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Rubber fatigue life prediction using a random forest method and nonlinear cumulative fatigue damage model
ABSTRACT In this study, a random forest machine‐learning method is introduced on the basis of the analysis of measured constant amplitude stress fatigue data. This method aims to predict rubber fatigue life under constant amplitude stress. Strain mean value, strain amplitude, and strain ratio are us...
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Published in: | Journal of applied polymer science 2020-04, Vol.137 (14), p.n/a |
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description | ABSTRACT
In this study, a random forest machine‐learning method is introduced on the basis of the analysis of measured constant amplitude stress fatigue data. This method aims to predict rubber fatigue life under constant amplitude stress. Strain mean value, strain amplitude, and strain ratio are used as independent variables, and the prediction model of rubber fatigue life under constant amplitude stress is established. A nonlinear cumulative fatigue damage model is proposed to calculate rubber fatigue life under the variable amplitude stress. Results show that the random forest method has high precision and generalization capability for rubber fatigue life prediction under constant amplitude stress and the nonlinear cumulative fatigue damage model could be employed to calculate the fatigue life of rubber under variable amplitude stress with enough accuracy according to the constant amplitude stress fatigue life data. This research can provide a reference for rubber fatigue life prediction. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48519. |
doi_str_mv | 10.1002/app.48519 |
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In this study, a random forest machine‐learning method is introduced on the basis of the analysis of measured constant amplitude stress fatigue data. This method aims to predict rubber fatigue life under constant amplitude stress. Strain mean value, strain amplitude, and strain ratio are used as independent variables, and the prediction model of rubber fatigue life under constant amplitude stress is established. A nonlinear cumulative fatigue damage model is proposed to calculate rubber fatigue life under the variable amplitude stress. Results show that the random forest method has high precision and generalization capability for rubber fatigue life prediction under constant amplitude stress and the nonlinear cumulative fatigue damage model could be employed to calculate the fatigue life of rubber under variable amplitude stress with enough accuracy according to the constant amplitude stress fatigue life data. This research can provide a reference for rubber fatigue life prediction. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48519.</description><identifier>ISSN: 0021-8995</identifier><identifier>EISSN: 1097-4628</identifier><identifier>DOI: 10.1002/app.48519</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Amplitudes ; Damage assessment ; Fatigue failure ; Fatigue life ; Independent variables ; Life prediction ; Materials science ; Mathematical models ; nonlinear cumulative fatigue damage model ; Polymers ; random forest ; Rubber ; rubber fatigue ; Strain ; variable amplitude stress</subject><ispartof>Journal of applied polymer science, 2020-04, Vol.137 (14), p.n/a</ispartof><rights>2019 Wiley Periodicals, Inc.</rights><rights>2020 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4009-26dd02ec9f15b4aa930096b477421b734b2060e6533ae103635fe395e1d81c1e3</citedby><cites>FETCH-LOGICAL-c4009-26dd02ec9f15b4aa930096b477421b734b2060e6533ae103635fe395e1d81c1e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Liu, Qiaobin</creatorcontrib><creatorcontrib>Shi, Wenku</creatorcontrib><creatorcontrib>Chen, Zhiyong</creatorcontrib><title>Rubber fatigue life prediction using a random forest method and nonlinear cumulative fatigue damage model</title><title>Journal of applied polymer science</title><description>ABSTRACT
