PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding |
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| Authors: | ZH Che |
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| Affiliation: | 1. Area de Ciencia e Ingeniería de los Materiales, E.T.S. Ingenieros Industriales, Universidad de Castilla — La Mancha, Edificio Politécnico, Avda. Camilo Jose Cela s/n, 13071 Ciudad Real, Spain;2. Area de Ingeniería Mecánica, E.T.S. Ingenieros Industriales, Universidad de Castilla — La Mancha, Edificio Politécnico, Avda. Camilo Jose Cela s/n, 13071 Ciudad Real, Spain;1. School of Information Science and Engineering, Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang 110819, China;2. Department of Systems and Computer Networks, Wroc?aw University of Technology, Wyb. Wyspiańskiego 27, 50-370 Wroc?aw, Poland;3. Department of Computer Science and Artificial Intelligence, University of Granada, P.O. Box 18071, Granada, Spain;4. Instituto Nacional de Astrofísica, Óptica y Electrónica, Computer Science Department, Luis E. Erro No. 1, Santa María Tonantzintla, Puebla, 72840, Mexico;5. Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia |
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| Abstract: | To simplify complicated traditional cost estimation flow, this study emphasizes the cost estimation approach for plastic injection products and molds. It is expected designers and R&D specialists can consider the competitiveness of product cost in the early stage of product design to reduce product development time and cost resulting from repetitive modification. Therefore, the proposed cost estimation approach combines factor analysis (FA), particle swarm optimization (PSO) and artificial neural network with two back-propagation networks, called FAPSO-TBP. In addition, another artificial neural network estimation approach with a single back-propagation network, called FAPSO-SBP, is also established. To verify the proposed FAPSO-TBP approach, comparisons with the FAPSO-SBP and general back-propagation artificial neural network (GBP) are made. The computational results show the proposed FAPSO-TBP approach is very competitive for the product and mold cost estimation problems of plastic injection molding. |
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