Product design optimization method and system

• US 20090037153A1
• Filed: 07/30/2007
• Published: 02/05/2009
• Est. Priority Date: 07/30/2007
• Status: Active Grant

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Grichnik, Anthony J., Seskin, Michael, Roozenboom, Stephan D., Patel, Amar, Brazzale-Anderson, Andrea, Milam, David M.
• Granted Patent: US 7,788,070 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 2111/08 Probabilistic or stochastic CAD
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

A method is provided for developing a product. The method may include obtaining data records from a plurality of stages of development of the product; identifying respective objectives of the plurality of stages and corresponding objective factors of the objectives; and determining common variables associated with the objectives based on the objective factors. The method may also include selecting one or more input parameters based on the common variables and one or more output parameters based on the objectives; and updating the data records to generate desired data records indicative characteristics of the one or more input parameters and the one or more output parameters. Further, the method may include generating a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records; and providing a set of constraints to the computational model representative of a design of the product.

Claims

1. A method for developing a product, comprising:

obtaining data records from a plurality of stages of development of the product;

identifying respective objectives of the plurality of stages and corresponding objective factors of the objectives;

determining common variables associated with the objectives based on the objective factors;

selecting one or more input parameters based on the common variables and one or more output parameters based on the objectives;

updating the data records to generate desired data records indicative of characteristics of the one or more input parameters and the one or more output parameters;

generating a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records; and

providing a set of constraints to the computational model representative of a design of the product.

2. The method according to claim 1, further including:

using the computational model and the provided set of constraints to generate statistical distributions for the one or more input parameters and the one or more output parameters, wherein the one or more input parameters and the one or more output parameters represent a design for the product.

3. The method according to claim 1, wherein the plurality stages include a strategy stage, a concept design stage, a development stage, and a production stage.

4. The method according to claim 1, wherein the objectives includes a quality objective, a performance objective, and a cost objective.

5. The method according to claim 1, wherein updating the data records further includes:

obtaining values of the common variables and the objectives of each of the plurality of stages;

determining unified values of the common variables and the objectives based on the values of the common variables and the objectives of each of the plurality of stages; and

generating the desired data records based on the unified values of the common variables and the objectives.

6. The method according to claim 5, wherein determining further includes:

determining the unified values based on multi-dimension distances, wherein each dimension represents one of the plurality of stages.

7. The method according to claim 1, wherein updating the data records further includes:

obtaining values of a plurality of objective parameters associated with an objective of one of the plurality of stages;

normalizing the values of the plurality of objective parameters;

determining a combined value of the objective representing the values of the plurality of objective parameters; and

generating the desired data records based on the combined value of the objective.

8. The method according to claim 7, wherein the normalizing further includes:

normalizing each of the values based on a percentage of the each of the values over a corresponding range of the each of the values.

9. The method according to claim 7, wherein determining further includes:

determining the combined value of the objective based on a multi-dimension distance, wherein each dimension represents one of the plurality of objective parameters.

10. The method of claim 2, wherein using the computational model further includes:

obtaining respective ranges of the input parameters;

creating a plurality of model data records based on the respective ranges of the input parameters;

determining a candidate set of values of input parameters using the plurality of model data records;

determining the statistical distributions of the one or more output parameters based on the candidate set; and

searching the input parameters the computational model generates a maximum zeta statistic,

wherein the zeta statistic ζ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input of the computational model.

11. The method of claim 10, wherein the searching is based on a genetic algorithm.

12. The method of claim 10, wherein the searching includes calculating a capability statistic C_pk by

$C_{\mathrm{pk}}=\min $

{ USL - x _ j 3 

σ

j , x _ j - LSL 3 

σ

j } ,

provided that USL and LSL represent the upper and lower specification limits of the jth output, respectively, and x_j,σ

_j represent the mean and standard deviation of the jth output, respectively.

13. A computer-based product design system, comprising:

a database containing data records relating to a plurality of stages of development of the product; and

a processor configured to;

obtain the data records associated with the plurality of stages;

identify respective objectives of the plurality of stages and corresponding objective factors of the objectives;

determine common variables associated with the objectives based on the objective factors;

select one or more input parameters based on the common variables and one or more output parameters based on the objectives;

update the data records to generate desired data records indicative of characteristics of the one or more input parameters and the one or more output parameters;

generate a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records; and

provide a set of constraints to the computational model representative of a design of the product.

14. The computer system according to claim 13, wherein the processor is further configured to:

use the computational model and the provided set of constraints to generate statistical distributions for the one or more input parameters and the one or more output parameters, wherein the one or more input parameters and the one or more output parameters represent a design for the product.

15. The computer system according to claim 13, wherein the plurality stages include a strategy stage, a concept design stage, a development stage, and a production stage.

16. The computer system according to claim 13, wherein the objectives includes a quality objective, a performance objective, and a cost objective.

17. The computer system according to claim 13, wherein, to update the data records, the processor is further configured to:

obtain values of the common variables and the objectives of each of the plurality of stages;

determine unified values of the common variables and the objectives based on the values of the common variables and the objectives of each of the plurality of stages; and

generate the desired data records based on the unified values of the common variables and the objectives.

18. The computer system according to claim 17, wherein, to determine the unified values, the processor is further configured to:

determine the unified values based on multi-dimension distances, wherein each dimension represents one of the plurality of stages.

19. The computer system according to claim 13, wherein, to update the data records, the processor is further configured to:

obtain values of a plurality of objective parameters associated with an objective of one of the plurality of stages;

normalize the values of the plurality of objective parameters;

determine a combined value of the objective representing the values of the plurality of objective parameters; and

generate the desired data records based on the combined value of the objective.

20. The computer system according to claim 19, wherein, to normalize the values, the processor is further configured to:

normalize each of the values based on a percentage of the each of the values over a corresponding range of the each of the values.

21. The computer system according to claim 20, wherein, to determine the combined value, the processor is further configured to:

determine the combined value of the objective based on a multi-dimension distance, wherein each dimension represents one of the plurality of objective parameters.

22. The computer system of claim 14, wherein, to use the computational model, the processor is further configured to:

obtain respective ranges of the input parameters;

create a plurality of model data records based on the respective ranges of the input parameters;

determining a candidate set of values of input parameters using the plurality of model data records;

determining the statistical distributions of the one or more output parameters based on the candidate set; and

searching the input parameters the computational model generates a maximum zeta statistic,

wherein the zeta statistic ζ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input of the computational model.