Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 2, Pages 1114-1122Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2007.10.020
Keywords
Plastic injection molding; Back-propagation neural networks; Taguchi's parameter designs; Genetic algorithms; Soft computing
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Funding
- National Science Council, R.O.C. [NSC 95-2622-E-216-010-CC3]
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Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding (PIM) industry. Previously, production engineers used either trial-and-error method or Taguchi's parameter design method to determine optimal process parameter settings for PIM. However, these methods are unsuitable in present PIM because the increasing complexity of product design and the requirement of multi-response quality characteristics. This research presents an approach in a soft computing paradigm for the process parameter optimization of multiple-input multiple-output (MIMO) plastic injection molding process. The proposed approach integrates Taguchi's parameter design method, back-propagation neural networks, genetic algorithms and engineering optimization concepts to optimize the process parameters. The research results indicate that the proposed approach call effectively help engineers determine optimal process parameter settings and achieve competitive advantages of product quality and costs. (C) 2007 Elsevier Ltd. All rights reserved.
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