Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network
•This paper is to optimize the fatigue life of pearlitic Grade 900A steel used in railway applications.•The FCP of the CT specimens of pearlitic Grade 900A steel have been studied.•An artificial neural network to predict m, OLR, and OCR has been designed.•The genetic algorithm is applied to find the...
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| Veröffentlicht in: | International journal of fatigue 2022-09, Vol.162, p.106975, Article 106975 |
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| container_title | International journal of fatigue |
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| creator | Masoudi Nejad, Reza Sina, Nima Ma, Wenchen Liu, Zhiliang Berto, Filippo Gholami, Aboozar |
| description | •This paper is to optimize the fatigue life of pearlitic Grade 900A steel used in railway applications.•The FCP of the CT specimens of pearlitic Grade 900A steel have been studied.•An artificial neural network to predict m, OLR, and OCR has been designed.•The genetic algorithm is applied to find the maximum fatigue life based on the input values.•Sensitivity analysis is applied to the obtained artificial neural network values.
In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this artificial neural network are compared to the surface fitting method. Then sensitivity analysis is applied to the obtained artificial neural network values to measure the effect of each input parameter on the fatigue life. Finally, the genetic algorithm is applied to find the maximum fatigue life based on the input values. |
| doi_str_mv | 10.1016/j.ijfatigue.2022.106975 |
| format | Article |
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In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this artificial neural network are compared to the surface fitting method. Then sensitivity analysis is applied to the obtained artificial neural network values to measure the effect of each input parameter on the fatigue life. Finally, the genetic algorithm is applied to find the maximum fatigue life based on the input values.</description><identifier>ISSN: 0142-1123</identifier><identifier>EISSN: 1879-3452</identifier><identifier>DOI: 10.1016/j.ijfatigue.2022.106975</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Artificial neural network ; Artificial neural networks ; Fatigue life ; Genetic algorithm ; Genetic algorithms ; Materials fatigue ; Optimization ; Rail steels ; Railway rail ; Sensitivity analysis ; Stress intensity factor</subject><ispartof>International journal of fatigue, 2022-09, Vol.162, p.106975, Article 106975</ispartof><rights>2022 Elsevier Ltd</rights><rights>Copyright Elsevier BV Sep 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c273t-20bbdac57e9a829ff980212bb664ff4daf900daf75619994ff92e48bf7cfb6d13</citedby><cites>FETCH-LOGICAL-c273t-20bbdac57e9a829ff980212bb664ff4daf900daf75619994ff92e48bf7cfb6d13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ijfatigue.2022.106975$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,27928,27929,45999</link.rule.ids></links><search><creatorcontrib>Masoudi Nejad, Reza</creatorcontrib><creatorcontrib>Sina, Nima</creatorcontrib><creatorcontrib>Ma, Wenchen</creatorcontrib><creatorcontrib>Liu, Zhiliang</creatorcontrib><creatorcontrib>Berto, Filippo</creatorcontrib><creatorcontrib>Gholami, Aboozar</creatorcontrib><title>Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network</title><title>International journal of fatigue</title><description>•This paper is to optimize the fatigue life of pearlitic Grade 900A steel used in railway applications.•The FCP of the CT specimens of pearlitic Grade 900A steel have been studied.•An artificial neural network to predict m, OLR, and OCR has been designed.•The genetic algorithm is applied to find the maximum fatigue life based on the input values.•Sensitivity analysis is applied to the obtained artificial neural network values.
