Robust Parameter Design by Neural networks and Genetic Algorithms
نویسندگان
چکیده
Taguchi’s robust parameter design has been widely applied to a variety of quality engineering problems; however, it is unable to deal with dynamic multiresponse owing to the increasing complexity of the product or design process. This study incorporates desirability functions into a hybrid neural network/genetic algorithm approach to optimize the parameter design of dynamic multiresponses with continuous values of parameters. The objective is to find the optimal combination of control factors to simultaneously maximize the robustness of each response. The effectiveness of the propose approach is illustrated with a simulated example. The results of analysis reveal that the approach has higher performance than the traditional experimental design does.
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