Manufacture modeling and monitoring

• US 9,864,366 B2
• Filed: 03/14/2014
• Issued: 01/09/2018
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Etegent Technologies, Ltd.
• Original Assignee: Etegent Technologies, Ltd.
• Inventors: Kesler, Joseph M., Sharp, Thomas D., Liggett, Uriah M., Bahr, Brian, Hodapp, Chris M., Coyan, Gary E.
• Primary Examiner(s): Kasenge, Charles
• Application Number:
  US14/211,600
• Publication Number:
  US 20140277662A1
• Time in Patent Office: 1,397 Days
• CPC Class Codes: B64F 5/60 Testing or inspecting aircraft components or systems
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/32189 Compare between original solid model and measured manufactured object
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/04 Manufacturing
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y02P 90/30 Computing systems specially adapted for manufacturing

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.

Claims

1. A method of monitoring manufacture of a particular type of part by a manufacturing process including a plurality of manufacturing steps in a system of the type that includes at least one processing unit and a memory, the method comprising:

receiving a non-destructive evaluation (NDE) dataset, wherein the NDE dataset is associated with a manufactured part of the particular type of part, wherein the NDE dataset for the manufactured part includes a plurality of NDE data points and inspection information, wherein each NDE dataset corresponds to data collected during a non-destructive evaluation process of the particular part;

aligning the NDE dataset to a simulated model associated with at least a portion of the particular type of part, including aligning each NDE data point to a corresponding simulated location on the simulated model wherein the step of aligning the NDE dataset involves;

determination, from an analysis of the NDE dataset, of the non-destructive evaluation process by which the NDE data was collected;

selection of an alignment algorithm based on the determined non-destructive evaluation process;

using the selected alignment algorithm, transformation of the NDE data points into a coordinate system of the simulated model for locating the NDE data points on the simulated model;

analyzing at least a subset of the aligned NDE data points and the simulated location corresponding to each aligned NDE data point of the subset to determine a spatially correlated statistic which is a value that is at least one of a measurement or calculated from a measurement that is associated with the manufactured part and that corresponds to a spatial area of that part and is based at least partially on the NDE data points of the subset and the corresponding simulated locations for the particular part; and

generating output data based at least partially on the spatially correlated statistic.

2. The method of claim 1, further comprising aligning the spatially correlated statistic to the simulated model based on the corresponding simulated locations of the subset of NDE data points.

3. The method of claim 2, further comprising:

comparing the spatially correlated statistic to a spatially correlated baseline value for the type of part to determine whether the spatially correlated statistic for the particular part is acceptable for the type of part.

4. The method of claim 1, wherein the NDE dataset is a first NDE dataset, wherein the manufactured part is a first part of the particular type of part, wherein the first NDE dataset includes a first plurality of NDE data points, wherein the subset is a first subset, and wherein the spatially correlated statistic is a first spatially correlated statistic, the method further comprising

receiving a second NDE dataset, wherein the second NDE dataset is associated with a second manufactured part of the particular type of part, wherein the second NDE dataset for the second part includes a second plurality of NDE data points;

and

aligning the second NDE dataset to the simulated model, including aligning each NDE data point of the second plurality of NDE data points to a corresponding simulated location on the simulated model using the selected algorithm for transforming the second NDE data points in order to locate the second NDE data points on the simulated model;

analyzing at least a second subset of the aligned NDE data points of the second plurality and locations corresponding thereto to determine a second spatially correlated statistic based at least partially on the NDE data points of the second subset and the corresponding simulated locations, wherein the output data is based at least partially on the second spatially correlated statistic.

5. The method of claim 4, further comprising:

generating a control chart for the manufacturing process associated with the type of part including the first spatially correlated statistic and the second spatially correlated statistic.

