Manufacturing design and process analysis system

• US 7,321,848 B2
• Filed: 07/13/2005
• Issued: 01/22/2008
• Est. Priority Date: 02/04/2002
• Status: Expired due to Fees

• Current Assignee: Steve W. Tuszynski
• Original Assignee: Steve W. Tuszynski
• Inventors: Tuszynski, Steve W.
• Primary Examiner(s): Paladini; Albert W.
• Application Number:
  US11/180,244
• Publication Number:
  US 20050246149A1
• Time in Patent Office: 923 Days
• Field of Search: None
• US Class Current: 703/2
• 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 provides an understanding of how the multiple characteristics of a given process output are related to each other and to process inputs. This knowledge facilitates a reduction in measurement costs. It also facilitates an understanding of the sometimes complex interrelationships between design targets, design tolerances, process inputs, process control variables, average process output and variation in the process output. As discussed in more detail below, embodiments of the present invention facilitate 1.) determination of design target values, 2.) determination of design specification limits, 3.) design of process inputs, 4.) determination of process control variable settings, and/or 5.) reduction of measurement costs.

Claims

1. An apparatus facilitating design, manufacturing, and other processes comprising:

a processor;

a memory;

a software application, physically stored in the memory, comprising instructions operable to cause the processor and the apparatus to;

receive a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics resulting from a process;

select a predictor characteristic from the plurality of article characteristics; and

,

determine the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics in the plurality of article characteristics.

2. The apparatus of claim 1 wherein the software application further comprises instructions operable to cause the processor and the apparatus to

receive a target value for the predictor characteristic and a target value for at least one remaining article characteristic;

and

determine the intersection of the target value for the predictor characteristic and the target value of a first remaining article characteristic relative to the regression model between the predictor characteristic and the first remaining article characteristic.

3. The apparatus of claim 1 wherein the software application further comprises instructions operable to cause the processor and the apparatus to

determine the respective upper and lower prediction intervals associated with the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics.

4. The apparatus of claim 3 wherein the software application further comprises instructions operable to cause the processor and the apparatus to

receive lower and upper specification limits for said at least one of the remaining article characteristics;

locate the lower and upper specification limits associated with said at least one of the remaining article characteristics;

receive lower and upper specification limits for the predictor characteristic;

locate the upper specification limit associated with the predictor characteristic; and

determine a maximum article characteristic value for the predictor characteristic by selecting the lesser of (1) the upper specification limit for the predictor characteristic and (2) the value of the predictor characteristic at which the upper prediction interval intersects the upper specification limit for said at least one remaining article characteristic.

5. The apparatus of claim 4 wherein the software application further comprises instructions operable to cause the processor and the apparatus to

repeat determining a maximum article characteristic value for a desired number of remaining article characteristics in the plurality of article characteristics;

and

determine the most constraining maximum article characteristic value for the predictor characteristic by selecting the lowest maximum article characteristic value.

6. The apparatus of claim 1 wherein the software application further comprises instructions operable to cause the processor and the apparatus to

select the predictor characteristic based at least in part on an assessment of the capability of each article characteristic to be predictive of all or a subset of the article characteristics in the plurality of article characteristics.

7. The apparatus of claim 6 wherein the software application further comprises instructions operable to cause the processor and the apparatus to

calculate the correlation coefficients between all or a subset of the article characteristics;

determine, based on the calculated correlation coefficients, a value indicating the predictive capability of a first article characteristic relative to all other article characteristics;

repeat determining a value indicating the predictive capability for said all or a subset of the article characteristics; and

select a predictor characteristic based at least in part on the values indicating the predictive capabilities of the article characteristics.

8. A computer program product, physically stored on a machine-readable medium, for facilitating design, manufacturing, and other processes, comprising instructions operable to cause a programmable processor to:

receive a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics resulting from a process;

select a predictor characteristic from the plurality of article characteristics; and

,

determine the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics in the plurality of article characteristics.

9. The computer program product of claim 8 wherein the instructions are further operable to cause the programmable processor to:

select the predictor characteristic based at least in part on an assessment of the capability of each article characteristic to be predictive of all or a subset of the article characteristics in the plurality of article characteristics.

10. The computer program product of claim 9 wherein the instructions are further operable to cause the programmable processor to:

calculate the correlation coefficients between all or a subset of the article characteristics;

determine, based on the calculated correlation coefficients, a value indicating the predictive capability of a first article characteristic relative to all other article characteristics;

repeat determining a value indicating the predictive capability for said all or a subset of the article characteristics; and

select a predictor characteristic based at least in part on the values indicating the predictive capabilities of the article characteristics.

11. The computer program product of claim 10 wherein the predictor characteristic is selected as the article characteristic associated with the value indicating the highest predictive capability.

12. A computer-implemented method facilitating design and manufacturing 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 resulting from a process;

and

computing, for each article characteristic, a predictor value indicative of the capability of an article characteristic to be predictive of all or a subset of the article characteristics in the plurality of article characteristics.

13. The computer-implemented method of claim 12 wherein the computing step comprises

calculating the correlation coefficients between all or a subset of the article characteristics;

determining, based on the calculated correlation coefficients, a value indicating the predictive capability of a first article characteristic relative to all other article characteristics; and

repeating the determining step for said all or a subset of the article characteristics.

14. The computer-implemented method of claim 12 further comprising

ranking the article characteristics based on the corresponding predictor values.

15. The computer-implemented method of claim 12 further comprising

displaying the predictor values to a user.

16. The computer-implemented method of claim 12 further comprising

receiving a selection of a predictor characteristic from the plurality of article characteristics;

and,

determining the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics in the plurality of article characteristics.

17. The computer-implemented method of claim 16 further comprising

graphically displaying the regression model(s).

18. The computer-implemented method of claim 16 further comprising

receiving a target value for the predictor characteristic and a target value for at least one remaining article characteristic;

and

determining the intersection of the target value for the predictor characteristic and the target value of a first remaining article characteristic relative to the regression model between the predictor characteristic and the first remaining article characteristic.

19. The computer-implemented method of claim 16 further comprising

determining the respective upper and lower prediction intervals associated with the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics.

20. The computer-implemented method of claim 16 further comprising

determining the respective upper and lower prediction intervals associated with the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics.