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A nonlinear programming solution to robust multi-response quality problem
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DOI：10.1016/j.amc.2007.06.023.png)
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Abstract
En 中文
Quite often, engineers obtain measurements associated with several response variables. Both the design and analysis of multi-response experiments with a focus on quality control and improvement have received little attention although they are sorely needed. In a multi-response case the optimization problem is more complex than in the single-response situation. In this paper we present a method to optimize multiple quality characteristics based on the mean square error (MSE) criterion when the data are collected from a combined array. The proposed method will generate more alternative solutions. The string of solutions and the trade-offs aid in determining the underlying mechanism of a system or process. The procedure is illustrated with an example, using the generalized reduced gradient (GRG) algorithm for nonlinear programming. (C) 2007 Elsevier Inc. All rights reserved.
Keywords:
multi-response process optimization
mean square error
robust parameter design
response surface methodology
nonlinear programming
Journal
IF:
3.4
Papers:
2.3W
Citations:
3.3W
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