6. The method of claim 4 further comprising:

analyzing the first spatially correlated statistic and the second spatially correlated statistic to determine a trend for the type of part based at least in part on the first spatially correlated statistic and the second spatially correlated statistic;

analyzing the trend to determine whether the manufacturing process for the particular type of part is operating properly.

7. The method of claim 6 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 first spatially correlated statistic and the second spatially correlated statistic.

8. The method of claim 7 further comprising:

generating manufacturing data indicating the determined problem and the corresponding simulated locations associated with the determined problem for the type of part.

9. The method of claim 7 further comprising:

in response to determining the root cause problem, determining at least one manufacturing step of a manufacturing process associated with the type of part corresponding to the root cause problem based at least in part on the root cause problem, the first spatially correlated statistic and the second spatially correlated statistic.

10. The method of claim 6 further comprising:

generating a control chart for the manufacturing process including the first location correlated statistic and the second location correlated statistic, wherein analyzing the trend to determine whether the manufacturing process for the type of part is operating properly includes analyzing the process control chart.

11. The method of claim 1, further comprising:

receiving data indicating an area of interest for the type of part, wherein the area of interest is associated with one or more corresponding simulated locations on the simulated model of the type of part; and

selecting the at least a subset of aligned NDE data points for analysis based on whether the corresponding simulated locations for such NDE data points is associated with the area of interest, wherein the determined spatially correlated statistic is associated with the area of interest.

12. The method of claim 11, wherein the area of interest for the type of part includes a key feature for the type of part.

13. The method of claim 12, wherein the key feature corresponds to a manufacturing process relevant feature for the type of part.

14. The method of claim 13, wherein the manufacturing process relevant feature corresponds to a manufacturing step of the manufacturing process associated with the type of part.

15. The method of claim 14, wherein the manufacturing step corresponds to a manufacturing tool utilized in the manufacturing process.

16. The method of claim 1, further comprising:

analyzing manufacturing data associated with the type of part indicating at least one problem associated with at least one corresponding simulated location on the simulated model of the type of part to determine an area of interest based at least in part on the indicated problem and the corresponding simulated locations; and

transmitting data indicating the determined area of interest.

17. The method of claim 16, wherein determining the area of interest includes identifying a plurality of proximate corresponding simulated locations on the simulated model of the type of part that include a plurality of related problems associated therewith, wherein the determined area of interest is associated with the identified plurality of proximate corresponding simulated locations.

18. The method of claim 1, further comprising:

generating a display representation that visually represents at least a portion of the simulated model of the type of part; and

receiving user input data indicating an area of interest on the display representation of the simulated model for the type of part; and

transmitting data indicating the area of interest for the type of part.

19. The method of claim 18, further comprising:

analyzing manufacturing data associated with the type of part indicating at least one problem associated with at least one corresponding simulated location on the simulated model of the type of part, wherein the display representation visually represents each problem indicated in the manufacturing data for the area of interest at the corresponding simulated location on the display representation of the at least a portion of the simulated model for the type of part.

20. The method of claim 18, wherein generating the display representation that visually represents at least a portion of the simulated model of the type of part, includes receiving user input data indicating an area of interest on the display representation of the simulated model for the type of part, and transmitting data indicating the area of interest for the type of part are performed prior to aligning the NDE dataset to the simulated model.

21. The method of claim 1, wherein the NDE dataset is a first NDE dataset, wherein the manufactured part is a first particular part, and wherein the plurality of NDE data points are a first plurality of NDE data points, the method further comprising:

receiving a plurality of additional NDE datasets each associated with an additional manufactured part of the particular type of part, wherein each additional NDE dataset includes an additional plurality of NDE data points and inspection information, wherein each NDE dataset is collected during a non-destructive evaluation process of the associated additional manufactured part;

aligning the additional NDE datasets to the simulated model, including aligning each additional NDE data point to a corresponding simulated location on the simulated model using the selected algorithm for transforming the additional NDE data points in order to locate the additional NDE data points on the simulated model;

analyzing the aligned additional NDE data points, wherein the output data is based at least in part on the analysis of the aligned additional NDE data points.

