PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding
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 prod...
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| Veröffentlicht in: | Computers & industrial engineering 2010-05, Vol.58 (4), p.625-637 |
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| creator | Che, Z.H. |
| description | 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. |
| doi_str_mv | 10.1016/j.cie.2010.01.004 |
| format | Article |
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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. 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The computational results show the proposed FAPSO-TBP approach is very competitive for the product and mold cost estimation problems of plastic injection molding.</description><subject>Back propagation</subject><subject>Back-propagation network</subject><subject>Comparative analysis</subject><subject>Cost estimates</subject><subject>Cost estimation</subject><subject>Discriminant analysis</subject><subject>Factor analysis</subject><subject>Injection molding</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Particle swarm optimization</subject><subject>Plastic injection molding</subject><subject>Product design</subject><subject>Propagation</subject><subject>Rapid prototyping</subject><subject>Studies</subject><issn>0360-8352</issn><issn>1879-0550</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp9kE9LxDAQxYMouK5-AG_Be2vSNG2KJ1n8BwsrqOeQpNMl3W5Tk1bx25vdevY0zPB7M28eQteUpJTQ4rZNjYU0I7EnNCUkP0ELKsoqIZyTU7QgrCCJYDw7RxchtCQSvKIL9PX6tkm0ClBjrcwuGbwb1FaN1vVY-dE21ljV4R4mfyzjt_M73DiPI1lPZsSqr_HedTU2LowYwmj3s9w1eOhU7A22fQvmODyQtt9eorNGdQGu_uoSfTw-vK-ek_Xm6WV1v04My_IxqRTLalKWQjQVE3ll8rJRAkAIVWYlJyZvlKYFaA7AGYAWOegi0xkjuqZCsyW6mfdGt59TNCdbN_k-npS0YhUreMUiRGfIeBeCh0YOPj7hfyQl8pCubGVMVx7SlYTKmF3U3M0aiO6_LHgZItIbqK2Pr8ra2X_Uv9KShHY</recordid><startdate>20100501</startdate><enddate>20100501</enddate><creator>Che, Z.H.</creator><general>Elsevier Ltd</general><general>Pergamon Press Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20100501</creationdate><title>PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding</title><author>Che, Z.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c324t-9a32d07788f93849c47fa8ee88a72750c4fab16eb5ee53eeb84eb62b230bd18b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Back propagation</topic><topic>Back-propagation network</topic><topic>Comparative analysis</topic><topic>Cost estimates</topic><topic>Cost estimation</topic><topic>Discriminant analysis</topic><topic>Factor analysis</topic><topic>Injection molding</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Particle swarm optimization</topic><topic>Plastic injection molding</topic><topic>Product design</topic><topic>Propagation</topic><topic>Rapid prototyping</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Che, Z.H.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers & industrial engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Che, Z.H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding</atitle><jtitle>Computers & industrial engineering</jtitle><date>2010-05-01</date><risdate>2010</risdate><volume>58</volume><issue>4</issue><spage>625</spage><epage>637</epage><pages>625-637</pages><issn>0360-8352</issn><eissn>1879-0550</eissn><coden>CINDDL</coden><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.</abstract><cop>New York</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.cie.2010.01.004</doi><tpages>13</tpages></addata></record> |
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| ispartof | Computers & industrial engineering, 2010-05, Vol.58 (4), p.625-637 |
| issn | 0360-8352 1879-0550 |
| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Back propagation Back-propagation network Comparative analysis Cost estimates Cost estimation Discriminant analysis Factor analysis Injection molding Neural networks Optimization Particle swarm optimization Plastic injection molding Product design Propagation Rapid prototyping Studies |
| title | PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding |
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