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The development of multi‐response experimental designs for process parameter optimization

The development of multi‐response experimental designs for process parameter optimization Purpose – The selection of the optimal process target for a manufacturing process is critically important as it directly affects the defect rate, rejection and rework costs, and the loss to customers. A recent review of process target literature suggests that future work should incorporate models using multiple quality characteristics. Thus, the purpose of this paper is to create a more flexible and realistic approach to solving the multi‐response process target problem. Design/methodology/approach – A design of experiments methodology is proposed to provide estimates of process parameters and a nonlinear constrained optimization scheme is employed to identify optimal settings. Findings – The approximation of cost savings undoubtedly has a higher degree of accuracy than in the case where the engineer assumes values for the process parameters. Furthermore, greater flexibility is obtained in finding solutions that support both the manufacturer and the customer. Research limitations/implications – This methodology relies on controlled experimentation and the replication of observations made on multiple nominal‐the‐best quality characteristics. Future research may include examining the effects of using smaller‐the‐better or larger‐the‐better type characteristics. Originality/value – Unlike traditional attempts at solving the process target problem where the process mean, variance, and covariance between characteristics are assumed known in advance, this paper uses an approach that removes these assumptions, thereby providing a more practical depiction of the overall system. Furthermore, this methodology broadens the scope of process target problem research by seeking the simultaneous optimization of process parameters and considering a loss in quality attributed to deviation from a target value. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Quality & Reliability Management Emerald Publishing

The development of multi‐response experimental designs for process parameter optimization

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References (28)

Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
0265-671X
DOI
10.1108/02656711111141193
Publisher site
See Article on Publisher Site

Abstract

Purpose – The selection of the optimal process target for a manufacturing process is critically important as it directly affects the defect rate, rejection and rework costs, and the loss to customers. A recent review of process target literature suggests that future work should incorporate models using multiple quality characteristics. Thus, the purpose of this paper is to create a more flexible and realistic approach to solving the multi‐response process target problem. Design/methodology/approach – A design of experiments methodology is proposed to provide estimates of process parameters and a nonlinear constrained optimization scheme is employed to identify optimal settings. Findings – The approximation of cost savings undoubtedly has a higher degree of accuracy than in the case where the engineer assumes values for the process parameters. Furthermore, greater flexibility is obtained in finding solutions that support both the manufacturer and the customer. Research limitations/implications – This methodology relies on controlled experimentation and the replication of observations made on multiple nominal‐the‐best quality characteristics. Future research may include examining the effects of using smaller‐the‐better or larger‐the‐better type characteristics. Originality/value – Unlike traditional attempts at solving the process target problem where the process mean, variance, and covariance between characteristics are assumed known in advance, this paper uses an approach that removes these assumptions, thereby providing a more practical depiction of the overall system. Furthermore, this methodology broadens the scope of process target problem research by seeking the simultaneous optimization of process parameters and considering a loss in quality attributed to deviation from a target value.

Journal

International Journal of Quality & Reliability ManagementEmerald Publishing

Published: Jun 28, 2011

Keywords: Quality; Surface fitting; Optimal process target; Multivariate normal distribution

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