In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this artificial neural network are compared to the surface fitting method. Then sensitivity analysis is applied to the obtained artificial neural network values to measure the effect of each input parameter on the fatigue life. Finally, the genetic algorithm is applied to find the maximum fatigue life based on the input values.</description><subject>Artificial neural network</subject><subject>Artificial neural networks</subject><subject>Fatigue life</subject><subject>Genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Materials fatigue</subject><subject>Optimization</subject><subject>Rail steels</subject><subject>Railway rail</subject><subject>Sensitivity analysis</subject><subject>Stress intensity factor</subject><issn>0142-1123</issn><issn>1879-3452</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFkMtOAzEMRSMEEqXwDURiPSVJ55VlVfGSKnUD6yiTcVoP00lJUhB8AN9NSlG3bGzZ8j22LyHXnE044-VtN8HO6oirHUwEEyJ1S1kVJ2TE60pm07wQp2TEeC4yzsX0nFyE0DHGJKuKEflebiNu8CsB3ECdpX8o2qOFfb0F7XuMaOiD1y1QydiMhgjQ00YHaGmSxTVQ4zYNDkfMCgbYi3S_ch7jekP10FLtI1o0qHs6wM7_pvjh_OslObO6D3D1l8fk5f7uef6YLZYPT_PZIjOimsZMsKZptSkqkLoW0lpZM8FF05Rlbm3eapvOS7EqSi6lTD0pIK8bWxnblC2fjsnNgbv17m0HIarO7fyQVipR1qXgvGYyTVWHKeNdCB6s2nrcaP-pOFN701WnjqarvenqYHpSzg5KSE-8I3gVDMJgoEUPJqrW4b-MH4ERkWo</recordid><startdate>202209</startdate><enddate>202209</enddate><creator>Masoudi Nejad, Reza</creator><creator>Sina, Nima</creator><creator>Ma, Wenchen</creator><creator>Liu, Zhiliang</creator><creator>Berto, Filippo</creator><creator>Gholami, Aboozar</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>202209</creationdate><title>Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network</title><author>Masoudi Nejad, Reza ; Sina, Nima ; Ma, Wenchen ; Liu, Zhiliang ; Berto, Filippo ; Gholami, Aboozar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c273t-20bbdac57e9a829ff980212bb664ff4daf900daf75619994ff92e48bf7cfb6d13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial neural network</topic><topic>Artificial neural networks</topic><topic>Fatigue life</topic><topic>Genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Materials fatigue</topic><topic>Optimization</topic><topic>Rail steels</topic><topic>Railway rail</topic><topic>Sensitivity analysis</topic><topic>Stress intensity factor</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Masoudi Nejad, Reza</creatorcontrib><creatorcontrib>Sina, Nima</creatorcontrib><creatorcontrib>Ma, Wenchen</creatorcontrib><creatorcontrib>Liu, Zhiliang</creatorcontrib><creatorcontrib>Berto, Filippo</creatorcontrib><creatorcontrib>Gholami, Aboozar</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>International journal of fatigue</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Masoudi Nejad, Reza</au><au>Sina, Nima</au><au>Ma, Wenchen</au><au>Liu, Zhiliang</au><au>Berto, Filippo</au><au>Gholami, Aboozar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network</atitle><jtitle>International journal of fatigue</jtitle><date>2022-09</date><risdate>2022</risdate><volume>162</volume><spage>106975</spage><pages>106975-</pages><artnum>106975</artnum><issn>0142-1123</issn><eissn>1879-3452</eissn><abstract>•This paper is to optimize the fatigue life of pearlitic Grade 900A steel used in railway applications.•The FCP of the CT specimens of pearlitic Grade 900A steel have been studied.•An artificial neural network to predict m, OLR, and OCR has been designed.•The genetic algorithm is applied to find the maximum fatigue life based on the input values.•Sensitivity analysis is applied to the obtained artificial neural network values.
In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this artificial neural network are compared to the surface fitting method. Then sensitivity analysis is applied to the obtained artificial neural network values to measure the effect of each input parameter on the fatigue life. Finally, the genetic algorithm is applied to find the maximum fatigue life based on the input values.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ijfatigue.2022.106975</doi></addata></record> |
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| ispartof | International journal of fatigue, 2022-09, Vol.162, p.106975, Article 106975 |
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| language | eng |
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| source | Access via ScienceDirect (Elsevier) |
| subjects | Artificial neural network Artificial neural networks Fatigue life Genetic algorithm Genetic algorithms Materials fatigue Optimization Rail steels Railway rail Sensitivity analysis Stress intensity factor |
| title | Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network |
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