shown, the
manufacturing data 32 may store location data 60, where location
data 60 may identify one or more simulated locations on a simulated
model of the type of part. In general, location data 60 may be
related to one or more other types of data to thereby
correlate/associate such data to one or more simulated locations on
the simulated model of the type of part.
[0077] In some embodiments, the manufacturing data 32 may store
manufacturing step data 62 that identifies one or more
manufacturing steps associated with the manufacturing process
and/or type of part. Similarly, the manufacturing data 32 may
comprise manufacturing apparatus data 64 that identifies one or
more manufacturing apparatuses associated with the manufacturing
process and/or type of part. In general, a manufacturing apparatus
may be equipment utilized in the manufacturing process (e.g.,
cutting tools, molds, drilling tools, resin pumps, vacuum pumps,
autoclaves, adhesive dispensers, carbon fiber tape rollup machines,
carbon fiber placement machines, industrial ovens for curing, etc.)
The manufacturing data 32 may comprise manufacturing tool data 66
that identifies one or more manufacturing tools associated with the
manufacturing process and/or the type of part. In general, a
manufacturing tool may be a portion of equipment that is
replaceable/consumable and/or experience wear (e.g., drill bits,
cutting blades, mold seams, thermocouples, seals/gaskets, vacuum
ports, resin flow paths, resin injection ports, mold planes, caul
planes, mandrel sections, bladders, injection nozzles, etc.) The
manufacturing data 32 may comprise manufacturing parameter data 68
that identifies one or more manufacturing parameters associated
with the manufacturing process and/or the type of part. In general
a manufacturing parameter and/or manufacturing step parameter may
be considered a parameter that may affect the manufacturing process
(e.g., temperature in a curing oven, pressure in a mold, ratio for
an adhesive mixture, pressure of a water cutting apparatus, age of
material, temperature of material, viscosity of a resin, anomalies
in material structure, etc.). Moreover, an additional consideration
with respect to the manufacturing parameters may be the intended
manufacturing parameter as compared to an actual manufacturing
parameter, where embodiments of the invention may analyze, model,
and/or monitor a manufacturing process based on combinations
thereof. Furthermore, a manufacturing parameter may comprise
anomalies reported by the manufacturing equipment (e.g., a
manufacturing apparatus, an NDE/I collection device, etc.),
including for example, data stored in machine logs for
manufacturing equipment used in the manufacturing process. These
logs may indicate events (i.e., anomalies) that may affect the
manufacture of parts by the manufacturing process. For example, if
a manufacturing apparatus of a manufacturing process was a fiber
placement system, a machine log for such fiber placement system may
include data related to loss of tension, fiber slippage, compaction
pressure, deviations in velocity of fiber layup, and/or other such
events/anomalies that may affect the manufacture of a part in the
manufacturing process. The manufacturing parameter data may store
data related to such anomalies for the various manufacturing
equipment utilized in the manufacturing process. The manufacturing
data 32 may comprise evaluation equipment data 70 that identifies
one or more evaluation equipment (i.e., NDE/I devices) 44
associated with the manufacturing process and/or the type of part.
The manufacturing data 32 may comprise possible root cause problem
data 72 that identifies one or more root cause problems associated
with the manufacturing process and/or the type of part. The
manufacturing data 32 may comprise manufacturing defect data 74
that identifies one or more manufacturing defects associated with
the manufacturing process and/or type of part. In general, the one
or more identified manufacturing defects may be based on previous
analysis of the manufacturing process (i.e., historical data for
previously manufactured parts). The manufacturing data 32 may
comprise spatially correlated statistic data 76 that indicates one
or more spatially correlated statistics associated with the
manufacturing process and/or type of part. The manufacturing data
32 may comprise manufacturing trend data 78 that indicates one or
more manufacturing trends associated with the manufacturing process
and/or type of part. Furthermore, the manufacturing data 32 may
comprise problem indication data 80 that indicates one or more
potential problems that may be associated with the type of
part.
[0078] As discussed, the manufacturing data 32 may be organized
relationally such that relationships between the types of data may
be indicated. For example, location data 60 may be associated with
manufacturing step data 62 to thereby indicate an association
between a particular manufacturing step identified in the
manufacturing step data 62 and one or more simulated locations on
the type of part indicated by the associated location data 60.
Building on the example, manufacturing apparatus data 64 may be
relationally associated with the manufacturing step data 62 and the
location data 60 to thereby indicate an association between the
particular manufacturing step, the one or more simulated locations,
and a particular manufacturing apparatus identified in the
manufacturing apparatus data 64. Similarly, possible root cause
problem data 72 may be relationally associated manufacturing step
data 62 to thereby identify one or more possible root cause
problems that are associated with a particular manufacturing step
identified in the relationally associated manufacturing step data
62. As illustrated by these examples, in general, the manufacturing
data 32 may indicate relationships between the various types of
data, and furthermore, the manufacturing data 32 may be associated
with a simulated model of the type of part to thereby spatially
organize/represent the data on the simulated model of the type of
part. In some embodiments, a display representation of the
simulated model and manufacturing data may be generated, and the
display representation may be output on a display for a user.
[0079] In general, the routines executed to implement the
embodiments of the invention, whether implemented as part of an
operating system or a specific application, component, algorithm,
program, object, module or sequence of instructions, or even a
subset thereof, will be referred to herein as "computer program
code" or simply "program code." Program code typically comprises
one or more instructions or sequence of operations that are
resident at various times in memory and storage devices in a
computer, and that, when read and executed by at least one
processor in a computer, cause that computer to perform the steps
necessary to execute steps or elements embodying the various
aspects of the invention. Moreover, while the invention has and
hereinafter will be described in the context of fully functioning
computers and computer systems, those skilled in the art will
appreciate that the various embodiments of the invention are
capable of being distributed as a program product in a variety of
forms, and that the invention applies regardless of the particular
type of computer readable media used to actually carry out the
invention. Examples of computer readable media include, but are not
limited to, non-transitory, recordable type media such as volatile
and non-volatile memory devices, floppy and other removable disks,
hard disk drives, tape drives, optical disks (e.g., CD-ROM's,
DVD's, HD-DVD's, Blu-Ray Discs), among others.
[0080] In addition, various program code described hereinafter may
be identified based upon the application or software component
within which it is implemented in specific embodiments of the
invention. However, it should be appreciated that any particular
program nomenclature that follows is merely for convenience, and
thus embodiments of the invention should not be limited to use
solely in any specific application identified and/or implied by
such nomenclature. Furthermore, given the typically endless number
of manners in which computer programs may be organized into
routines, procedures, methods, modules, objects, and the like, as
well as the various manners in which program functionality may be
allocated among various software layers that are resident within a
typical computer (e.g., operating systems, libraries, Application
Programming Interfaces [APIs], applications, applets, etc.), it
should be appreciated that embodiments of the invention are not
limited to the specific organization and allocation of program
functionality described herein.
