Performance measures independent of adjustment
Technometrics
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Design and Analysis of Experiments
Design and Analysis of Experiments
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Improving quality is essential for manufacturing organizations competing in the global marketplace. Generally, two forms of quality response are available: a quantitative response and a qualitative response. Most studies primarily focus on quantitative quality improvement. Quantitative quality improvement has rarely been reported. he qualitative response is generally represented in the percentage form, or it is classified into several categories. Employing the ordered categorical descriptions (or subjective estimations) to formulate the performance of the qualitative characteristic is also a meaningful approach. Subjective estimation may provide more information for analyzing the problem. However, subjective estimation cannot be directly defined using the conventional binary set for the uncertainties involved. Experimental design techniques and the Taguchi method are two primary approaches used to improve quality. However, these two approaches are inappropriate when the quality response must be subjectively estimated. Hence, a novel approach based on a fuzzy set is proposed in this study to deal with the quality improvement problem of qualitative quality response. he fuzzy set is a well-known approach for dealing with the uncertainties of ordered categorical response. An illustrative example, based on the uniformity of an ion implantation process in Taiwan's semiconductor industry, demonstrates the effectiveness of the proposed approach.