Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel
Herein, isothermal compression experiments are conducted on EA4T steel at 970–1170 °C, with strain rates of 0.01–1.0 s−1 and a strain of 0.2–0.8 s−1. Based on the experimental data, a high‐temperature constitutive model is developed for EA4T steel. The activation energy of dynamic recrystallization...
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Veröffentlicht in: | Steel research international 2023-05, Vol.94 (5), p.n/a |
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description | Herein, isothermal compression experiments are conducted on EA4T steel at 970–1170 °C, with strain rates of 0.01–1.0 s−1 and a strain of 0.2–0.8 s−1. Based on the experimental data, a high‐temperature constitutive model is developed for EA4T steel. The activation energy of dynamic recrystallization (DRX) is calculated to be 383 666 J mol−1, and the correlation coefficient and root mean square error between the results of the constitutive model and experimental results are 0.9943 and 4.6823, respectively. The average grain size for each deformation condition is determined using the linear‐intercept method. The grain growth model widely used in cellular automaton (CA) simulations is found unsuitable for EA4T steel. Therefore, a modified CA model of DRX behavior suitable for EA4T steel is developed. The nucleation rates and solute drag effect coefficients under different deformation conditions are determined. Furthermore, simulations are performed under other deformation conditions using the CA model. The simulated results for the average grain size, microstructure morphology, and DRX fraction agree well with the experimental results. The reason for the deviation between the observed and simulated DRX fractions is also explored.
A high‐temperature constitutive model of EA4T steel has been established based on the results of isothermal compression experiments. A modified cellular automaton model of dynamic recrystallization (DRX) behavior suitable for EA4T steel is established. The average grain size, grain morphology, and DRX fraction obtained from the simulation are in good agreement with the experimental results. |
doi_str_mv | 10.1002/srin.202200699 |
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A high‐temperature constitutive model of EA4T steel has been established based on the results of isothermal compression experiments. A modified cellular automaton model of dynamic recrystallization (DRX) behavior suitable for EA4T steel is established. The average grain size, grain morphology, and DRX fraction obtained from the simulation are in good agreement with the experimental results.</description><identifier>ISSN: 1611-3683</identifier><identifier>EISSN: 1869-344X</identifier><identifier>DOI: 10.1002/srin.202200699</identifier><language>eng</language><publisher>Weinheim: Wiley Subscription Services, Inc</publisher><subject>Cellular automata ; cellular automaton ; constitutive model ; Constitutive models ; Correlation coefficients ; Deformation effects ; Dynamic recrystallization ; EA4T steels ; Grain growth ; Grain size ; Growth models ; Low alloy steels ; Mathematical models ; microstructure morphology ; Nucleation ; Simulation</subject><ispartof>Steel research international, 2023-05, Vol.94 (5), p.n/a</ispartof><rights>2023 Wiley‐VCH GmbH</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3179-4f0b71c5728bb574072496925a6dfe8c786cb42a0acea7f1cfb07c62867e1afb3</citedby><cites>FETCH-LOGICAL-c3179-4f0b71c5728bb574072496925a6dfe8c786cb42a0acea7f1cfb07c62867e1afb3</cites><orcidid>0000-0002-2552-3292 ; 0000-0002-0836-7873</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fsrin.202200699$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fsrin.202200699$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Ren, Xu</creatorcontrib><creatorcontrib>Huo, Yuan-Ming</creatorcontrib><creatorcontrib>He, Tao</creatorcontrib><creatorcontrib>Hosseini, Seyed Reza Elmi</creatorcontrib><creatorcontrib>Bian, Zhi-Yuan</creatorcontrib><creatorcontrib>Bai, Jie</creatorcontrib><creatorcontrib>Du, Xiang-Yang</creatorcontrib><title>Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel</title><title>Steel research international</title><description>Herein, isothermal compression experiments are conducted on EA4T steel at 970–1170 °C, with strain rates of 0.01–1.0 s−1 and a strain of 0.2–0.8 s−1. Based on the experimental data, a high‐temperature constitutive model is developed for EA4T steel. The activation energy of dynamic recrystallization (DRX) is calculated to be 383 666 J mol−1, and the correlation coefficient and root mean square error between the results of the constitutive model and experimental results are 0.9943 and 4.6823, respectively. The average grain size for each deformation condition is determined using the linear‐intercept method. The grain growth model widely used in cellular automaton (CA) simulations is found unsuitable for EA4T steel. Therefore, a modified CA model of DRX behavior suitable for EA4T steel is developed. The nucleation rates and solute drag effect coefficients under different deformation conditions are determined. Furthermore, simulations are performed under other deformation conditions using the CA model. The simulated results for the average grain size, microstructure morphology, and DRX fraction agree well with the experimental results. The reason for the deviation between the observed and simulated DRX fractions is also explored.