[0081] Those skilled in the art will recognize that the
environments illustrated in FIGS. 1-2 are not intended to limit the
embodiments of the invention. Indeed, those skilled in the art will
recognize that other alternative hardware and/or software
environments may be used without departing from the scope of the
invention.
Software Description and Flows
[0082] FIGS. 3-36 provide flowcharts that illustrate sequences of
operations that may be performed by the computing system 10 and/or
user device 12 of FIG. 1. In general, the flowcharts of FIGS. 3-36
illustrate operation of possible implementations of systems,
methods, and computer program products according to various
embodiments of the invention. In this regard, each block in a
flowchart may represent a module, segment, or portion of program
code, which comprises one or more executable instructions for
implementing the specified logical function(s). Furthermore, any
blocks of the below mentioned flowcharts may be deleted, augmented,
made to be simultaneous with another, combined, re-ordered, or be
otherwise altered in accordance with the principles of the
invention.
[0083] FIGS. 3-12 provide flowcharts that illustrate a sequence of
operations that may be performed by the computing system 10
consistent with embodiments of the invention to monitor a
manufacturing process that manufactures a type of part. Turning now
to FIG. 3, which provides flowchart 100, the computing system 10
receives an NDE dataset associated with a particular part of the
type of part (block 102). The NDE dataset includes a plurality of
NDE data points and the NDE dataset corresponds to data collected
during non-destructive evaluation of the particular part. In
general, raw data may be collected by a NDE/I collection device,
and the NDE dataset is based thereon. The NDE dataset is aligned to
a simulated model associated with the type of part (block 104). In
some embodiments, the simulated model may be a simulated model of
the entire type of part; in other embodiments, the simulated model
may be a portion of the type of part. In general, the computing
system 10 includes model data 34 upon which the simulated model may
be based, and the computing system 10 may align one or more NDE
data points of the NDE dataset to the simulated model.
[0084] The computing system 10 may analyze one or more aligned NDE
data points for one or more locations corresponding to a spatially
related feature on the simulated model to determine a spatially
correlated statistic that corresponds to the spatially related
feature for the particular part (block 106). The spatially
correlated statistic may be aligned to the simulated model (block
108). As discussed, the spatially correlated statistic corresponds
to the spatially related feature, and therefore, aligning the
spatially correlated statistic to the simulated model may include
aligning the spatially correlated statistic to the simulated
spatially related feature on the simulated model. For example, if
the spatially related feature is a defined area on the type of
part, the spatially correlated statistic may be aligned to the
simulated representation of the defined area on the simulated model
of the type of part. The computing system 10 may generate output
data based at least in part on the spatially correlated statistic
(block 110). In general, the output data may be stored in a memory
location associated with the computing system 10 and/or
communicated by the computing system 10.
[0085] In some embodiments of the invention, the model data 34 may
store one or more baseline values associated with the simulated
model, where the baseline values may be indicate a baseline value
associated with the simulated model. In general, the baseline value
defines a value associated with the type of part that is a target
value for the type of part by the manufacturing process. In some
embodiments the baseline value may be spatially correlated such
that the baseline value indicates a target value. For example, the
baseline value may indicate a target average thickness for a
particular portion of the type of part. Hence, in some embodiments,
the computing system 10 may compare the spatially correlated
statistic for the particular part to a related baseline value for
the type of part to determine whether the particular part is
acceptable for the type of part (block 112). Continuing the example
provided above, the computing system 10 may compare a determined
average thickness for the particular portion of the particular part
to the baseline value, and if the determined average thickness for
the particular part is within a predefined range (e.g., +/-1%) of
the baseline value, the particular part may be determined to be
acceptable.
[0086] FIG. 4 provides flowchart 120, which illustrates further
operations that may be performed by the computing system 10 to
monitor the manufacturing process. Particularly, the computing
system 10 may process a plurality of spatially correlated
statistics (block 122), where each spatially correlated statistic
corresponds to a manufactured part of the type of part manufactured
by the manufacturing process. The computing system 10 may generate
a control chart for the manufacturing process based on the
spatially correlated statistics (block 124). In these embodiments,
the control chart may indicate the spatially correlated statistic
for each part manufactured in the manufacturing process. In some
embodiments, the computing system may generate output data based at
least in part on the spatially correlated statistics (block 126),
where the output data may be stored in a memory accessible by the
computing system 10 and/or communicated by the computing system 10.
In some embodiments, the computing system may analyze the spatially
correlated statistics from the parts to determine a manufacturing
trend for the manufacturing process based at least in part on the
spatially correlated statistics (block 128). In these embodiments,
the manufacturing trend may indicate a trend of the manufacturing
process related to the spatially correlated statistics. As each
spatially correlated statistic is related to a spatially related
feature, the manufacturing trend may thereby indicate a trend
associated with the spatially related feature for the manufacturing
process. For example, if each spatially correlated statistic may
correspond to an average thickness for a particular portion of the
corresponding part, the manufacturing trend may correspond to the
variability in the average thickness for the portion of each
part.
[0087] FIG. 5 provides flowchart 140, which illustrates further
operations that may be performed by the computing system 10 to
monitor the manufacturing process. In some embodiments, a
manufacturing trend and/or control chart based on spatially
correlated statistics for a plurality of parts manufactured by the
manufacturing process may be processed (block 142). The computing
system 10 may analyze the manufacturing trend and/or the control
chart to determine whether the manufacturing process is operating
properly (block 144). As discussed previously, the manufacturing
trend may correspond to a spatially related feature for the type of
part manufactured by the manufacturing process. Hence, based on the
manufacturing trend, the computing system may determine whether the
spatially related feature of the manufactured parts indicate that
the manufacturing process is operating properly with respect to the
spatially related feature.
[0088] For example, continuing the average thickness example from
above, if an acceptable range of average thickness for the
particular portion is defined for the type of part, the computing
system 10 may analyze the manufacturing trend to determine if,
based on the manufacturing trend, the manufacturing process is
likely to begin producing parts having an average thickness for the
particular portion not in the acceptable range. In this example,
the average thickness for each manufactured part may be within the
acceptable range, but the manufacturing trend may indicate that
out-of-range parts are likely to be produced. If the average
thickness of the particular portion of each part is increasing for
each later manufactured part, even if the particular portion of
each manufactured part is within the acceptable range, the
computing system may determine that the manufacturing process is
not operating properly because subsequently produced parts will
exceed the maximum acceptable limit of the acceptable range based
on the manufacturing trend.
[0089] Returning to FIG. 5, in response to determining that the
manufacturing process is operating properly (`Y` branch of block
146), the computing system 10 continues analyzing the manufacturing
trend. While the description and flowcharts may describe receiving
NDE datasets receiving NDE datasets, determining spatially
correlated statistics, determining a manufacturing trend, analyzing
the manufacturing trend to determine whether the manufacturing
process is operating properly, etc. as a single occurrence, the
invention is not so limited. In some embodiments, the receipt of
NDE datasets, processing of the NDE datasets, are continuous, such
that as parts may be manufactured by the manufacturing process, the
computing system 10 continues to process received NDE data and/or
quality related data. As such, as shown in FIG. 5, when the
computing system 10 determines that the manufacturing process is
operating properly, the computing system 10 continues analyzing the
manufacturing trend, as data is received and processed for parts
manufactured by the manufacturing process. By continuously
receiving and processing data associated with the manufactured
parts, embodiments of the invention continuously monitor the
manufacturing process such that any potential problems that may
arise in the manufacturing process may be detected in a timely
manner.
