JSM 2004 - Toronto

Abstract #300164

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Activity Number: 251
Type: Invited
Date/Time: Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor: Technometrics
Abstract - #300164
Title: Failure Amplification Method: An Information Maximization Approach to Categorical Response Optimization
Author(s): V. Roshan Joseph*+ and C.F. Jeff Wu
Companies: Georgia Institute of Technology and Georgia Institute of Technology
Address: School of Industrial and Systems Engineering, Atlanta, GA, 30332-0205,
Keywords: accelerated life testing ; operating window method ; process capability ; quality engineering ; robust parameter design
Abstract:

Categorical data arise quite often in industrial experiments because of an expensive or inadequate measurement system for obtaining continuous data. When the failure probability (defect rate) is small, experiments with categorical data provide little information regarding the effect of factors of interest and are generally not useful for product or process optimization. An engineering-statistical framework for categorical response optimization is proposed that overcomes the inherent problems associated with categorical data. The basic idea is to select a factor that has a known effect on the response and use it to amplify the failure probability so as to maximize the information in the experiment. New modeling and optimization methods are developed and illustrated with examples.

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