Probabilistic modeling system for product design

• US 20060229753A1
• Filed: 04/08/2005
• Published: 10/12/2006
• Est. Priority Date: 04/08/2005
• Status: Abandoned Application

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Tse, Ben Kwok-kwong, Grichnik, Anthony J., Seskin, Michael
• Application Number:
  US11/101,498
• Publication Number:
  US 20060229753A1
• Time in Patent Office: Days
• US Class Current: 700/97
• 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 for designing a product includes obtaining data records relating to one or more input variables and one or more output parameters associated with the product. One or more input parameters may be selected from the one or more input variables, and 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 may be generated. The method further includes providing a set of constraints to the computational model representative of a compliance state for the product and using the computational model to generate statistical distributions for the one or more input parameters and the one or more output parameters, based on the set of constraints, that represent a design for the product.

Claims

1. A method for designing a product, comprising:

obtaining data records relating to one or more input variables and one or more output parameters associated with the product;

selecting one or more input parameters from the one or more input variables;

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;

providing a set of constraints to the computational model representative of a compliance state for the product; and

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

2. The method according to claim 1, wherein obtaining the data records includes:

generating a plurality of sets of random values for the one or more input variables representative of a desired product design space;

supplying each of the plurality of sets of random values to at least one simulation algorithm to generate values for the one or more output parameters.

3. The method of claim 2, wherein the at least one simulation algorithm is associated with a system for performing at least one of finite element analysis, computational fluid dynamics analysis, radio frequency simulation, electromagnetic field simulation, electrostatic discharge simulation, network propagation simulation, discrete event simulation, constraint-based network simulation.

4. The method of claim 1, further including using the computation model to generate nominal values for the one or more input parameters and the one or more output parameters.

5. The method of claim 4, further including modifying the design for the product by adjusting at least one of the statistical distributions and the nominal values for any of the one or more input parameters and the one or more output parameters.

6. The method of claim 1, wherein the selecting further includes:

pre-processing the data records; and

using a genetic algorithm to select the one or more input parameters from the one or more input variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

7. The method of claim 1, wherein generating the computational model includes:

creating a neural network computational model;

training the neural network computational model using the data records; and

validating the neural network computation model using the data records.

8. The method of claim 1, wherein using the computational model to generate statistical distributions further includes:

determining a candidate set of input parameters with a maximum zeta statistic using a genetic algorithm; and

determining the statistical distributions of the one or more input parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

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

provided that {overscore (x)}_i represents a mean of an ith input;

{overscore (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.

9. The method of claim 1, further including graphically displaying on a display:

the statistical distributions for the one or more input parameters and the one or more output parameters; and

nominal values for the one or more input parameters and the one or more output parameters.

10. The method of claim 9, further including graphically displaying on the display:

statistical information for the one or more input parameters and the one or more output parameters obtained based on the data records.

11. A computer readable medium including a set of instructions for enabling a processor to:

obtain data records relating to one or more input variables and one or more output parameters associated with a product to be designed;

select one or more input parameters from the one or more input variables;

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;

obtain a set of constraints representative of a compliance state for the product; and

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

12. The computer readable medium of claim 11, wherein the instructions for enabling the processor to generate a computational model further enable the processor to:

create a neural network computational model;

train the neural network computational model using the data records; and

validate the neural network computation model using the data records.

13. The computer readable medium of claim 11, wherein the instructions for enabling the processor to use the computational model further enable the processor to:

determine a candidate set of input parameters with a maximum zeta statistic using a genetic algorithm; and

determine the statistical distributions of the one or more input parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

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

provided that {overscore (x)}_i represents a mean of an ith input;

{overscore (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.

14. The computer readable medium of claim 11 further including instructions for enabling the processor to graphically display:

the statistical distributions for the one or more input parameters and the one or more output parameters; and

nominal values for the one or more input parameters and the one or more output parameters.

15. The computer readable medium of claim 14, further including instructions for enabling the processor to graphically display:

statistical information for the one or more input parameters and the one or more output parameters obtained based on the data records.

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

a database containing data records relating one or more input variables and one or more output parameters associated with a product to be designed; and

a processor configured to;

select one or more input parameters from the one or more input variables;

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;

obtain a set of constraints representative of a compliance state for the product; and

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

17. The computer-based product design system of claim 16, wherein to generate the computational model, the processor is further configured to:

create a neural network computational model;

train the neural network computational model using the data records; and

validate the neural network computation model using the data records.

18. The computer-based product design system of claim 16, wherein to use the computational model to generate statistical distributions, the processor is further configured to:

determine a candidate set of input parameters with a maximum zeta statistic using a genetic algorithm; and

determine the statistical distributions of the one or more input parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

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

provided that {overscore (x)}_i represents a mean of an ith input;

{overscore (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.

19. The computer-based product design system of claim 16, further including:

a display;

wherein the processor is configured to display the statistical distributions for the one or more input parameters and the one or more output parameters; and

nominal values for the one or more input parameters and the one or more output parameters.

20. The computer-based product design system of claim 19, wherein the processor is configured to display statistical information for the one or more input parameters and the one or more output parameters obtained based on the data records.