[0090] In response to determining that the manufacturing process is
not operating properly (`N` branch of block 146), the computing
system may determine a root cause problem associated with the
manufacturing process (block 148). In general, the root cause
problem may be determined from a plurality of possible root cause
problems associated with the manufacturing process, the spatially
correlated statistics, the NDE datasets, and/or the type of part.
In some embodiments, the computing system 10 may receive user input
data that identifies the root cause problem associated with the
manufacturing process to thereby determine the root cause problem.
The root cause problem may correspond to one or more aspects of the
manufacturing process, where such aspects generally depend on the
type of part and the manufacturing process. For example, if a
manufacturing process manufactures molded parts, and a spatially
correlated statistic determined for each of a plurality of
manufactured parts is the average porosity of a portion of each
part, if the average porosity of manufactured parts is increasing
over time, a root cause problem associated with the manufacturing
process may be the wearing of a gasket for a mold used in the
manufacturing process.
[0091] Based on the determined root cause problem, the
manufacturing trend, the spatially correlated statistics, and/or
the NDE datasets, the computing system 10 may generate spatially
correlated manufacturing data that identifies the determined root
cause problem (block 150). In some embodiments, the computing
system may determine a manufacturing step that corresponds to the
root cause problem (block 152). For example, if the manufacturing
process comprises a plurality of manufacturing steps, such as
molding, curing, and cutting a type of part, the computing system
may determine a particular manufacturing step that corresponds to
the root cause problem.
[0092] FIG. 6 provides flowchart 160 that illustrates further
operations that may be performed by the computing system 10 to
monitor the manufacturing process. As shown, manufacturing data for
a type of part may be processed (block 162), where the
manufacturing data may indicate at least one problem that is
associated with at least one corresponding simulated location on
the simulated model. The manufacturing data may be associated with
the simulated model (block 164), and simulated locations that have
related indicated problems may be identified by the computing
system 10 (block 166). Based on the identified simulated locations,
the computing system 10 may determine an area of interest
associated with the type of part (block 168). The computing system
10 may communicate data that identifies the area of interest for
the type of part.
[0093] An area of interest for a type of part may define a part, a
particular portion of the type of part, an area, a region, a
volume, and/or other such spatially related feature of the type of
part. In general, an area of interest may be utilized by
embodiments of the invention to define a portion or other such
spatially related feature that particular interest should be paid
when inspecting each part of the type of part, or for which NDE
data and/or other quality related data should be collected. Such
spatially related features may include, for example, a seam on a
composite part that corresponds to a seam in a mold for the
composite part, a portion of a part proximate a cut, weld, securing
element, bonded portion, and/or other such types of spatially
related features. In addition, an area of interest may be defined
on the simulated model and used to filter data on the simulated
model, such that data not corresponding to the area of interest may
be filtered from the simulated model.
[0094] Turning now to FIG. 7, which provides flowchart 180, as
shown, the computing system 10 may analyze manufacturing data
associated with the type of part to determine an area of interest
for the type of part (block 182). As discussed previously,
manufacturing data may indicate one or more potential problems
associated with the type of part, where such potential problems may
have been derived from NDE data and/or quality related data for
manufactured parts of the type of part. In these embodiments, the
computing system 10 may analyze the manufacturing data to determine
an area of interest for the type of part, where such area of
interest may correspond to a plurality of indications of potential
problems. The computing system 10 may select NDE data that is
aligned to simulated locations on the simulated model that are
associated with the determined area of interest (block 184), and
the computing system 10 may determine a spatially correlated
statistic for the area of interest based on the selected NDE data
(block 186).
[0095] FIG. 8 provides flowchart 200 that illustrates a sequence of
operations that may be performed by the computing system 10
consistent with embodiments of the invention when processing the
simulated model including and aligned NDE data points (block 201).
The computing system may generate a display representation of the
simulated model (block 202). The display representation may be
displayed for a user via the computing system 10 and/or the user
device 12, and the computing system 10 may receive user input data
that indicates and area of interest on the display representation
(block 204). In general, the user may interface with the computing
system 10 and/or user device 12 executing an application that
allows the user to provide input data related to the display
representation via one or more input devices. The computing system
10 and/or user device 12 may communicate data that identifies the
area of interest for the display representation (block 206), and
the computing system 10 may receive manufacturing data associated
with the type of part (block 208). The computing system may
associate manufacturing data with the simulated model (block 210),
where the manufacturing data indicates at least one problem
associated with one or more simulated locations of the simulated
model. The computing system 10 may update the display
representation such that visual representations of problems
associated with the area of interest may be included in the display
representation of the simulated model (block 212).
[0096] FIG. 9 provides flowchart 220 that illustrates operations
that may be performed by the computing system 10 when processing
NDE data for the type of part (block 221). The computing system 10
aligns NDE data points of the NDE data to a simulated model of the
type of part (block 222), and the computing system 10 analyzes the
aligned NDE data points to determine one or more spatially
correlated statistics (block 224). The computing system 10 aligns
the one or more spatially correlated statistics to the simulated
model (block 226), and the computing system 10 may generate a
display representation of the simulated model that visually
represents the one or more spatially correlated statistics on the
simulated model of the type of part (block 228).
[0097] FIG. 10 provides flowchart 240 that illustrates operations
that may be performed by the computing system 10 when processing
NDE data for a type of part (block 242). The computing system 10
may align NDE data points of the NDE data to a simulated model of
the type of part (block 244). The computing system 10 may analyze
the aligned NDE data points to determine whether the aligned NDE
data points indicate a potential problem at a corresponding
location on a particular part associated with the NDE data point
(block 246). In response to determining that an aligned NDE data
point indicates a potential problem at a corresponding location on
the particular part associated with the NDE data point (`Y` branch
of block 246), the computing system 10 aligns an indication of the
potential problem to the simulated model (block 248). The computing
system 10 analyzes aligned indications of potential problems on the
simulated model to determine an area of interest for the type of
part (block 250). The computing system 10 may generate a display
representation of the simulated model that visually represents
indications of problems aligned to the simulated model (block 252).
If the computing system 10 does not determine that any aligned NDE
data points indicate a potential problem (`N` branch of block 246),
the computing system 10 may generate the display representation
without any indications (block 252).
[0098] FIG. 11 provides flowchart 260 that illustrates a sequence
of operations that may be performed by the computing system 10 when
processing the simulated model of the type of part that includes
aligned indications of potential problems (block 262). In some
embodiments, the computing system may generate a display
representation of the simulated model that visually represents the
aligned indications of potential problems (block 264), and the
computing system 10 may receive user input data that indicates an
area of interest for the type of part based on the display
representation (block 266).
