Most quality improvement or quality analysis frequently focused on the issue of the quantitative quality response. The issue of addressing a qualitative or a categorical quality response is seldom mentioned. Until now, only a few studies addressed the parameter optimization for achieving quality improvement for a categorical response. However, the weight effect for different categorical level of response cannot be included into their analysis and it will limit the rationality and feasibility for the real applications. The objective of this study is to propose a procedure about quality improvement based on artificial neural networks (ANNs) technique to deal with the parameter optimization of categorical response with different weight effect. A case study involving a taping process from a lead frame (L/F) manufacturer in Taiwan’s science-based park demonstrates the rationality and feasibility of the proposed approach.
Quality & Quantity – Springer Journals
Published: Dec 30, 2009
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