Cost estimation of plastic injection molding parts through integration of PSO and BP neural network

Authors:
H. S. Wang;Y. N. Wang;Y. C. Wang
Affiliations:
Department of Industrial Engineering & Management, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 106, Taiwan, ROC;Department of Industrial Engineering & Management, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 106, Taiwan, ROC;Department of Industrial Engineering & Management, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 106, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

Plastic injection molding technology has been widely used in a variety of high-tech products, auto parts and generic household products. Against the waves of globalization, the plastic injection enterprises must shorten time-to-market to enhancement of competence, and launch products ahead of all other competitors, and thus they can quickly seize a big target market and lead the price. The backpropagation (BP) neural network was used in this study to construct an estimating model for the cost of plastic injection molding parts so as to reduce the complexity in the traditional cost estimating procedures. Because the parameters of BP neural network have a significant influence on results, and particle swarm optimization (PSO) is capable of quickly finding optimal solutions. We integrated PSO and BP neural network so that the convergence rate was improved and precision was relatively enhanced through particle evolutions based on the optimum parameter combination from BP neural network.