Applying robust multi-response quality engineering for parameter selection using a novel neural-genetic algorithm
This study presents a neural-genetic algorithm to solve the selection problem of manufacturing process parameters. The proposed algorithm is a combination of artificial neural network (ANN) and genetic algorithms (GAs). In addition, the neural network is used to formulate a fitness function for predicting the value of the response based on the parameter settings. GAs then take the fitness function from the trained neural network to search for the optimal parameter combination. Owing to the most of manufactured products have more than one quality characteristic and the quality characteristics are generally correlated with each other, this study also proposes a desirability function to obtain a compromise, composite solution. A case study of how the silicon manufacturing process parameters are selected offline demonstrates the effectiveness of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.
parameter selection genetic algorithm artificial neural network desirability function multi-response optimization