[0099] FIG. 12 provides flowchart 280 that illustrates operations
that may be performed by the computing system 10 consistent with
embodiments of the invention to monitor the manufacturing process.
The computing system may receive an NDE dataset for each of a
plurality of parts of a type of part (block 282). The computing
system 10 may align the NDE datasets to a simulated model of the
type of part (block 284), and the computing system 10 may analyze
at least a subset of the data points for each NDE dataset to
determine a spatially correlates statistic for each part (block
286). Based on the spatially correlated statistics, the computing
system 10 may generate a control chart that includes each spatially
correlated statistic (block 288). In some embodiments, the
computing system 10 may determine a manufacturing trend for the
type of part based on each spatially correlated statistic (block
290), and the computing system may determine an area of interest
for the type of part based on the manufacturing trend (block 292).
In some embodiments, the computing system 10 may generate a display
representation of the simulated model that visually represents the
spatially correlated statistics on the simulated model (block
294).
[0100] FIGS. 13-16 provide flowcharts that illustrate sequences of
operations that may be performed by the computing system 10 and/or
user device 12 consistent with embodiments of the invention to
monitor manufacture of a type of part by a manufacturing process
that includes one or more manufacturing steps. Specifically,
referring to FIG. 13, which provides flowchart 300, the computing
system 10 may receive an NDE dataset for each of a plurality of
parts of the type of part, where each NDE dataset is associated
with an area of interest for the type of part (block 302). The
computing system 10 aligns the NDE data points of each NDE dataset
to a simulated model of the area of interest for the type of part
(block 304), and the computing system 10 analyzes the aligned NDE
data points for each part to determine a statistic associated with
the area of interest for each part (i.e., a spatially correlated
statistic) (block 306). The computing system 10 may align each
statistic to the simulated model (block 308), and generate a
display representation of the simulated model that visually
represents each aligned statistic on the simulated model (block
310). In some embodiments, the computing system 10 may generate a
control chart for the manufacturing process associated with the
area of interest for the type of part (block 312).
[0101] Turning now to FIG. 14, this figure provides flowchart 320,
which illustrates operations that the computing system 10 may
perform when processing the simulated model of the area of interest
that includes aligned statistics for the area of interest (block
322). The computing system 10 may determine a manufacturing trend
for the manufacturing process associated with the area of interest
(block 324). In some embodiments the computing system may generate
a display representation of the simulated model of the area of
interest that visually represents the manufacturing trend on the
simulated model (block 326).
[0102] In some embodiments of the computing system may analyze the
manufacturing trend and base line data associated with the
simulated model of the type of part to determine whether the
manufacturing process is operating properly (block 328). In
response to determining that the manufacturing process is operating
properly (`Y` branch of block 330), the computing system 10 may
continue analyzing the manufacturing trend as the manufacturing
trend updates based on received NDE data. In response to
determining that the manufacturing process is not operating
properly (`N` branch of block 330), the computing system 10 may
determine a root cause problem for the manufacturing process
associated with the area of interest (block 332).
[0103] FIG. 15 provides a flowchart 340 that illustrates operations
that the computing system 10 may perform when processing the
simulated model of the area of interest including aligned
statistics associated with the area of interest (block 342). The
computing system 10 may receive manufacturing data (block 344),
where the manufacturing data indicates one or more possible root
cause problems associated with the area of interest. In some
embodiments, the manufacturing data may correspond to the type of
NDE data from which the aligned statistics were determined. For
example, if the NDE data corresponded to measured porosity values
and the statistic associated with the area of interest for each
part was an average porosity, the manufacturing data may indicate
possible root cause problems associated with porosity values. The
computing system may analyze the manufacturing data and the aligned
statistics to determine a root cause problem associated with the
area of interest (block 348).
[0104] In some embodiments of the invention, the manufacturing data
may further indicate one or more manufacturing steps, one or more
manufacturing apparatuses, one or more manufacturing tools, and/or
one or more manufacturing parameters associated with the area of
interest, the root cause problem, and/or the other types of
indicated data. Therefore, consistent with these embodiments of the
invention, the computing system 10 may determine a manufacturing
step associated with the root cause problem and/or area of interest
(block 350). Similarly, the computing system 10 may determine a
manufacturing apparatus associated with the root cause problem, the
area of interest, and/or the determined manufacturing step (block
352). In addition, the computing device 10 may determine a
manufacturing tool associated with the root cause problem, the area
of interest, the determined manufacturing step, and/or the
determined manufacturing apparatus (block 354). Furthermore, the
computing device 10 may determine a manufacturing parameter
associated with the root cause problem, the area of interest, the
manufacturing step, and/or the manufacturing apparatus (block
356).
[0105] FIG. 16 provides flowchart 360 that illustrates operations
that may be performed by the computing system 10 after a process
adjustment is implemented for the manufacturing process (block
362). The computing system 10 receives one or more NDE datasets
collected from one or more parts manufactured after implementation
of the process adjustment (block 364). The computing system 10
aligns the one or more NDE datasets to the simulated model (block
366), and the computing system analyzes the aligned NDE datasets to
determine a statistic for the area of interest (i.e., a spatially
correlated statistic) for each part manufactured after the process
adjustment implementation (block 368). The computing system 10 may
evaluate the process adjustment to determine whether the process
adjustment corresponds to the root cause problem by analyzing the
statistics for the area of interest for each part manufactured
after the process adjustment and the statistics for the area of
interest for each part manufactured before the process adjustment
(block 370). In some embodiments, the computing system 10 may
further determine the extent to which the process adjustment
affected the root cause problem, where the extent may be defined
based at least in part on the difference in the statistics for the
area of interest for the parts manufactured after the process
adjustment and the statistics for the area of interest for the
parts manufactured before the process adjustment.
[0106] FIG. 17 provides a flowchart 380 that illustrates a sequence
of operations that may be performed by the computing system 10
consistent with embodiments of the invention to monitor the
manufacture of a type of part by a manufacturing process that
includes a plurality of manufacturing steps. The computing system
10 receives an NDE dataset for each of a plurality of parts of the
type of part manufactured by the manufacturing process, where the
NDE dataset for each part is associated with an area of interest
for the type of part (block 382). The computing system 10 aligns
the NDE datasets to a simulated model of the area of interest for
the type of part (block 384), and the computing system analyzes the
aligned NDE datasets detect any sub-rejectable physical
characteristic(s) associated with the area of interest for each
part. In general, a sub-rejectable physical characteristic refers
to a physical characteristic that is within an acceptable range for
the type of part, but that is outside an expected range (i.e., the
sub-rejectable physical characteristic is acceptable but outside
the range associated with noise in the manufacturing process). In
some embodiments pre-defined values associated with the simulated
model may define sub-rejectable ranges, where such the
sub-rejectable range may be proximate a minimum or maximum limit of
the acceptable range and/or not be within a typical/expected range.
