Manufacturing design and process analysis system

• US 20040167648A1
• Filed: 01/07/2004
• Published: 08/26/2004
• Est. Priority Date: 02/04/2002
• Status: Active Grant

• Current Assignee: Steve W. Tuszynski
• Original Assignee: Steve W. Tuszynski
• Inventors: Tuszynski, Steve W.
• Granted Patent: US 7,187,992 B2
• Time in Patent Office: Days
• US Class Current: 700/97
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/41865 characterised by job scheduling, process planning, material flow
• CPC Class Codes: G05B 2219/32058 Execute program as function of deviation from predicted state, result
• CPC Class Codes: G06F 2111/08 Probabilistic or stochastic CAD
• CPC Class Codes: G06F 2113/22 Moulding
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

Methods, apparatuses and systems that facilitate the design, production and/or measurement tasks associated with manufacturing and other processes. In one embodiment, the present invention relates to decision-making and logic structures, implemented in a computer software application, facilitating all phases of the design, development, tooling, pre-production, qualification, certification, and production process of any part or other article that is produced to specification. In one embodiment, the present invention provides knowledge of how the multiple characteristics of a given process output are related to each other, to specification limits and to pre-process inputs. This knowledge facilitates a reduction in measurement, analysis and reporting costs both prior to and during production. It also determines the changes needed to pre-process inputs in order to achieve production at design targets. It provides a prioritized order for relaxing design tolerances. It assesses the feasibility of producing parts that meet specification limits. It assesses the trade-off between performance and producibility and provides design targets that improve-producibility. It provides a determination of when process variability needs reduction. It facilitates material comparison and selection. It provides process engineers and operators with improved operating guidelines.

Claims

1. A method facilitating design, manufacturing, and other processes, the method comprising

receiving a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics;

selecting a predictor characteristic from the plurality of article characteristics;

determining the regression model between the predictor characteristic and a first remaining article characteristic in the plurality of article characteristics, wherein the regression model includes lower and upper prediction boundaries

receiving lower and upper specification limits for the predictor characteristic and the first remaining article characteristic;

locating, relative to the regression model between the predictor characteristic and the first remaining article characteristic, the compliance area bounded by the upper and lower specification limits associated with the first remaining article characteristic and the predictor characteristic;

locating the bounded regression area for the first remaining characteristic defined by the upper and lower prediction boundaries of the regression model and the upper and lower specification limits for the predictor characteristic; and

identifying the relationship between the bounded regression area and the compliance area.

2. The method of claim 1 wherein the identified relationship characterizes the structure of the geometric relationship between the bounded regression area and the compliance area.

3. The method of claim 1 wherein the identified relationship characterizes the relationship between the perimeter elements that define the bounded regression area and the perimeter elements that define the compliance area.

4. The method of claim 1 wherein the identified relationship is one from the group consisting of a defect potential relationship, a robust relationship and a constraining relationship.

5. The method of claim 1 wherein the identifying step comprises

determining whether the bounded regression area lies completely within the compliance area.

6. The method of claim 5 further comprising

if the bounded regression area lies completely within the compliance area, setting the minimum and maximum predictor characteristic values associated with the first remaining article characteristic to the lower and upper specification limits, respectively, of the predictor characteristic.

7. The method of claim 1 wherein the identifying step comprises

determining whether the bounded regression area extends above, below, or both above and below the compliance area over the specification limit range of the predictor characteristic;

and

if so, reporting a defect condition as to the first remaining article characteristic.

8. The method of claim 6 wherein the identifying step further comprises

determining whether the bounded regression area extends above, below, or both above and below the compliance area over the specification limit range of the predictor characteristic;

and

if so, reporting a defect condition as to the first remaining article characteristic.

9. The method of claim 1 wherein the identifying step comprises

determining whether any horizontal segment of the bounded regression area is contained completely within the compliance area and whether a second horizontal segment extends partially or completely outside of the compliance area;

and

if so, computing the minimum and maximum predictor characteristic values for the first remaining article characteristic.