A high‐temperature constitutive model of EA4T steel has been established based on the results of isothermal compression experiments. A modified cellular automaton model of dynamic recrystallization (DRX) behavior suitable for EA4T steel is established. The average grain size, grain morphology, and DRX fraction obtained from the simulation are in good agreement with the experimental results.</description><subject>Cellular automata</subject><subject>cellular automaton</subject><subject>constitutive model</subject><subject>Constitutive models</subject><subject>Correlation coefficients</subject><subject>Deformation effects</subject><subject>Dynamic recrystallization</subject><subject>EA4T steels</subject><subject>Grain growth</subject><subject>Grain size</subject><subject>Growth models</subject><subject>Low alloy steels</subject><subject>Mathematical models</subject><subject>microstructure morphology</subject><subject>Nucleation</subject><subject>Simulation</subject><issn>1611-3683</issn><issn>1869-344X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNqFkEFLwzAUx4soOHRXzwHPnUmaJelx1DkHU2Gb4K2kaYoZaTuT1FFPfgQ_o5_EjMk8-i7vz-P3f4_3j6IrBEcIQnzjrG5GGGIMIU3Tk2iAOE3jhJCX06ApQnFCeXIeDZ3bwFAJ55SRQeSytnFe-87rdwUe2lIZIJoSZMqYzggLJp1va-HbBqx0HSZeB9lWwL8qcNs3otYSLJW0vfPCGP1xBJZCm53owXRC1t-fXzMrSgVWXilzGZ1Vwjg1_O0X0fPddJ3dx4un2TybLGKZIJbGpIIFQ3LMMC-KMSOQYZLSFI8FLSvFJeNUFgQLKKQSrEKyKiCTFIfHFBJVkVxE14e9W9u-dcr5fNN2tgknc8whR2mSUBKo0YGStnXOqirfWl0L2-cI5vts8322-THbYEgPhp02qv-HzlfL-eOf9wdfEIAw</recordid><startdate>202305</startdate><enddate>202305</enddate><creator>Ren, Xu</creator><creator>Huo, Yuan-Ming</creator><creator>He, Tao</creator><creator>Hosseini, Seyed Reza Elmi</creator><creator>Bian, Zhi-Yuan</creator><creator>Bai, Jie</creator><creator>Du, Xiang-Yang</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><orcidid>https://orcid.org/0000-0002-2552-3292</orcidid><orcidid>https://orcid.org/0000-0002-0836-7873</orcidid></search><sort><creationdate>202305</creationdate><title>Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel</title><author>Ren, Xu ; Huo, Yuan-Ming ; He, Tao ; Hosseini, Seyed Reza Elmi ; Bian, Zhi-Yuan ; Bai, Jie ; Du, Xiang-Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3179-4f0b71c5728bb574072496925a6dfe8c786cb42a0acea7f1cfb07c62867e1afb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Cellular automata</topic><topic>cellular automaton</topic><topic>constitutive model</topic><topic>Constitutive models</topic><topic>Correlation coefficients</topic><topic>Deformation effects</topic><topic>Dynamic recrystallization</topic><topic>EA4T steels</topic><topic>Grain growth</topic><topic>Grain size</topic><topic>Growth models</topic><topic>Low alloy steels</topic><topic>Mathematical models</topic><topic>microstructure morphology</topic><topic>Nucleation</topic><topic>Simulation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ren, Xu</creatorcontrib><creatorcontrib>Huo, Yuan-Ming</creatorcontrib><creatorcontrib>He, Tao</creatorcontrib><creatorcontrib>Hosseini, Seyed Reza Elmi</creatorcontrib><creatorcontrib>Bian, Zhi-Yuan</creatorcontrib><creatorcontrib>Bai, Jie</creatorcontrib><creatorcontrib>Du, Xiang-Yang</creatorcontrib><collection>CrossRef</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Steel research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ren, Xu</au><au>Huo, Yuan-Ming</au><au>He, Tao</au><au>Hosseini, Seyed Reza Elmi</au><au>Bian, Zhi-Yuan</au><au>Bai, Jie</au><au>Du, Xiang-Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel</atitle><jtitle>Steel research international</jtitle><date>2023-05</date><risdate>2023</risdate><volume>94</volume><issue>5</issue><epage>n/a</epage><issn>1611-3683</issn><eissn>1869-344X</eissn><abstract>Herein, isothermal compression experiments are conducted on EA4T steel at 970–1170 °C, with strain rates of 0.01–1.0 s−1 and a strain of 0.2–0.8 s−1. Based on the experimental data, a high‐temperature constitutive model is developed for EA4T steel. The activation energy of dynamic recrystallization (DRX) is calculated to be 383 666 J mol−1, and the correlation coefficient and root mean square error between the results of the constitutive model and experimental results are 0.9943 and 4.6823, respectively. The average grain size for each deformation condition is determined using the linear‐intercept method. The grain growth model widely used in cellular automaton (CA) simulations is found unsuitable for EA4T steel. Therefore, a modified CA model of DRX behavior suitable for EA4T steel is developed. The nucleation rates and solute drag effect coefficients under different deformation conditions are determined. Furthermore, simulations are performed under other deformation conditions using the CA model. The simulated results for the average grain size, microstructure morphology, and DRX fraction agree well with the experimental results. The reason for the deviation between the observed and simulated DRX fractions is also explored.
A high‐temperature constitutive model of EA4T steel has been established based on the results of isothermal compression experiments. A modified cellular automaton model of dynamic recrystallization (DRX) behavior suitable for EA4T steel is established. The average grain size, grain morphology, and DRX fraction obtained from the simulation are in good agreement with the experimental results.</abstract><cop>Weinheim</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/srin.202200699</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-2552-3292</orcidid><orcidid>https://orcid.org/0000-0002-0836-7873</orcidid></addata></record> |
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subjects | Cellular automata cellular automaton constitutive model Constitutive models Correlation coefficients Deformation effects Dynamic recrystallization EA4T steels Grain growth Grain size Growth models Low alloy steels Mathematical models microstructure morphology Nucleation Simulation |
title | Constitutive Model and Cellular Automaton Simulation of the Dynamic Recrystallization of Railway EA4T‐Grade Steel |
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