22. The method of claim 21, wherein analyzing the aligned additional NDE data points includes analyzing the aligned additional NDE data points to determine whether any of the aligned additional NDE data points indicate a potential problem at a corresponding simulated location on a manufactured part associated with such NDE data point, the method further comprising:

in response to an aligned additional NDE data point indicating a potential problem at a corresponding simulated location on the manufactured part associated with such NDE data point, aligning an indication of the potential problem on the simulated model.

23. The method of claim 22, further comprising:

analyzing any aligned indications on the simulated model to determine an area of interest for the type of part, wherein the corresponding simulated locations of the at least a subset of NDE data points of the of the NDE datasets are associated with the area of interest.

24. The method of claim 23, wherein analyzing any aligned indications on the simulated model to determine an area of interest for the type of part includes analyzing the aligned indications to identify indications aligned to proximate corresponding simulated locations on the simulated model, and wherein the area of interest for the type of part is based at least in part on identified indications aligned to proximate corresponding simulated locations.

25. The method of claim 21, further comprising:

generating a display representation of the simulated model that visually represents indications at the corresponding simulated locations on the simulated model; and

receiving user input indicating an area of interest on the simulated model, wherein the corresponding simulated locations of the at least a subset of NDE data points of the of the first NDE dataset are associated with the area of interest.

26. The method of claim 21, wherein analyzing the aligned additional NDE data points includes analyzing at least a subset of the aligned additional NDE data points associated with each additional manufactured part and the location corresponding to each aligned additional NDE data point of the subset of additional aligned NDE data points to determine a spatially correlated statistic based at least in part on the subset of additional aligned NDE data points and the corresponding simulated locations for each additional manufactured part, and wherein the spatially correlated statistic is a first spatially correlated statistic the method further comprising:

determining a trend for the type of part based at least in part on the spatially correlated statistics for each additional respective manufactured part; and

determining an area of interest for the type of part based on the determined trend for the type of part, wherein the corresponding simulated locations of the at least a subset of NDE data points of the NDE datasets are associated with the area of interest.

27. A method of monitoring manufacture of a particular type of part by a manufacturing process including a plurality of manufacturing steps in a system of the type that includes at least one processing unit and a memory, the method comprising:

receiving a plurality of non-destructive evaluation (NDE) datasets, wherein each NDE dataset is associated with a manufactured part of the particular type of part, wherein the NDE dataset for each manufactured part of the particular type of part includes inspection information and NDE data associated with an area of interest for the associated manufactured part, wherein each NDE dataset corresponds to data collected during a non-destructive evaluation process of the associated manufactured part;

aligning the plurality of NDE datasets to a simulated model associated with at least a portion of the particular type of part, including aligning NDE data associated with the area of interest from each of a plurality of manufactured parts to corresponding simulated locations on the simulated model that are associated with the area of interest wherein the step of aligning NDE datasets involves;

determination, from an analysis of the NDE datasets, of the non-destructive evaluation process by which the NDE data was collected;

selection of an alignment algorithm based on the determined non-destructive evaluation process;

using the selected algorithm, transformation of the NDE data points of an NDE dataset into a coordinate system of the simulated model to locate the NDE data points of the plurality of datasets on the simulated model; and

analyzing NDE data aligned to corresponding simulated locations associated with the area of interest for each of the plurality of manufactured parts to determine a spatially correlated statistic for a manufactured part, the spatially correlated statistic for a manufactured part being a value that is at least one of a measurement or calculated from a measurement that is associated with that part and is associated with the area of interest for the manufactured part.

28. The method of claim 27 further comprising:

aligning each spatially correlated statistic associated with the area of interest for each manufactured part to the corresponding simulated locations associated with the area of interest on the simulated model.

29. The method of claim 28 further comprising:

generating a display representation that visually represents each spatially correlated statistic associated with the area of interest for each manufactured part on the simulated model.

