System level analysis and control of manufacturing process variation
Abstract
A computer-implemented method is implemented for determining the variability of a manufacturing system having a plurality of subsystems. Each subsystem of the plurality of subsystems is characterized by signal factors, noise factors, control factors, and an output response, all having mean and variance values. Response models are then fitted to each subsystem to determine unknown coefficients for use in the response models that characterize the relationship between the signal factors, noise factors, control factors, and the corresponding output response having mean and variance values that are related to the signal factors, noise factors, and control factors. The response models for each subsystem are coupled to model the output of the manufacturing system as a whole. The coefficients of the fitted response models are randomly varied to propagate variances through the plurality of subsystems and values of signal factors and control factors are found to optimize the output of the manufacturing system to meet a specified criterion.
- Inventors:
- Issue Date:
- Research Org.:
- Univ. of California, Oakland, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1175373
- Patent Number(s):
- 6901308
- Application Number:
- 10/191,202
- Assignee:
- The Regents of the University of California (Los Alamos, NM)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06Q - DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES
- DOE Contract Number:
- W-7405-ENG-36
- Resource Type:
- Patent
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Hamada, Michael S., Martz, Harry F., Eleswarpu, Jay K., and Preissler, Michael J. System level analysis and control of manufacturing process variation. United States: N. p., 2005.
Web.
Hamada, Michael S., Martz, Harry F., Eleswarpu, Jay K., & Preissler, Michael J. System level analysis and control of manufacturing process variation. United States.
Hamada, Michael S., Martz, Harry F., Eleswarpu, Jay K., and Preissler, Michael J. Tue .
"System level analysis and control of manufacturing process variation". United States. https://www.osti.gov/servlets/purl/1175373.
@article{osti_1175373,
title = {System level analysis and control of manufacturing process variation},
author = {Hamada, Michael S. and Martz, Harry F. and Eleswarpu, Jay K. and Preissler, Michael J.},
abstractNote = {A computer-implemented method is implemented for determining the variability of a manufacturing system having a plurality of subsystems. Each subsystem of the plurality of subsystems is characterized by signal factors, noise factors, control factors, and an output response, all having mean and variance values. Response models are then fitted to each subsystem to determine unknown coefficients for use in the response models that characterize the relationship between the signal factors, noise factors, control factors, and the corresponding output response having mean and variance values that are related to the signal factors, noise factors, and control factors. The response models for each subsystem are coupled to model the output of the manufacturing system as a whole. The coefficients of the fitted response models are randomly varied to propagate variances through the plurality of subsystems and values of signal factors and control factors are found to optimize the output of the manufacturing system to meet a specified criterion.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 31 00:00:00 EDT 2005},
month = {Tue May 31 00:00:00 EDT 2005}
}
Works referenced in this record:
Markov Chain Monte Carlo in Practice.
journal, December 1997
- Kass, Robert E.; Gilks, W. R.; Richardson, S.
- Journal of the American Statistical Association, Vol. 92, Issue 440