Process improvement in the presence of qualitative response by combining fuzzy sets and neural networks

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Abstract
Improving quality is essential work for manufacturing organizations competing in the global marketplace. Parameter optimization is an efficient technique to achieve process improvement. Most parameter optimization studies primarily focus on the quantitative quality response. Only a few studies address parameter optimization of the qualitative (or linguistic) response. The fuzzy set is a well-known approach for dealing with the uncertainties of the linguistic description. Additionally, Taguchi’s quadratic quality loss function is an efficient technique to evaluate quality of a product or an operational process. A concept of loss function, fuzzy-quality-loss-function (FQLF), developed in the proposed approach can be viewed as a feasible evaluation index for including the subjective estimation from engineers. Artificial neural networks (ANN) have been successfully employed to model the complexity structure of a system including linear or non-linear relationships. A novel approach combining fuzzy sets and ANN is proposed in this study to deal with the quality improvement problem of the quality response with a linguistic category. By employing the proposed approach, the information of subjective estimation can be considered, and the optimum continuous settings of control factors can be determined. An illustrative case involving a downset process from a lead frame manufacturer in Taiwan’s Science-Based Park demonstrates the effectiveness of the proposed approach.