Abstract
This study is concerned with a four steps approach to optimize the parameter combination of the multiple characteristic quality improvement for the pre-oriented yarn. First, problem is defined through the analytic hierarchy process (AHP) to specify the significant quality characteristics and control factors. Second, to greatly reduce the number of experiments, the orthogonal array of Taguchi method is applied for each single quality characteristic. The signal to noise ratios and the fuzzy inference method for quality characteristics are conducted. The artificial neural networks are implemented in the third step. The Taguchi-ANN-GA and Fuzzy-ANN-GA models are compared in this stage. Finally, the genetic algorithm is a plus to optimize the weights of the neural networks in this step. This study compares the RMSEs for multiple quality characteristics to summarize the best setting for the quality improvement of pre-oriented yarn.
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