Cost estimation of plastic injection products through back-propagation network

Authors:
H. S. Wang;Z. H. Che;Y. N. Wang
Affiliations:
Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan;Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan;Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan
Venue:
NN'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Neural Networks - Volume 8
Year:
2007

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Abstract

With science and technology development, the world plastics production and consumption have been increasing continuously in the recent twenty years. The plastic injection molding has become the most widely applied mass-production technology, as it can shorten the finished product manufacturing cycle to raise productivity with products of low plastics waste, high size precision and high quality stability in addition to fully automated production. Hence, the main purpose of this study is to design the cost estimation model for plastic injection products in the design and development initial stage by the advantages of back-propagation network(BPN), which belongs to monitoring style learning network of the neural networks with advantages such as excellent diagnosis, prediction, simple theory, fast response and high learning precision through the integration of 3D mode features data, price quotations and purchase costs.