Fuzzy neural network approach to optimizing process performance by using multiple responses

This research proposes a method for optimizing process performance; the method involves the use of multiple quality characteristics, fuzzy logic, and radial basis function neural networks (RBFNNs). In the method, each quality characteristic is transformed into a signal-to-noise ratio, and all the ratios are then provided as inputs to a fuzzy model to obtain a single comprehensive output measure (COM). The RBFNNs are used to generate a full factorial design. Finally, the average COM values are calculated for different factor levels, where for each factor, the level that maximizes the COM value is identified as the optimal level. Three case studies are presented for illustrating the method, and for all of them, the proposed method affords the largest total anticipated improvements in multiple quality responses compared with previously used methods, including the fuzzy, grey-Taguchi, Taguchi, and principal component analysis methods. The main advantages of the proposed method are its effectiveness in obtaining global optimal factor levels, its applicability and the requirement of less computational effort, and its efficiency in improving performance. In conclusion, the proposed method may enable practitioners optimize process performance by using multiple quality characteristics.