Multivariate yield maximization using CAD/CAE models. Efficient approximations based on mean and variance

Russell R. Barton, Kwok Leung Tsui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

A product contributes to yield if all of its performance functions fall within their upper and/or lower limits. For example, a piston connecting rod may be required to provide rigidity along several axes. The actual connecting rod deflection will vary, depending on variations in the materials and forging conditions, but the deflection must remain less than an upper limit. Designing for maximum yield for multivariate performance limits is a difficult task. Direct optimization may require excessive computing resources. We discuss two efficient methods for yield improvement: 'performance centering' and a method based on Taguchi's 'parameter design' philosophy. Both are shown to be motivated by the Chebychev inequality. It is important to remember that these are approximate methods. An example shows that they may produce sub-optimal yield, even when the random components of the performance functions are independent and identically distributed.

Original languageEnglish (US)
Title of host publicationDTM '91
PublisherPubl by ASME
Pages31-35
Number of pages5
ISBN (Print)0791807479
StatePublished - Dec 1 1991
Event3rd International Conference on Design Theory and Methodology presented at the 1991 ASME Design Technical Conferences - Miami, FL, USA
Duration: Sep 22 1991Sep 25 1991

Publication series

NameAmerican Society of Mechanical Engineers, Design Engineering Division (Publication) DE
Volume31

Other

Other3rd International Conference on Design Theory and Methodology presented at the 1991 ASME Design Technical Conferences
CityMiami, FL, USA
Period9/22/919/25/91

All Science Journal Classification (ASJC) codes

  • General Engineering

Fingerprint

Dive into the research topics of 'Multivariate yield maximization using CAD/CAE models. Efficient approximations based on mean and variance'. Together they form a unique fingerprint.

Cite this