A methodology for modelling manufacturing costs at conceptual design
ICC&IE Selected papers from the 22nd ICC&IE conference on Computers & industrial engineering
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Computers and Structures
Application of BPN with feature-based models on cost estimation of plastic injection products
Computers and Industrial Engineering
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IEEE Transactions on Evolutionary Computation
Mathematical and Computer Modelling: An International Journal
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International Journal of Bio-Inspired Computation
Expert Systems with Applications: An International Journal
Cost estimation of plastic injection molding parts through integration of PSO and BP neural network
Expert Systems with Applications: An International Journal
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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.