10. The method of claim 9 wherein the computing step comprises

determining the slope and intercept of the regression model between the predictor characteristic and the first remaining article characteristic;

determining the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic;

determining the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic;

if the slope of the regression model is positive, then;

setting the maximum predictor characteristic value associated with the first remaining article characteristic to the lesser of the upper specification limit of the predictor characteristic or the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic; and

setting the minimum predictor characteristic value associated with the first remaining article characteristic to the greater of the lower specification limit of the predictor characteristic or the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic;

otherwise, if the slope of the regression model is negative, then

setting the maximum predictor characteristic value associated with the first remaining article characteristic to the lesser of the upper specification limit of the predictor characteristic or the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic; and

setting the minimum predictor characteristic value associated with the first remaining article characteristic to the greater of the lower specification limit of the predictor characteristic or the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic.

11. The method of claim 8 wherein the identifying step further comprises

determining whether any horizontal segment of the bounded regression area is contained completely within the compliance area and whether a second horizontal segment extends partially or completely outside of the compliance area;

and

if so, computing the minimum and maximum predictor characteristic values for the first remaining article characteristic.

12. The method of claim 11 further comprising

storing the resulting minimum and maximum predictor characteristic values in association with the corresponding remaining article characteristic in a data structure.

13. The method of claim 12 wherein the data structure is an array.

14. The method of claim 12 wherein the reporting a defect condition step comprises storing a defect identifier in association with the corresponding remaining article characteristic.

15. The method of claim 13 further comprising

displaying the resulting minimum and maximum predictor characteristic values and/or defect identifiers in association with the corresponding remaining article characteristic.

16. The method of claim 1 wherein the identifying step comprises

determining whether all vertical cross sections of the bounded regression area lie within the compliance area.

17. The method of claim 16 further comprising

if all vertical cross sections of the bounded regression area lie within the compliance area, setting the minimum and maximum predictor characteristic values associated with the first remaining article characteristic to the lower and upper specification limits, respectively, of the predictor characteristic.

18. The method of claim 1 wherein the comparing step comprises

determining whether all vertical cross-sections of the bounded regression area are fully or partially outside of the compliance area;

and

if so, reporting a defect condition as to the first remaining article characteristic.

19. The method of claim 1 wherein the comparing step comprises

determining whether at least one vertical cross section of the bounded regression area is completely within the compliance region and whether at least one vertical cross section is partially or completely outside the compliance area;

and

if so, computing the minimum and maximum predictor characteristic values for the first remaining article characteristic.

20. The method of claim 19 wherein the computing step comprises

determining the slope of the regression model between the predictor characteristic and the first remaining article characteristic;

determining the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic;

determining the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic;

if the slope of the regression model is positive, then;

setting the maximum predictor characteristic value associated with the first remaining article characteristic to the lesser of the upper specification limit of the predictor characteristic or the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic; and

setting the minimum predictor characteristic value associated with the first remaining article characteristic to the greater of the lower specification limit of the predictor characteristic or the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic;

otherwise, if the slope of the regression model is negative, then

setting the maximum predictor characteristic value associated with the first remaining article characteristic to the lesser of the upper specification limit of the predictor characteristic or the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic; and

setting the minimum predictor characteristic value associated with the first remaining article characteristic to the greater of the lower specification limit of the predictor characteristic or the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic.

21. The method of claim 1 wherein the identifying step comprises

determining whether the lower and upper boundaries of the bounded regression area are completely within the compliance region.

22. The method of claim 21 further comprising

if the lower and upper boundaries of the bounded regression area are completely within the compliance region, setting the minimum and maximum predictor characteristic values associated with the first remaining article characteristic to the lower and upper specification limits, respectively, of the predictor characteristic.

23. The method of claim 1 wherein the identifying step comprises

determining whether either of the lower and upper boundaries of the bounded regression area fail to intersect the compliance area;

and

if so, reporting a defect condition as to the first remaining article characteristic.

24. The method of claim 1 wherein the identifying step comprises

determining whether the upper boundary of the bounded regression area intersects the upper boundary of the compliance area and the lower boundary of the bounded regression area intersects the compliance area;

and, if so, computing the minimum and maximum predictor characteristic values for the first remaining article characteristic;

else, determining whether the lower boundary of the bounded regression area intersects the lower boundary of the compliance area and the upper boundary of the bounded regression area intersects the compliance area; and

, if so, computing the minimum and maximum predictor characteristic values for the first remaining article characteristic.

