Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling
In this work, an artificial neural network (ANN) model was established in order to predict the mechanical properties of transformation induced plasticity/twinning induced plasticity (TRIP/TWIP) steels. The model developed in this study was consider the contents of Mn (15–30 wt%), Si (2–4 wt%) and Al...
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| Veröffentlicht in: | Computational materials science 2009-06, Vol.45 (4), p.959-965 |
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| container_title | Computational materials science |
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| creator | Dini, G. Najafizadeh, A. Monir-Vaghefi, S.M. Ebnonnasir, A. |
| description | In this work, an artificial neural network (ANN) model was established in order to predict the mechanical properties of transformation induced plasticity/twinning induced plasticity (TRIP/TWIP) steels. The model developed in this study was consider the contents of Mn (15–30
wt%), Si (2–4
wt%) and Al (2–4
wt%) as inputs, while, the total elongation, yield strength and tensile strength are presented as outputs. The optimal ANN architecture and training algorithm were determined. Comparing the predicted values by ANN with the experimental data indicates that trained neural network model provides accurate results. |
| doi_str_mv | 10.1016/j.commatsci.2008.12.015 |
| format | Article |
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wt%), Si (2–4
wt%) and Al (2–4
wt%) as inputs, while, the total elongation, yield strength and tensile strength are presented as outputs. The optimal ANN architecture and training algorithm were determined. Comparing the predicted values by ANN with the experimental data indicates that trained neural network model provides accurate results.</description><identifier>ISSN: 0927-0256</identifier><identifier>EISSN: 1879-0801</identifier><identifier>DOI: 10.1016/j.commatsci.2008.12.015</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Artificial neural network (ANN) ; Condensed matter: structure, mechanical and thermal properties ; Deformation and plasticity (including yield, ductility, and superplasticity) ; Exact sciences and technology ; Mechanical and acoustical properties of condensed matter ; Mechanical properties ; Mechanical properties of solids ; Physics ; Steel ; TRIP/TWIP</subject><ispartof>Computational materials science, 2009-06, Vol.45 (4), p.959-965</ispartof><rights>2009 Elsevier B.V.</rights><rights>2009 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-425034d4c213f49db179bb5f5ce6c7ba69477b5c23bd47b531951583f40ce6183</citedby><cites>FETCH-LOGICAL-c376t-425034d4c213f49db179bb5f5ce6c7ba69477b5c23bd47b531951583f40ce6183</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S092702560900007X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21616360$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Dini, G.</creatorcontrib><creatorcontrib>Najafizadeh, A.</creatorcontrib><creatorcontrib>Monir-Vaghefi, S.M.</creatorcontrib><creatorcontrib>Ebnonnasir, A.</creatorcontrib><title>Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling</title><title>Computational materials science</title><description>In this work, an artificial neural network (ANN) model was established in order to predict the mechanical properties of transformation induced plasticity/twinning induced plasticity (TRIP/TWIP) steels. The model developed in this study was consider the contents of Mn (15–30
wt%), Si (2–4
wt%) and Al (2–4
wt%) as inputs, while, the total elongation, yield strength and tensile strength are presented as outputs. The optimal ANN architecture and training algorithm were determined. Comparing the predicted values by ANN with the experimental data indicates that trained neural network model provides accurate results.</description><subject>Artificial neural network (ANN)</subject><subject>Condensed matter: structure, mechanical and thermal properties</subject><subject>Deformation and plasticity (including yield, ductility, and superplasticity)</subject><subject>Exact sciences and technology</subject><subject>Mechanical and acoustical properties of condensed matter</subject><subject>Mechanical properties</subject><subject>Mechanical properties of solids</subject><subject>Physics</subject><subject>Steel</subject><subject>TRIP/TWIP</subject><issn>0927-0256</issn><issn>1879-0801</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNqFkN1KwzAYhoMoOKfXYE8UBdvlp2nawzGcDiYOnXgY0vSry-zPTDrFM-_BO_RKzNjw1JMkJM_7fuFB6JTgiGCSDJaRbutadU6biGKcRoRGmPA91COpyEKcYrKPejijIsSUJ4foyLkl9skspT20mFkojO5M8xK0ZVCDXqjGaFUFK9uuwHYG3OZhDD9f33eNXy6G1VXwaC6D-cNkNpg_T2aB6wAqF6zdpqWBtfXxBrqP1r4GdVtA5e-P0UGpKgcnu72PnsbX89FtOL2_mYyG01AzkXRhTDlmcRFrSlgZZ0VORJbnvOQaEi1ylWSxEDnXlOVF7A-MZJzw1LPYEyRlfXS-7fX_f1uD62RtnIaqUg20aydZzCkTGfWg2ILats5ZKOXKmlrZT0mw3JiVS_lnVm7MSkKlN-uTZ7sRynlTpVWNNu4vTklCEpZgzw23nJcD7was9E3QaC_cgu5k0Zp_Z_0CH0iUXw</recordid><startdate>20090601</startdate><enddate>20090601</enddate><creator>Dini, G.</creator><creator>Najafizadeh, A.</creator><creator>Monir-Vaghefi, S.M.</creator><creator>Ebnonnasir, A.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20090601</creationdate><title>Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling</title><author>Dini, G. ; Najafizadeh, A. ; Monir-Vaghefi, S.M. ; Ebnonnasir, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-425034d4c213f49db179bb5f5ce6c7ba69477b5c23bd47b531951583f40ce6183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Artificial neural network (ANN)</topic><topic>Condensed matter: structure, mechanical and thermal properties</topic><topic>Deformation and plasticity (including yield, ductility, and superplasticity)</topic><topic>Exact sciences and technology</topic><topic>Mechanical and acoustical properties of condensed matter</topic><topic>Mechanical properties</topic><topic>Mechanical properties of solids</topic><topic>Physics</topic><topic>Steel</topic><topic>TRIP/TWIP</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dini, G.</creatorcontrib><creatorcontrib>Najafizadeh, A.</creatorcontrib><creatorcontrib>Monir-Vaghefi, S.M.</creatorcontrib><creatorcontrib>Ebnonnasir, A.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials 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>Computational materials science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dini, G.</au><au>Najafizadeh, A.</au><au>Monir-Vaghefi, S.M.</au><au>Ebnonnasir, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling</atitle><jtitle>Computational materials science</jtitle><date>2009-06-01</date><risdate>2009</risdate><volume>45</volume><issue>4</issue><spage>959</spage><epage>965</epage><pages>959-965</pages><issn>0927-0256</issn><eissn>1879-0801</eissn><abstract>In this work, an artificial neural network (ANN) model was established in order to predict the mechanical properties of transformation induced plasticity/twinning induced plasticity (TRIP/TWIP) steels. The model developed in this study was consider the contents of Mn (15–30
wt%), Si (2–4
wt%) and Al (2–4
wt%) as inputs, while, the total elongation, yield strength and tensile strength are presented as outputs. The optimal ANN architecture and training algorithm were determined. Comparing the predicted values by ANN with the experimental data indicates that trained neural network model provides accurate results.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.commatsci.2008.12.015</doi><tpages>7</tpages></addata></record> |
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| ispartof | Computational materials science, 2009-06, Vol.45 (4), p.959-965 |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Artificial neural network (ANN) Condensed matter: structure, mechanical and thermal properties Deformation and plasticity (including yield, ductility, and superplasticity) Exact sciences and technology Mechanical and acoustical properties of condensed matter Mechanical properties Mechanical properties of solids Physics Steel TRIP/TWIP |
| title | Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling |
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