In this study, a random forest machine‐learning method is introduced on the basis of the analysis of measured constant amplitude stress fatigue data. This method aims to predict rubber fatigue life under constant amplitude stress. Strain mean value, strain amplitude, and strain ratio are used as independent variables, and the prediction model of rubber fatigue life under constant amplitude stress is established. A nonlinear cumulative fatigue damage model is proposed to calculate rubber fatigue life under the variable amplitude stress. Results show that the random forest method has high precision and generalization capability for rubber fatigue life prediction under constant amplitude stress and the nonlinear cumulative fatigue damage model could be employed to calculate the fatigue life of rubber under variable amplitude stress with enough accuracy according to the constant amplitude stress fatigue life data. This research can provide a reference for rubber fatigue life prediction. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48519.</description><subject>Amplitudes</subject><subject>Damage assessment</subject><subject>Fatigue failure</subject><subject>Fatigue life</subject><subject>Independent variables</subject><subject>Life prediction</subject><subject>Materials science</subject><subject>Mathematical models</subject><subject>nonlinear cumulative fatigue damage model</subject><subject>Polymers</subject><subject>random forest</subject><subject>Rubber</subject><subject>rubber fatigue</subject><subject>Strain</subject><subject>variable amplitude stress</subject><issn>0021-8995</issn><issn>1097-4628</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kE9LxDAQxYMouK4e_AYBTx66m0nadHNcFv-B4CJ6DmkzXbO0TU1bZb-90Yo3YWDgzW_eDI-QS2ALYIwvTdct0lUG6ojMgKk8SSVfHZNZnEGyUio7JWd9v2cMIGNyRtzzWBQYaGUGtxuR1q5C2gW0rhycb-nYu3ZHDQ2mtb6hlQ_YD7TB4c1bGjXa-rZ2LZpAy7EZ62jzgX9u1jRmh7TxFutzclKZuseL3z4nr7c3L5v75PHp7mGzfkzKlDGVcGkt41iqCrIiNUaJqMoizfOUQ5GLtOBMMpSZEAaBCSmyCoXKEOwKSkAxJ1eTbxf8-xi_1Xs_hjae1FwIzkDGitT1RJXB933ASnfBNSYcNDD9naSOSeqfJCO7nNhPV-Phf1Cvt9tp4wsjOHUo</recordid><startdate>20200410</startdate><enddate>20200410</enddate><creator>Liu, Qiaobin</creator><creator>Shi, Wenku</creator><creator>Chen, Zhiyong</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20200410</creationdate><title>Rubber fatigue life prediction using a random forest method and nonlinear cumulative fatigue damage model</title><author>Liu, Qiaobin ; Shi, Wenku ; Chen, Zhiyong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4009-26dd02ec9f15b4aa930096b477421b734b2060e6533ae103635fe395e1d81c1e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Amplitudes</topic><topic>Damage assessment</topic><topic>Fatigue failure</topic><topic>Fatigue life</topic><topic>Independent variables</topic><topic>Life prediction</topic><topic>Materials science</topic><topic>Mathematical models</topic><topic>nonlinear cumulative fatigue damage model</topic><topic>Polymers</topic><topic>random forest</topic><topic>Rubber</topic><topic>rubber fatigue</topic><topic>Strain</topic><topic>variable amplitude stress</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Qiaobin</creatorcontrib><creatorcontrib>Shi, Wenku</creatorcontrib><creatorcontrib>Chen, Zhiyong</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Journal of applied polymer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Qiaobin</au><au>Shi, Wenku</au><au>Chen, Zhiyong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rubber fatigue life prediction using a random forest method and nonlinear cumulative fatigue damage model</atitle><jtitle>Journal of applied polymer science</jtitle><date>2020-04-10</date><risdate>2020</risdate><volume>137</volume><issue>14</issue><epage>n/a</epage><issn>0021-8995</issn><eissn>1097-4628</eissn><abstract>ABSTRACT
In this study, a random forest machine‐learning method is introduced on the basis of the analysis of measured constant amplitude stress fatigue data. This method aims to predict rubber fatigue life under constant amplitude stress. Strain mean value, strain amplitude, and strain ratio are used as independent variables, and the prediction model of rubber fatigue life under constant amplitude stress is established. A nonlinear cumulative fatigue damage model is proposed to calculate rubber fatigue life under the variable amplitude stress. Results show that the random forest method has high precision and generalization capability for rubber fatigue life prediction under constant amplitude stress and the nonlinear cumulative fatigue damage model could be employed to calculate the fatigue life of rubber under variable amplitude stress with enough accuracy according to the constant amplitude stress fatigue life data. This research can provide a reference for rubber fatigue life prediction. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48519.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/app.48519</doi><tpages>6</tpages></addata></record> |
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subjects | Amplitudes Damage assessment Fatigue failure Fatigue life Independent variables Life prediction Materials science Mathematical models nonlinear cumulative fatigue damage model Polymers random forest Rubber rubber fatigue Strain variable amplitude stress |
title | Rubber fatigue life prediction using a random forest method and nonlinear cumulative fatigue damage model |
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