25. The method of claim 24 wherein the computing step comprises

determining the slope of the regression model between the predictor characteristic and the first remaining article characteristic;

determining the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic;

determining the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic;

if the slope of the regression model is positive, then;

setting the maximum predictor characteristic value associated with the first remaining article characteristic to the lesser of the upper specification limit of the predictor characteristic or the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic; and

setting the minimum predictor characteristic value associated with the first remaining article characteristic to the greater of the lower specification limit of the predictor characteristic or the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic;

otherwise, if the slope of the regression model is negative, then

setting the maximum predictor characteristic value associated with the first remaining article characteristic to the lesser of the upper specification limit of the predictor characteristic or the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic; and

setting the minimum predictor characteristic value associated with the first remaining article characteristic to the greater of the lower specification limit of the predictor characteristic or the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic.

26. The method of claim 1 wherein the identifying step comprises

computing the upper and lower prediction boundary values for the first remaining article characteristic at the upper and lower specification limits of the predictor characteristic;

determining whether the upper prediction boundary values in the computing step are both less than the upper specification limit for the first remaining article characteristic, and whether the lower prediction boundary values in the computing step are both greater than the lower specification limit for the first remaining article characteristic.

27. The method of claim 26 further comprising

if the upper prediction boundary values in the computing step are both less than the upper specification limit for the first remaining article characteristic, and the lower prediction boundary values in the computing step are both greater than the lower specification limit for the first remaining article characteristic, then

setting the minimum and maximum predictor characteristic values associated with the first remaining article characteristic to the lower and upper specification limits, respectively, of the predictor characteristic.

28. The method of claim 1 wherein the identifying step comprises

computing the upper and lower prediction boundary values for the first remaining article characteristic at the upper and lower specification limits of the predictor characteristic;

if the upper prediction boundary values in the computing step are both greater than the upper specification limit for the first remaining article characteristic, then reporting a defect condition as to the first remaining article characteristic; and

if the lower prediction boundary values in the computing step are both less than the lower specification limit for the first remaining article characteristic, then reporting a defect condition as to the first remaining article characteristic.

29. The method of claim 1 wherein the identifying step comprises

computing the upper and lower prediction boundary values for the first remaining article characteristic at the upper and lower specification limits of the predictor characteristic;

determining whether one of the upper prediction boundary values in the computing step is less than, and the other of the upper prediction boundary values is greater than, the upper specification limit of the first remaining article characteristic, and both of the lower prediction boundary values are greater than the upper specification limit for the first remaining article characteristic; and

if so, computing the minimum and maximum predictor characteristic values for the first remaining article characteristic;

else, determining whether one of the lower prediction boundary values in the computing step is less than, and the other of the lower prediction boundary values is greater than, the lower specification limit for the first remaining article characteristic, and whether the upper prediction boundary values in the computing step, are both less than the upper specification limit of the first remaining article characteristic; and

, if so, computing the minimum and maximum predictor characteristic values for the first remaining article characteristic;

else, determining whether one of the upper prediction boundary values in the computing step is less than, and the other of the upper prediction boundary values is greater than, the upper specification limit of the first remaining article characteristic, and whether one of the lower prediction boundary values in the computing step is less than, and the other of the lower prediction boundary values is greater than, the lower specification limit for the first remaining article characteristic; and

, if so, computing the minimum and maximum predictor characteristic values for the first remaining article characteristic.

30. The method of claim 29 wherein the computing step comprises

determining the slope of the regression model between the predictor characteristic and the first remaining article characteristic;

determining the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic;

determining the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic;

if the slope of the regression model is positive, then;

setting the maximum predictor characteristic value associated with the first remaining article characteristic to the lesser of the upper specification limit of the predictor characteristic or the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic; and

setting the minimum predictor characteristic value associated with the first remaining article characteristic to the greater of the lower specification limit of the predictor characteristic or the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic;

otherwise, if the slope of the regression model is negative, then

setting the maximum predictor characteristic value associated with the first remaining article characteristic to the lesser of the upper specification limit of the predictor characteristic or the value of the predictor characteristic at which the lower prediction boundary corresponding to the regression model intersects the lower specification limit for the first remaining article characteristic; and

setting the minimum predictor characteristic value associated with the first remaining article characteristic to the greater of the lower specification limit of the predictor characteristic or the value of the predictor characteristic at which the upper prediction boundary corresponding to the regression model intersects the upper specification limit for the first remaining article characteristic.