In general, an NDE data point may indicate a measured value for a
location on a part from which the NDE data point was collected, and
if the measured value is proximate a limit associated with an
acceptable range for the value, embodiments of the invention may
identify the location on as a sub-rejectable physical
characteristic. Moreover, the model data of the simulated model may
define values that correspond to sub-rejectable physical
characteristics.
[0107] In some embodiments of the invention, the computing system
may align an indication of each detected sub-rejectable physical
characteristic to a corresponding simulated location on the
simulated model (block 388). In addition, the computing system 10
may generate a control chart that includes indications for each
detected sub-rejectable for the type of part (block 390). The
computing system 10 may analyze the control chart and/or aligned
indications to determine whether a potential problem is occurring
for the manufacturing process (block 392). The computing system 10
may determine a manufacturing trend for the manufacturing process
based at least in part on the aligned indications and/or the
control chart (block 394). In some embodiments, the computing
system 10 may analyze the control chart, one or more baseline
values associated with the area of interest, and/or the
manufacturing trend to determine whether the control chart
indicates a potential problem in the manufacturing process (block
396). As discussed previously, a potential problem may be indicated
by data that indicates that the manufacturing process is
manufacturing parts that are trending towards a limit of an
acceptable range for one or more physical characteristics. Hence,
while the manufacturing process may be manufacturing acceptable
parts, based on the NDE data and/or quality related data for each
manufactured part, the computing system 10 may determine that a
potential problem is occurring in the manufacturing process.
[0108] FIG. 18 provides a flowchart 420 that illustrates a sequence
of operations that may be performed by the computing system 10 to
model the manufacture of a type of part by a manufacturing process
that includes at least one manufacturing step. The computing system
may receive at least one NDE dataset for at least one part of the
type of part (block 422), and the computing system 10 may align the
NDE dataset to a simulated model of the type of part (block 424).
The computing system may receive manufacturing data associated with
the manufacturing process (block 426) and associate the
manufacturing data with the simulated model (block 428). Based on
the simulated location of aligned NDE data, manufacturing data may
be associated with particular NDE data (block 430), such that the
computing system may: identify a manufacturing step associated with
particular NDE data aligned to one or more particular simulated
locations (block 432); identify a manufacturing apparatus
associated with particular NDE data aligned to one or more
particular simulated locations (block 434); identify a
manufacturing tool associated with particular NDE data aligned to
one or more particular simulated locations (block 436); and/or
generate a display representation of the simulated model that
visually represents at least some aligned NDE data and
manufacturing data associated therewith (block 438). Hence, in
these embodiments, data associated with aspects of the
manufacturing process may be spatially organized on a simulated
model of the type of part, such that the data associated with the
manufacturing process may be spatially correlated with NDE data
and/or quality related data collected from one or more parts
manufactured by the manufacturing process. Therefore, consistent
with these embodiments of the invention, the manufacturing process
may be modeled on the simulated model of the type of part.
[0109] Turning now to FIG. 19, this figure provides a flowchart 460
that illustrates a sequence of operations that may be performed by
the computing system 10 to analyze a manufactured part of a type of
part manufactured by a manufacturing process. The computing system
receives an NDE dataset associated with the manufactured part
(block 462) and aligns the NDE dataset to a simulated model
associated with the type of part (block 464), where such aligning
includes aligning NDE data of the NDE dataset associated with an
area of interest on the manufactured part to at least one
corresponding simulated location on the simulated model. The
computing system 10 may analyze the NDE data aligned to the area of
interest to determine a spatially correlated statistic for the area
of interest for the manufactured part (block 466). The computing
system 10 may compare the spatially correlated statistic to a
baseline value associated with the area of interest (block 468).
Based at least in part on the NDE data aligned to the area of
interest and/or the comparison of the spatially correlated
statistic to the baseline value, the computing system 10 may
determine whether the manufactured part includes a manufacturing
defect associated with the area of interest (block 470). In
general, a manufacturing defect corresponds to a physical
characteristic that is not within an acceptable range, where the
acceptable range is predefined. If a manufacturing defect is not
detected for the manufactured part (`N` branch of block 470), then
the analysis process ends (block 472).
[0110] In response to detecting a defect for the manufactured part
(`Y` branch of block 470), the computing system 10 may align the
detected defect to the simulated model (block 474). The computing
system 10 may analyze manufacturing data associated with the
simulated location of the aligned defect to determine a root cause
problem associated with the simulated location and/or detected
defect (block 478).
[0111] In addition, in response to detecting a defect for the
manufactured part (`Y` branch of block 470), the computing system
10 may determine a manufacturing step associated with the defect
based at least in part on the simulated location of the aligned
defect (block 480). Similarly, the computing system 10 may
determine a manufacturing apparatus associated with the defect
based at least in part on the simulated location of the aligned
defect and/or the determined manufacturing step (block 482).
Furthermore, the computing system 10 may determine a manufacturing
tool associated with the defect based at least in part on the
simulated location of the aligned defect, the determined
manufacturing step, and/or the determined manufacturing
apparatus.
[0112] Referring to FIG. 20, this figure provides flowchart 500
that illustrates a sequence of operations that may be performed by
the computing system 10 to monitor the manufacture of composite
aircraft parts of a type of part by a manufacturing process. In
general, the production of composite aircraft parts may be a
complicated and expensive process, where producing even one
defective part may result in significant time and cost losses.
Therefore, in this embodiment of the invention, the manufacturing
process is monitored continuously as NDE data and/or quality
related data is collected from one or more of the aircraft parts
during and immediately following manufacture of each part. In this
manner, embodiments of the invention may monitor whether the
manufacturing process is operating properly to reduce the
probability of time and cost losses due to the development of a
problem in the manufacturing process. As discussed, the NDE
datasets are received continuously, and processing and analysis
based thereon is performed in a continuous manner. Hence, flowchart
500 may be considered a snapshot of the continuously performed
operations consistent with some embodiments of the invention.
[0113] The computing system 10 receives NDE datasets for each of a
plurality of composite aircraft parts manufactured in the
manufacturing process (block 502). The computing system 10 aligns
the received NDE datasets to a simulated model of the type of part
(block 504), and the computing system 10 analyzes the aligned NDE
datasets to determine a spatially correlated statistic for each
composite aircraft part of the type (block 506). The computing
system 10 aligns the spatially correlated statistics to the
simulated model (block 508). In some embodiments, the computing
system 10 receives manufacturing data associated with the type of
part (block 510), and the computing system 10 associates the
manufacturing data with the simulated model (block 512). The
manufacturing data may include data that indicates: at least one
manufacturing step of the manufacturing process associated with one
or more physical locations on the type of part, data that indicates
a manufacturing apparatus utilized in the manufacturing process
associated with at least one physical location on the type of part;
a manufacturing parameter of the manufacturing process associated
with at least one physical location on the type of part; a
manufacturing tool utilized in the manufacturing process associated
with at least one physical location on the type of part; at least
one possible root cause problem associated with the manufacturing
process and at least one physical location on the type of part.
