Hybrid neural network‐based prediction model for tribological properties of polyamide6‐based friction materials
The hybrid neural network employed to predict the tribological properties of solid lubricants reinforced nano‐TiO2/polyamide6 composites was established based on the back propagation and radial basis function networks, and optimized by adaptive genetic algorithm. With such three factors as material...
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| Veröffentlicht in: | Polymer composites 2017-08, Vol.38 (8), p.1705-1711 |
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| container_title | Polymer composites |
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| creator | Li, Duxin Lv, Ruoyun Si, Gaojie You, Yilan |
| description | The hybrid neural network employed to predict the tribological properties of solid lubricants reinforced nano‐TiO2/polyamide6 composites was established based on the back propagation and radial basis function networks, and optimized by adaptive genetic algorithm. With such three factors as material composition, testing loads, and velocities, orthogonal tests were designed and the data obtained was used for training the neural network. The correlation index between the predicted and the experimental values for friction coefficient and ware rate were 0.992 and 0.998, respectively, and 3D plots for the predicted friction coefficient and wear rate as a function of material compositions and testing conditions were established. It shows that the results are in good agreement with the data measured. It is demonstrated that the well‐optimized neural network had remarkable capability for modeling concern. POLYM. COMPOS., 38:1705–1711, 2017. © 2015 Society of Plastics Engineers |
| doi_str_mv | 10.1002/pc.23740 |
| format | Article |
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With such three factors as material composition, testing loads, and velocities, orthogonal tests were designed and the data obtained was used for training the neural network. The correlation index between the predicted and the experimental values for friction coefficient and ware rate were 0.992 and 0.998, respectively, and 3D plots for the predicted friction coefficient and wear rate as a function of material compositions and testing conditions were established. It shows that the results are in good agreement with the data measured. It is demonstrated that the well‐optimized neural network had remarkable capability for modeling concern. POLYM. COMPOS., 38:1705–1711, 2017. © 2015 Society of Plastics Engineers</description><identifier>ISSN: 0272-8397</identifier><identifier>EISSN: 1548-0569</identifier><identifier>DOI: 10.1002/pc.23740</identifier><language>eng</language><publisher>Newtown: Blackwell Publishing Ltd</publisher><subject>Adaptive algorithms ; Back propagation networks ; Friction ; Genetic algorithms ; Loads (forces) ; Mathematical models ; Neural networks ; Polymers ; Prediction models ; Radial basis function ; Solid lubricants ; Titanium oxides ; Tribology ; Wear rate</subject><ispartof>Polymer composites, 2017-08, Vol.38 (8), p.1705-1711</ispartof><rights>2015 Society of Plastics Engineers</rights><rights>2017 Society of Plastics Engineers</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2930-44f3b1d9fa800982f10a6f8652230af46b8e8d74e05410b2299063b23e8eb9153</citedby><cites>FETCH-LOGICAL-c2930-44f3b1d9fa800982f10a6f8652230af46b8e8d74e05410b2299063b23e8eb9153</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fpc.23740$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fpc.23740$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,778,782,1414,27907,27908,45557,45558</link.rule.ids></links><search><creatorcontrib>Li, Duxin</creatorcontrib><creatorcontrib>Lv, Ruoyun</creatorcontrib><creatorcontrib>Si, Gaojie</creatorcontrib><creatorcontrib>You, Yilan</creatorcontrib><title>Hybrid neural network‐based prediction model for tribological properties of polyamide6‐based friction materials</title><title>Polymer composites</title><description>The hybrid neural network employed to predict the tribological properties of solid lubricants reinforced nano‐TiO2/polyamide6 composites was established based on the back propagation and radial basis function networks, and optimized by adaptive genetic algorithm. With such three factors as material composition, testing loads, and velocities, orthogonal tests were designed and the data obtained was used for training the neural network. The correlation index between the predicted and the experimental values for friction coefficient and ware rate were 0.992 and 0.998, respectively, and 3D plots for the predicted