31. The method of claim 1 further comprising

repeating the determining the regression model, receiving the specification limits, locating the compliance area, locating the bounded regression area, and identifying the relationship steps for all desired remaining article characteristics.

32. The method of claim 31 further comprising displaying the identified relationships.

33. The method of claim 31 further comprising storing the identified relationships in a data structure.

34. A method facilitating a determination of the magnitude and direction by which a pre-process characteristic would have to be adjusted to achieve a given design target, comprising

receiving a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics;

selecting a predictor characteristic from the plurality of article characteristics;

determining the regression model between the predictor characteristic and a first remaining article characteristic in the plurality of article characteristics,

receiving the target values for the predictor characteristic and the first remaining article characteristic;

computing, based on the regression model, the value of the first remaining article characteristic at the target value of the predictor characteristic;

determining the magnitude and direction of the offset for the first remaining article characteristic by computing the difference between the computed value of the first remaining article characteristic and the target value of the first remaining article characteristic;

storing the magnitude and direction of the offset in a data structure in association with an identifier for the first remaining article characteristic; and

repeating the computing, determining and storing steps for all desired remaining characteristics.

35. The method of claim 34 further comprising displaying the resulting magnitudes and directions of the offsets in association with the corresponding remaining article characteristics.

36. A method facilitating analysis of the achievable gains in operating range associated with relaxing design tolerances corresponding to at least one article characteristic, comprising

receiving a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics;

selecting a predictor characteristic from the plurality of article characteristics;

determining the regression model between the predictor characteristic and a first remaining article characteristic in the plurality of article characteristics, wherein the regression model includes lower and upper prediction boundaries;

receiving lower and upper specification limits for the predictor characteristic and the first remaining article characteristic;

computing, based on the regression model, the minimum and maximum predictor characteristic values at which the first remaining article characteristic remains within the lower and upper specification limits of the first remaining article characteristic;

repeating the determining, receiving, and computing steps for all desired remaining article characteristics;

creating a most constraining minimum predictor characteristic list by ranking the remaining article characteristics by the respective minimum predictor characteristic values associated therewith; and

starting with the remaining article characteristic associated with the greatest minimum predictor characteristic value;

computing the individual gain in operating range achieved by relaxing the applicable specification limit of the remaining article characteristic to the value corresponding to the minimum predictor characteristic value associated with the next remaining article characteristic in the ranked list;

computing the cumulative gain associated with relaxing the applicable specification limit of the corresponding article characteristic; and

repeating the first and second computing steps for all desired remaining article characteristics.

37. The method of claim 36 further comprising

creating a most constraining maximum predictor characteristic list by ranking the remaining article characteristics by the respective maximum predictor characteristic values associated therewith;

and

starting with the remaining article characteristic associated with the lowest maximum predictor characteristic value;

computing the individual gain in operating range achieved by relaxing the applicable specification limit of the remaining article characteristic to the value corresponding to the maximum predictor characteristic value associated with the next remaining article characteristic in the ranked list,

computing the cumulative gain associated with relaxing the applicable specification limit of the corresponding article characteristic; and

,

repeating the first and second computing steps for all desired remaining article characteristics.

38. The method of claim 37 further comprising

receiving a selection of at least one remaining article characteristic from either or both of the most constraining minimum or maximum predictor characteristic list;

if the selection includes a remaining article characteristic from the most constraining minimum predictor characteristic list, then;

setting the minimum predictor characteristic value to the minimum predictor characteristic value corresponding to the next article characteristic in the most constraining minimum predictor characteristic list, and

for all article characteristics in the most constraining minimum predictor characteristic list up to the selected article characteristic, computing the new lower or upper specification limit for the article characteristic; and

if the selection includes a remaining article characteristic from the most constraining maximum predictor characteristic list, then;

setting the maximum predictor characteristic value to the maximum predictor characteristic value corresponding to the next article characteristic in the most constraining maximum predictor characteristic list, and

for all article characteristics in the most constraining maximum predictor characteristic list up to the selected article characteristic, determining the new lower or upper specification limit for the article characteristic.