Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)

The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< wa...
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Autor*in:	Z. M. Cai [verfasserIn] H. C. Ji [verfasserIn] W. C. Pei [verfasserIn] X. M. Huang [verfasserIn] W. D. Li [verfasserIn] Y. M. Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	3Cr23Ni8Mn3N artificial neural network constitutive model heat - resistant stress - strain curve

Übergeordnetes Werk:	In: Metalurgija - Croatian Metallurgical Society, 2007, 58(2019), 3-4, Seite 191-195
Übergeordnetes Werk:	volume:58 ; year:2019 ; number:3-4 ; pages:191-195

Links:	Link aufrufen Link aufrufen Journal toc Journal toc

Katalog-ID:	DOAJ045196257

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520			\|a The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing.
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allfields	(DE-627)DOAJ045196257 (DE-599)DOAJa51357825b7945d6a8e8c2a2e866854f DE-627 ger DE-627 rakwb eng TN1-997 Z. M. Cai verfasserin aut Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing. 3Cr23Ni8Mn3N artificial neural network constitutive model heat - resistant stress - strain curve Mining engineering. Metallurgy H. C. Ji verfasserin aut W. C. Pei verfasserin aut X. M. Huang verfasserin aut W. D. Li verfasserin aut Y. M. Li verfasserin aut In Metalurgija Croatian Metallurgical Society, 2007 58(2019), 3-4, Seite 191-195 (DE-627)523860609 (DE-600)2268647-2 13342576 nnns volume:58 year:2019 number:3-4 pages:191-195 https://doaj.org/article/a51357825b7945d6a8e8c2a2e866854f kostenfrei http://hrcak.srce.hr/file/318746 kostenfrei https://doaj.org/toc/0543-5846 Journal toc kostenfrei https://doaj.org/toc/1334-2576 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2019 3-4 191-195
spelling	(DE-627)DOAJ045196257 (DE-599)DOAJa51357825b7945d6a8e8c2a2e866854f DE-627 ger DE-627 rakwb eng TN1-997 Z. M. Cai verfasserin aut Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing. 3Cr23Ni8Mn3N artificial neural network constitutive model heat - resistant stress - strain curve Mining engineering. Metallurgy H. C. Ji verfasserin aut W. C. Pei verfasserin aut X. M. Huang verfasserin aut W. D. Li verfasserin aut Y. M. Li verfasserin aut In Metalurgija Croatian Metallurgical Society, 2007 58(2019), 3-4, Seite 191-195 (DE-627)523860609 (DE-600)2268647-2 13342576 nnns volume:58 year:2019 number:3-4 pages:191-195 https://doaj.org/article/a51357825b7945d6a8e8c2a2e866854f kostenfrei http://hrcak.srce.hr/file/318746 kostenfrei https://doaj.org/toc/0543-5846 Journal toc kostenfrei https://doaj.org/toc/1334-2576 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2019 3-4 191-195
allfields_unstemmed	(DE-627)DOAJ045196257 (DE-599)DOAJa51357825b7945d6a8e8c2a2e866854f DE-627 ger DE-627 rakwb eng TN1-997 Z. M. Cai verfasserin aut Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing. 3Cr23Ni8Mn3N artificial neural network constitutive model heat - resistant stress - strain curve Mining engineering. Metallurgy H. C. Ji verfasserin aut W. C. Pei verfasserin aut X. M. Huang verfasserin aut W. D. Li verfasserin aut Y. M. Li verfasserin aut In Metalurgija Croatian Metallurgical Society, 2007 58(2019), 3-4, Seite 191-195 (DE-627)523860609 (DE-600)2268647-2 13342576 nnns volume:58 year:2019 number:3-4 pages:191-195 https://doaj.org/article/a51357825b7945d6a8e8c2a2e866854f kostenfrei http://hrcak.srce.hr/file/318746 kostenfrei https://doaj.org/toc/0543-5846 Journal toc kostenfrei https://doaj.org/toc/1334-2576 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2019 3-4 191-195