friction coefficient and wear rate as a function of material compositions and testing conditions were established. It shows that the results are in good agreement with the data measured. It is demonstrated that the well‐optimized neural network had remarkable capability for modeling concern. POLYM. COMPOS., 38:1705–1711, 2017. © 2015 Society of Plastics Engineers</description><subject>Adaptive algorithms</subject><subject>Back propagation networks</subject><subject>Friction</subject><subject>Genetic algorithms</subject><subject>Loads (forces)</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Polymers</subject><subject>Prediction models</subject><subject>Radial basis function</subject><subject>Solid lubricants</subject><subject>Titanium oxides</subject><subject>Tribology</subject><subject>Wear rate</subject><issn>0272-8397</issn><issn>1548-0569</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp10LFOwzAQBmALgUQpSDxCJBaWlLOdOPaIKqBIlWCA2bKTM3JJ62CnQt14BJ6RJyEQYGP6l-_-Ox0hpxRmFIBddPWM8aqAPTKhZSFzKIXaJxNgFcslV9UhOUppNUgqBJ-QtNjZ6Jtsg9to2iH61xCfP97erUnYZF3Exte9D5tsHRpsMxdi1kdvQxuefD1MdDF0GHuPKQsu60K7M2vfoPircPG3wPQYvWnTMTlwQ-DJT07J4_XVw3yRL-9ubueXy7xmikNeFI5b2ihnJICSzFEwwklRMsbBuEJYibKpCoSyoGAZUwoEt4yjRKtoyafkbOwdbnzZYur1KmzjZlipqWJCllIAHdT5qOoYUorodBf92sSdpqC_Xqq7Wn-_dKD5SF99i7t_nb6fj_4Tnkp55A</recordid><startdate>201708</startdate><enddate>201708</enddate><creator>Li, Duxin</creator><creator>Lv, Ruoyun</creator><creator>Si, Gaojie</creator><creator>You, Yilan</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>201708</creationdate><title>Hybrid neural network‐based prediction model for tribological properties of polyamide6‐based friction materials</title><author>Li, Duxin ; Lv, Ruoyun ; Si, Gaojie ; You, Yilan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2930-44f3b1d9fa800982f10a6f8652230af46b8e8d74e05410b2299063b23e8eb9153</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adaptive algorithms</topic><topic>Back propagation networks</topic><topic>Friction</topic><topic>Genetic algorithms</topic><topic>Loads (forces)</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Polymers</topic><topic>Prediction models</topic><topic>Radial basis function</topic><topic>Solid lubricants</topic><topic>Titanium oxides</topic><topic>Tribology</topic><topic>Wear rate</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Duxin</creatorcontrib><creatorcontrib>Lv, Ruoyun</creatorcontrib><creatorcontrib>Si, Gaojie</creatorcontrib><creatorcontrib>You, Yilan</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Polymer composites</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Duxin</au><au>Lv, Ruoyun</au><au>Si, Gaojie</au><au>You, Yilan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hybrid neural network‐based prediction model for tribological properties of polyamide6‐based friction materials</atitle><jtitle>Polymer composites</jtitle><date>2017-08</date><risdate>2017</risdate><volume>38</volume><issue>8</issue><spage>1705</spage><epage>1711</epage><pages>1705-1711</pages><issn>0272-8397</issn><eissn>1548-0569</eissn><abstract>The hybrid neural network employed to predict the tribological properties of solid lubricants reinforced nano‐TiO2/polyamide6 composites was established based on the back propagation and radial basis function networks, and optimized by adaptive genetic algorithm. With such three factors as material composition, testing loads, and velocities, orthogonal tests were designed and the data obtained was used for training the neural network. The correlation index between the predicted and the experimental values for friction coefficient and ware rate were 0.992 and 0.998, respectively, and 3D plots for the predicted friction coefficient and wear rate as a function of material compositions and testing conditions were established. It shows that the results are in good agreement with the data measured. It is demonstrated that the well‐optimized neural network had remarkable capability for modeling concern. POLYM. COMPOS., 38:1705–1711, 2017. © 2015 Society of Plastics Engineers</abstract><cop>Newtown</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/pc.23740</doi><tpages>7</tpages></addata></record> |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Adaptive algorithms Back propagation networks Friction Genetic algorithms Loads (forces) Mathematical models Neural networks Polymers Prediction models Radial basis function Solid lubricants Titanium oxides Tribology Wear rate |
| title | Hybrid neural network‐based prediction model for tribological properties of polyamide6‐based friction materials |
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