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• W2034445578 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." @default.
• W2034445578 created "2016-06-24" @default.
• W2034445578 creator A5077756396 @default.
• W2034445578 date "2010-05-01" @default.
• W2034445578 modified "2023-10-18" @default.
• W2034445578 title "PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding" @default.
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• W2034445578 doi "https://doi.org/10.1016/j.cie.2010.01.004" @default.
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