Multiresponse Process Optimization via Constrained Confidence Regions

Article

Del Castillo, Enrique   (1996, ASQC)   University of Texas at Arlington, Arlington, TX

Journal of Quality Technology    Vol. 28    No. 1
QICID: 11437    January 1996    pp. 61-70
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Article Abstract

This paper presents a new methodology for analyzing multiresponse experiments. The methodology consists of computing confidence regions for the stationary points of quadratic responses and confidence cones for the direction of maximum improvements for linear responses. The stationary points are constrained to lie within the region of experimentation. It is shown that the confidence regions depend on the value of the Lagrange multiplier of the region's constraint. Then, nonlinear optimization problems are set up and solved for obtaining experimental points that lie inside all the confidence regions, cones and constraints. Robust process design examples illustrate the methods proposed. The examples address the "target is best" and "larger the better" cases.

Keywords

Region of interest,Response surface methodology (RSM),Cross training


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