30. The method of claim 27 further comprising:

generating a control chart for the manufacturing process associated with the area of interest for the particular type of part including each spatially correlated statistic associated with the area of interest for each manufactured part.

31. The method of claim 27 further comprising:

analyzing each spatially correlated statistic associated with the area of interest for each manufactured part to determine a manufacturing trend associated with the area of interest for the manufacturing process based on at least a subset of the spatially correlated statistics.

32. The method of claim 31 further comprising:

analyzing the manufacturing trend and base line data associated with the area of interest of the simulated model to determine whether the manufacturing process is operating properly.

33. The method of claim 32 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 manufacturing trend and the area of interest.

34. The method of claim 33, wherein the NDE data associated with the area of interest on each manufactured part is a first type of NDE data, the method further comprising:

receiving manufacturing data associated with the area of interest of the simulated model and associated with the first type of NDE data, wherein the manufacturing data indicates one or more possible root cause problems associated with the area of interest and the first type of NDE data,

wherein determining the root cause problem associated with the manufacturing process includes analyzing the manufacturing trend and manufacturing data to determine the root cause problem of the one or more root cause problems associated with the manufacturing process.

35. The method of claim 34, wherein the manufacturing data indicates at least one manufacturing step associated with the area of interest and the first type of NDE data, wherein determining the root cause problem associated with the manufacturing process includes analyzing the manufacturing trend and manufacturing data to determine at least one manufacturing step associated with the root cause problem.

36. The method of claim 35, wherein the manufacturing data indicates at least one manufacturing apparatus utilized in the manufacturing process associated with the area of interest and the first type of NDE data, wherein determining the root cause problem associated with the manufacturing process includes analyzing the manufacturing trend and manufacturing data to determine at least one manufacturing apparatus associated with the root cause problem.

37. The method of claim 36, wherein the manufacturing data indicates at least one manufacturing tool utilized by a manufacturing apparatus associated with the area of interest and the first type of NDE data, wherein determining the root cause problem associated with the manufacturing process includes analyzing the manufacturing trend and manufacturing data to determine at least one manufacturing tool utilized by a manufacturing apparatus associated with the root cause problem.

38. The method of claim 33, wherein the manufacturing data indicates at least one manufacturing parameter associated with the area of interest and the first type of NDE data, wherein determining the root cause problem associated with the manufacturing process includes analyzing the manufacturing trend and manufacturing data to determine a manufacturing parameter associated with the root cause problem.

39. The method of claim 33, wherein the plurality of NDE datasets is a first plurality of NDE data sets, the method further comprising:

after implementing a process adjustment to the manufacturing process, receiving a second plurality of NDE datasets, wherein each NDE dataset of the second plurality of NDE datasets is associated with a manufactured part of the particular type of part manufactured after implementation of the process adjustment, wherein each NDE dataset of the second plurality includes NDE data associated with the area of interest for the associated manufactured part, wherein each NDE dataset of the second plurality of NDE datasets is collected during non-destructive evaluation of the associated manufactured part;

aligning the second plurality of NDE datasets to the simulated model associated with at least a portion of the particular type of part, including aligning the NDE data associated with the area of interest for each manufactured part associated with the second plurality of NDE datasets to corresponding simulated locations on the simulated model associated with the area of interest using the selected algorithm for transforming the NDE data points of the second plurality of NDE datasets to locate the NDE data points on the simulated model;

analyzing each aligned NDE dataset of the second plurality to determine a spatially correlated statistic associated with the area of interest for each manufactured part manufactured after implementation of the process adjustment; and

analyzing each spatially correlated statistic associated with the area of interest based on the second plurality of NDE datasets for each manufactured part to determine whether the implemented process adjustment corresponds to the root cause problem.

40. The method of claim 31, further comprising:

generating a display representation that visually represents the manufacturing trend on the simulated model.

