Abstract
Taguchi parameter design is used extensively in industry to determine the optimal set of process parameters necessary to produce a product that meets or exceeds customer expectations of performance while minimizing performance variation. The majority of research in Taguchi parameter design has concentrated on approaches to optimize process parameters based on experimental observation of a single quality characteristic. This paper develops a statistical method, the DMT method, to evaluate and optimize multiple quality characteristic problems. The method incorporates desirability functions, a performance statistic based on the mean squared error, and data-driven transformations to provide a systematic approach that is adjustable to a variety of situations and easy for nonexperts to apply. This paper presents the DMT method in a step-by-step format and applies the method to two examples to illustrate its applicability to a variety of parameter design problems.
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