Applying robust multi-response quality engineering for parameter selection using a novel neural--genetic algorithm

  • Authors:

  • T. S. Li;C. T. Su;T. L. Chiang

  • Affiliations:

  • Department of Industrial Engineering and Management, Ming Hsin University of Science and Technology, Hsinchu, Taiwan 304, ROC;Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan 300, ROC;Department of Business Administration, Ming Hsin University of Science and Technology, Hsinchu, Taiwan 304, ROC

  • Venue:
  • Computers in Industry
  • Year:
  • 2003

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Abstract

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.