[0114] The computing system may generate a display representation
of the simulated model that visually represents the spatially
correlated statistics, manufacturing data, and/or NDE data of the
NDE datasets aligned on the simulated model (block 514). In some
embodiments, the computing system 10 determines a manufacturing
trend for the manufacturing process based at least in part on the
spatially correlated statistics (block 516), and the computing
system may analyze the manufacturing trend, NDE data, and/or
baseline data associated with the simulated model to determine
whether the manufacturing process is operating properly (blocks
518-520). In response to determining that the manufacturing process
is operating properly (`Y` branch of block 520), the computing
system 10 continues monitoring the manufacturing process. In
response to determining that the manufacturing process is not
operating properly (`N` branch of block 522), the computing system
10 may generate output data that indicates that the manufacturing
process is not operating properly and/or the computing system 10
may determine a root cause problem associated with the
manufacturing process based at least in part on the spatially
correlated statistics, manufacturing trend, and/or manufacturing
data (block 522). In general, the output data may be communicated
such that an alarm or other such notification is generated for an
operator/technician/supervisor associated with the manufacturing
process.
[0115] FIG. 21 provides a flowchart 540 that illustrates a sequence
of operations that may be performed by the computing system 10 to
analyze manufacture of a type of part by a manufacturing process.
The computing system may receive an NDE dataset for each part of a
plurality of parts of the type of part (block 542). The computing
device 10 aligns the NDE datasets to a simulated model associated
with the type of part (block 544) and analyzes the aligned NDE
datasets (block 546) to determine whether the manufacturing process
is operating properly (block 548). In response to determining that
the manufacturing process is operating properly (`Y` branch of
block 548), the computing system 10 may continue analyzing the
manufacturing process as NDE datasets are received.
[0116] In response to determining that the manufacturing process is
not operating properly (`N` branch of block 548), the computing
system 10 may determine a root cause problem associated with the
manufacturing process based at least in part on the aligned NDE
data (block 550). Furthermore, the computing system 10 may identify
one or more other aspects of the manufacturing process based on the
aligned NDE data, including at least one manufacturing step (block
552), at least one manufacturing parameter associated with the
manufacturing step (block 554), at least one manufacturing
apparatus (block 556), and/or at least one manufacturing tool
(558). In some embodiments, the computing system may determine the
root cause problem based at least in part on the one or more
identified aspects of the manufacturing process. In some
embodiments, the computing system 10 may generate output data
responsive to determining that the manufacturing process is not
operating properly (block 560). The output data may be communicated
to provide a notification that the manufacturing process is not
operating properly, and the output data may include the determined
root cause problem and/or one or more identified manufacturing
aspects.
[0117] FIG. 22 provides a flowchart 580 that illustrates a sequence
of operations that may be performed by the computing system 10 to
analyze the manufacture of a type of part by a manufacturing
process. The computing system may receive NDE data associated with
the type of part, where the NDE data includes associated inspection
information (block 582). The computing system may automatically
align the NDE data including the inspection information to
corresponding simulated locations on a simulated model associated
with the type of part (block 584), and the computing system may
analyze the aligned NDE data to monitor the manufacturing process
and determine whether the manufacturing process is operating
properly (block 586).
[0118] FIG. 23 provides a flowchart 600 that illustrates a sequence
of operations that the computing system may perform to monitor the
manufacturing process. When the monitor is initialized (block 602),
the computing system 10 determines whether the aligned NDE data
and/or inspection information indicates a potential problem
associated with the manufacturing process (blocks 604-606). In
response to determining that the aligned NDE data and/or the
inspection information does not indicate a potential problem (`N`
branch of block 606), the computing system 10 continues analyzing
the NDE data and/or inspection information as it is received and
aligned.
[0119] In response to determining that the aligned NDE data and/or
inspection information indicates a potential problem (`Y` branch of
block 606), the computing system 10 may align an indication of the
potential problem to the simulated model (block 608). The computing
system 10 may generate a display representation of the simulated
model that visually represents the aligned indication on the
simulated model (block 610). In some embodiments the computing
system 10 may analyze the one or more aligned indications to
determine a root cause problem associated with the manufacturing
process and the aligned indication (block 612).
[0120] FIG. 24 provides flowchart 640 that illustrates a sequence
of operations that the computing system may perform to determine a
root cause problem based on a plurality of indications of potential
problems aligned to the simulated model (block 642). The computing
system 10 may analyze the corresponding simulated locations of the
simulated model to which the indications are aligned to identify a
pattern for the corresponding location (block 644). The computing
system 10 may determine a root cause problem for the manufacturing
process based at least in part on the identified pattern (block
646).
[0121] FIG. 25 provides flowchart 660 that illustrates a sequence
of operations that may be performed by the computing system 10
consistent with embodiments of the invention when processing the
simulated model with aligned indications of potential problems
(block 662). The computing system 10 may analyze manufacturing data
associated with the one or more simulated locations of the one or
more aligned indications (block 664). In some embodiments, the
computing system 10 may suggest one or more possible root cause
problems based at least in part on the manufacturing data (block
668). In these embodiments, the computing system 10 may output data
via a user interface to a user, where the output data includes the
one or more suggested possible root cause problems. The computing
system receives user input data that selects one or more root cause
problems from the suggested possible root cause problems (block
670), and the computing system 10 may associate the one or more
selected root cause problems with one or more aligned indications
(block 672). In some embodiments, the computing system 10 may
generate and/or update manufacturing data associated with the type
of part based at least in part on the one or more selected root
cause problems, the associated aligned indications, and/or the
simulated locations (block 674). Consistent with some embodiments,
the computing system 10 may determine a root cause problem
associated with the manufacturing process based on manufacturing
data associated with the simulated model, one or more aligned
indications, simulated locations of the aligned indications, and/or
aligned NDE data (block 676).
[0122] Turning now to FIG. 26, this figure provides a flowchart 700
which illustrates a sequence of operations that may be performed by
the computing system 10 to analyze manufacture of a type of part by
a manufacturing process. The computing system receives NDE data
that includes inspection information and a plurality of indications
of one or more potential problems (block 702) for a part of the
type of part, and the computing system 10 may align the NDE data to
a simulated model associated with the type of part (block 704). The
computing system generates a display representation of the
simulated model that visually represents the aligned indications on
the simulated model (block 706), and the computing system 10 may
receive data that indicates a root cause problem associated with at
least one particular indication (block 708). The computing system
associates the root cause problem with the at least one particular
indication (block 710), and the computing system may generate
manufacturing data based on the at least one particular indication
and the root cause problem (block 712).