41. A method of monitoring manufacture of a particular type of part by a manufacturing process including a plurality of manufacturing steps in a system of the type that includes at least one processing unit and a memory, the method comprising:

receiving a plurality of non-destructive evaluation (NDE) datasets, wherein each NDE dataset is associated with a manufactured part of the particular type of part, wherein the NDE dataset for each manufactured part of the particular type of part includes inspection information and NDE data associated with an area of interest for the associated manufactured part, wherein each NDE dataset corresponds to data collected during a non-destructive evaluation process of the associated manufactured part;

aligning the plurality of NDE datasets to a simulated model associated with at least a portion of the particular type of part, including aligning NDE data associated with the area of interest from each of a plurality of manufactured parts to corresponding simulated locations on the simulated model that are associated with the area of interest wherein the step of aligning NDE datasets involves;

determination, from an analysis of the NDE datasets, of the non-destructive evaluation process by which the NDE data was collected;

selection of an alignment algorithm based on the determined non-destructive evaluation process;

using the selected alignment algorithm, transformation of the NDE data points of an NDE dataset into a coordinate system of the simulated model to locate the NDE data points of the plurality of datasets on the simulated model;

determining a spatially correlated statistic for a manufactured part based on the aligned NDE data of NDE datasets associated with the area of interest, the spatially correlated statistic being a value that is at least one of a measurement or calculated from a measurement that is associated with that part;

analyzing each NDE dataset based on the spatially correlated statistic for the particular manufactured part to detect any manufactured part that includes a sub-rejectable physical characteristic associated with the area of interest based at least in part on the NDE data aligned to corresponding simulated locations associated with the area of interest on the simulated model.

42. The method of claim 41, wherein each NDE dataset includes inspection information for the associated manufactured part, wherein the inspection information includes an indication of a defect detected during non-destructive evaluation of the associated manufactured part, wherein aligning each NDE dataset includes aligning the inspection information included in each NDE dataset to corresponding simulated locations on the simulated model of the type of part associated with the area of interest, and wherein analyzing each NDE dataset to detect any manufactured part including a sub-rejectable physical characteristic associated with the area of interest includes analyzing the inspection information included in the aligned NDE data.

43. The method of claim 41, wherein analyzing each NDE dataset to detect any manufactured part including a sub-rejectable physical characteristic associated with the area of interest includes processing the aligned NDE data of each NDE dataset associated with the area of interest to automatically detect a defect on the associated manufactured part.

44. The method of claim 43, wherein processing the aligned NDE data of each NDE dataset associated with the area of interest on the simulated model to automatically detect a defect on the associated manufactured part includes the determination of the spatially correlated statistic for the area of interest of each manufactured part based on the aligned NDE data of each NDE dataset associated with the area of interest, wherein detecting a manufactured part including a sub-rejectable physical characteristic associated with the area of interest is based at least in part on the determined spatially correlated statistic for the area of interest for such manufactured part.

45. The method of 41, further comprising:

in response to detecting a sub-rejectable characteristic associated with the area of interest for a manufactured part, aligning an indication of the detected sub-rejectable physical characteristic to at least one corresponding simulated location on the simulated model.

46. The method of claim 45, further comprising:

generating a control chart for the manufacturing process based at least in part on the NDE data associated with the area of interest for each manufactured part and including an indication for each manufactured part including a detected sub-rejectable physical characteristic for the area of interest; and

analyzing the control chart to determine whether the control chart indicates a potential problem in the manufacturing process.

47. The method of claim 46, wherein analyzing the control chart to determine whether the control chart indicates a potential problem in the manufacturing process is based on a baseline value associated with the physical characteristic of the area of interest.

48. The method of claim 46, wherein analyzing the control chart includes determining a manufacturing trend based on the control chart, and wherein determining whether the control chart indicates a potential problem in the manufacturing process is based at least in part on the manufacturing trend.