[0123] FIG. 27 provides a flowchart 720 that illustrates operations
that may be performed by the computing system 10 to analyze the
manufacturing process based at least in part on the simulated
model, aligned NDE data, and manufacturing data of FIG. 26 (block
722) (i.e., first NDE data from a first part). The computing system
10 may receive second NDE data that includes inspection information
and at least one indication of at least one potential problem for a
second part of the type of part (block 724), and the computing
system aligns the NDE data to the simulated model (block 726). The
aligned second NDE data and aligned indication may be analyzed by
the computing system 10 to determine whether the aligned at least
one indication of the second NDE data is related to any indications
of the first NDE data based at least in part on the manufacturing
data (blocks 728-730). In response to determining that one or more
indications of the second NDE data are related to one or more
indications of the first NDE data (`Y` branch of block 730), the
computing system may associate the root cause problem associated
with the one or more related indications of the first NDE data to
the one or more related indications of the second NDE data (block
732). If the one or more indications of the second NDE data are not
determined to be related to any indications of the first NDE data,
the computing system 10 may process the indications of the second
NDE as described above with respect to FIG. 26 to determine an
associated root cause problem (block 734).
[0124] FIG. 28 provides flowchart 760 that illustrates operations
that may be performed by the computing system 10 to determine a
root cause problem associated with NDE data and/or one or more
indications of one or more potential problems aligned to the
simulated model of FIG. 26 (block 762). The computing system 10 may
analyze the aligned NDE data and/or indications and manufacturing
data associated with the simulated locations to which the NDE data
and/or indications are aligned to determine a root cause problem
associated with each of the one or more aligned indications (block
764). The computing system 10 may generate data that indicates the
determined root cause problem and the associated one or more
indications (block 766).
[0125] Turning now to FIG. 29, this figure provides flowchart 800
that illustrates a sequence of operations that may be performed by
the computing system 10 to model the manufacture of a type of part
by a manufacturing process. The computing system 10 may analyze NDE
data associated with the manufacturing process to determine
indications of potential problems associated with the manufacturing
process (block 802), and the computing system 10 aligns the NDE
data and indications to a simulated model associated with the type
of part (block 804). The computing system 10 may receive request
data that indicates a root cause problem associated with the
manufacturing process (block 806). The computing system 10
identifies aligned indications associated with the root cause
problem (block 808), and the computing system 10 filters any
indications not associated with the root cause problem out of the
simulated model (block 810). In some embodiments, the computing
system 10 may analyze the aligned NDE data to determine a spatially
correlated statistic associated with the root cause problem (block
812). Furthermore, the computing system may generate a display
representation of the simulated model that visually represents the
aligned indications and/or spatially correlated statistic
associated with the root cause problem on the simulated model
(block 814).
[0126] FIG. 30 provides a flowchart 840 that illustrates operations
that may be performed by the computing system 10 when processing
the simulated model and the aligned NDE data of FIG. 29 (block
842). The computing system 10 may generate a display representation
of the simulated model that visually represents the aligned
indications on the simulated model (block 844). The computing
system 10 may receive user input data that indicates a root cause
problem for the manufacturing process (block 846), and the
computing system 10 may generate request data that indicates the
root cause problem based on the user input data (block 848). The
computing system may update the display representation based on the
request data such that the display representation visually
represents only indications associated with the root cause problem
(block 850).
[0127] Turning to FIG. 31, this figure provides a flowchart 860
that illustrates operations that may be performed by the computing
system 10 when processing the simulated model and the aligned NDE
data of FIG. 29 (block 862). The computing system 10 may receive
manufacturing data associated with the manufacturing process (block
864) and associate the manufacturing data with one or more
simulated locations on the simulated model (block 866). The
computing system may determine one or more indications and
manufacturing data associated with the root cause problem of the
received request of FIG. 29 (block 868), and the computing system
10 may filter the simulated model, aligned indications, and the
manufacturing data to remove the aligned indications and
manufacturing data not associated with the root cause problem
(block 870).
[0128] Referring to FIG. 32, this figure provides a flowchart 880
that illustrates a sequence of operations that may be performed by
the computing system 10 to monitor a manufacturing process that
manufactures a type of part. The computing system 10 may receive
non-compliance report data for each of a plurality of parts
manufactured by the manufacturing process (block 882), where each
non-compliance report indicates at least one visually detected
defect corresponding to a location on the respective part that is
associated with the non-compliance report. In general, a
non-compliance report and visually detected defect thereof may not
be as location specific as NDE data; hence, in many embodiments the
particular location of the visually detected defect may correspond
to an area, volume, region, and/or other such spatially related
feature. The computing system 10 aligns each visually detected
defect to a simulated model of the type of part (block 884), where
each visually detected defect is aligned to one or more simulated
locations on the simulated model that correspond to the particular
location of the visually detected defect on the respective part.
The computing system 10 may monitor the manufacturing process by
analyzing the aligned visually detected defects (block 886).
[0129] In some embodiments, the computing system 10 may generate a
display representation of the simulated model that includes the one
or more aligned visually detected defects (block 888). In addition,
the computing system 10 may generate manufacturing data based at
least in part on the aligned visually detected defects (block 890).
Furthermore, the computing system may determine whether a
manufacturing problem is occurring (block 892). In general, if the
computing system 10 detects a plurality of visually detected
defects aligned to common and/or related simulated locations, the
computing system 10 may determine that a manufacturing problem is
occurring.
[0130] With reference to FIG. 33, this figure provides a flowchart
920 that illustrates a sequence of operations that may be performed
by the computing system 10 to monitor a manufacturing process that
manufactures a type of part. The computing system 10 may receive
indication data that includes indications of potential problems at
locations on parts of the type of part manufactured by the
manufacturing process (block 922), and the computing system aligns
the indications to a simulated model of the type of part (block
924), where each indication is aligned to one or more simulated
locations on the simulated model that correspond to the location on
the part at which the potential problem was detected. The computing
system 10 may receive manufacturing data associated with the
manufacturing process that includes one or more possible root cause
problems associated with the manufacturing process (block 926). The
computing system may analyze the aligned indications and the
received manufacturing data and associate the manufacturing data
with the aligned indications (block 928). In some embodiments, the
computing system 10 may generate a display representation of the
simulated model that visually represents the aligned indications
and/or the manufacturing data with the simulated model (block
930).
[0131] FIG. 34 provides a flowchart 940 that illustrates operations
the computing system 10 may perform when processing the display
representation of the simulated model including the aligned
indications of FIG. 33 (block 942). The computing system 10 may
receive user input data that selects a particular root cause
problem (block 946), and the computing system 10 may filter the
display representation to remove indications not associated with
the selected root cause problem (block 948). In other embodiments,
the computing system 10 may filter the display representation to
remove indications associated with the selected root cause problem
(block 950).
[0132] FIG. 35 provides a flowchart 940 that illustrates operations
the computing system 10 may perform related to the operations of
FIG. 33, where the aligned indications of FIG. 33 are a first set
of aligned indications, when processing the display representation
of the simulated model including the aligned indications of FIG. 33
(block 962). The computing system 10 may receive second indication
data including a second set of indications of potential problems at
locations on parts of the type of part (block 964), and the
computing system 10 may align the second set of indications (block
966). Based on the root cause problem associated with the first set
of aligned indications and the simulated locations thereof, the
computing system 10 may generate data that suggests a possible root
cause problem (i.e., a root cause hypothesis) for at least one
indication of the second set of aligned indications (block 968).
Hence, the computing system may rely on previously identified root
cause problems to determine possible root cause problems for
received indications of potential problems.
[0133] FIG. 36 provides a flowchart 980 that illustrates operations
the computing system 10 may perform when processing the display
representation of the simulated model including the aligned
indications of FIG. 33 (block 982). The computing system 10 may
receive user input data that identifies a root cause problem
associated with one or more identified aligned indications (block
984), and the computing system 10 may associate the identified root
cause problem to the identified one or more aligned indications
(block 986). In some embodiments, the computing system 10 may
filter the display representation to remove aligned indications
associated with the identified root cause problem from the display
representation (block 988).
[0134] Turning now to FIG. 37, this figure provides an example
illustration of a graphical user interface (GUI') 1000 that may be
output to a display by a processor executing an application on the
user device 12 and/or computing system 10. In this example, the GUI
1000 includes a three dimensional generated display representation
of a simulated model of a type of composite aircraft part 1002. In
this example, the GUI 1000 may include the display representation
1002 as well as facilitate user interface with the display
representation to input data (such as selecting an area of interest
on the display representation) via one or more included interface
features 1004 (i.e., selection buttons, text input boxes, etc.).
FIG. 38 provides an example illustration of the GUI 1000 of FIG.
37, where the generated display representation 1002 of FIG. 38
includes aligned NDE data 1006, which in the example, corresponds
to aligned ultrasonic scan data that may be utilized to determine a
porosity value and/or other such physical characteristic at a
location of a part. FIGS. 39A-C provide an example illustration of
the GUI 1000 of FIG. 38, where the generated display representation
1002 of FIGS. 39A, B includes aligned indications of potential
problems 1008. FIG. 39B provides a close up view of the selected
area 1010 of FIG. 39A to better illustrate the aligned indications
1008. FIG. 39C provides the interface features 1004 portion of the
GUI 1000, where the interface features 1004 portion of the GUI 1000
includes information related to the aligned indications 1012. FIG.
40 provides an example illustration of the GUI 1000 of FIGS.
37-39A-C, where the user is interfacing with the GUI to select an
area of interest 1014 on the display representation 1002.
[0135] FIGS. 41A-B provide an example illustration of a GUI 1050
that may be output to a display by a processor executing an
application on the user device 12 and/or computing system 10. In
this example, the GUI 1050 includes a generated display
representation of a simulated model of a type of composite aircraft
part 1052 manufactured by a manufacturing process. In this example,
the GUI 1050 may include the display representation 1052 as well as
facilitate user interface with the display representation to input
data (such as selecting an area of interest on the display
representation) via one or more included interface features 1054.
In this example, a plurality of indications 1056 associated with a
first part of the type of part are aligned to the display
representation of the simulated model 1052. As shown in the
interface features 1054 of FIG. 41B, the indications 1056
correspond to `Porosity` indications of a part numbered `Serial
Number=Unit 100`. FIGS. 42A-B provide an example illustration of
the GUI 1050 of FIGS. 41A-B, where the display representation of
the simulated model of the type of aircraft part 1052 includes a
plurality of aligned indications 1058 associated with a second part
of the type of part. As shown in the interface features 1054 of
FIG. 42B, the indications 1056 correspond to `Porosity` indications
of a part numbered `Serial Number=Unit 101`. In this example, the
number of indications increases between the first part and the
second part, which may be used to determine a manufacturing trend
for the type of part, and/or determine that the manufacturing
process for the type of part is not operating properly. In this
particular example, the increase in the number of indications
between Unit 100 and Unit 101 indicates that a seam in a mold used
in the manufacturing process is likely wearing over time. For
example, one or more seals and/or gaskets may be leaking air into
the mold. If similar indications had been previously experienced on
parts made in the manufacturing process and manufacturing data
associated with a root cause problem was stored, embodiments of the
invention may suggest the same root cause problem on the GUI by
analyzing the aligned indications, manufacturing trend, and/or
manufacturing data.
[0136] FIG. 43 provides an example control chart 1100 that may be
generated by the computing system 10 and/or user device 12 for a
manufacturing process based at least in part on NDE data, quality
related data, spatially correlated statistics, and/or manufacturing
data associated with the manufacturing process. In this example, a
plurality of spatially correlated statistics 1102 may be included
on the control chart, where each spatially correlated statistic
1102 corresponds to a part of a type of part manufactured by the
manufacturing process. Based on the spatially correlated statistics
1102, a manufacturing trend 1104 may be determined. As discussed, a
baseline value and/or an acceptable range may be defined for a type
of part, which in this example is represented by dashed line 1106.
As shown, many of the spatially correlated statistics 1102 are
acceptable based on the acceptable range 1106. Moreover, based on
the manufacturing trend 1104, embodiments of the invention may be
able to detect that the manufacturing process will begin producing
unacceptable parts prior to actually manufacturing unacceptable
parts (i.e., parts corresponding to the spatially correlated
statistics 1102 that are above the acceptable range 1106). For
example, the highlighted spatially correlated statistics 1108 may
indicate parts in a sub-rejectable range, and embodiments of the
invention may determine that the manufacturing process is not
operating properly based at least in part on the trend 1104 and/or
the highlighted spatially correlated statistics 1108, such that
process adjustments may be made prior to the manufacture of
unacceptable parts.
[0137] While the example illustrates a single control chart 1100
for a single spatially correlated statistic 1102 collected from
each part, embodiments of the invention are not so limited. In
general, embodiments of the invention may monitor a manufacturing
process by collecting and monitoring a plurality of spatially
correlated statistics for each part, where each spatially
correlated statistic for each part may be included on a
corresponding control chart. Therefore, embodiments of the
invention may monitor a plurality of aspects of each part
manufactured by a manufacturing process continuously. In some
embodiments such monitoring may be substantially in real-time, such
that developing and/or potential problems may be addressed in an
efficient manner to reduce the production of unacceptable
parts.
[0138] While the present invention has been illustrated by a
description of the various embodiments and the examples, and while
these embodiments have been described in considerable detail, it is
not the intention of the applicants to restrict or in any way limit
the scope of the appended claims to such detail. Additional
advantages and modifications will readily appear to those skilled
in the art. For example, one having skill in the art will
appreciate that multiple filters may be used without departing from
the scope of the invention. Moreover, one having skill in the art
will appreciate that a plurality of datasets of NDE data from a
plurality of portions of a plurality of parts may be processed
without departing from the scope of the invention, and thus
embodiments of the invention should not be limited to the modeling,
monitoring, and analyzing examples disclosed herein.
[0139] Thus, the invention in its broader aspects is therefore not
limited to the specific details, representative apparatus and
method, and illustrative example shown and described. Accordingly,
departures may be made from such details without departing from the
spirit or scope of applicants' general inventive concept.
* * * * *