allfieldsGer	(DE-627)DOAJ045196257 (DE-599)DOAJa51357825b7945d6a8e8c2a2e866854f DE-627 ger DE-627 rakwb eng TN1-997 Z. M. Cai verfasserin aut Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing. 3Cr23Ni8Mn3N artificial neural network constitutive model heat - resistant stress - strain curve Mining engineering. Metallurgy H. C. Ji verfasserin aut W. C. Pei verfasserin aut X. M. Huang verfasserin aut W. D. Li verfasserin aut Y. M. Li verfasserin aut In Metalurgija Croatian Metallurgical Society, 2007 58(2019), 3-4, Seite 191-195 (DE-627)523860609 (DE-600)2268647-2 13342576 nnns volume:58 year:2019 number:3-4 pages:191-195 https://doaj.org/article/a51357825b7945d6a8e8c2a2e866854f kostenfrei http://hrcak.srce.hr/file/318746 kostenfrei https://doaj.org/toc/0543-5846 Journal toc kostenfrei https://doaj.org/toc/1334-2576 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2019 3-4 191-195
allfieldsSound	(DE-627)DOAJ045196257 (DE-599)DOAJa51357825b7945d6a8e8c2a2e866854f DE-627 ger DE-627 rakwb eng TN1-997 Z. M. Cai verfasserin aut Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN) 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing. 3Cr23Ni8Mn3N artificial neural network constitutive model heat - resistant stress - strain curve Mining engineering. Metallurgy H. C. Ji verfasserin aut W. C. Pei verfasserin aut X. M. Huang verfasserin aut W. D. Li verfasserin aut Y. M. Li verfasserin aut In Metalurgija Croatian Metallurgical Society, 2007 58(2019), 3-4, Seite 191-195 (DE-627)523860609 (DE-600)2268647-2 13342576 nnns volume:58 year:2019 number:3-4 pages:191-195 https://doaj.org/article/a51357825b7945d6a8e8c2a2e866854f kostenfrei http://hrcak.srce.hr/file/318746 kostenfrei https://doaj.org/toc/0543-5846 Journal toc kostenfrei https://doaj.org/toc/1334-2576 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2019 3-4 191-195
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callnumber-first	T - Technology
author	Z. M. Cai
spellingShingle	Z. M. Cai misc TN1-997 misc 3Cr23Ni8Mn3N misc artificial neural network misc constitutive model misc heat - resistant misc stress - strain curve misc Mining engineering. Metallurgy Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)
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topic_title	TN1-997 Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN) 3Cr23Ni8Mn3N artificial neural network constitutive model heat - resistant stress - strain curve
topic	misc TN1-997 misc 3Cr23Ni8Mn3N misc artificial neural network misc constitutive model misc heat - resistant misc stress - strain curve misc Mining engineering. Metallurgy
topic_unstemmed	misc TN1-997 misc 3Cr23Ni8Mn3N misc artificial neural network misc constitutive model misc heat - resistant misc stress - strain curve misc Mining engineering. Metallurgy
topic_browse	misc TN1-997 misc 3Cr23Ni8Mn3N misc artificial neural network misc constitutive model misc heat - resistant misc stress - strain curve misc Mining engineering. Metallurgy
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title	Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)
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title_full	Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)
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author_browse	Z. M. Cai H. C. Ji W. C. Pei X. M. Huang W. D. Li Y. M. Li
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title_sort	constitutive model of 3cr23ni8mn3n heat-resistant steel based on back propagation (bp) neural network (nn)
callnumber	TN1-997
title_auth	Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)
abstract	The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing.
abstractGer	The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing.
abstract_unstemmed	The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s<sup<-1</sup< was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing.
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title_short	Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)
url	https://doaj.org/article/a51357825b7945d6a8e8c2a2e866854f http://hrcak.srce.hr/file/318746 https://doaj.org/toc/0543-5846 https://doaj.org/toc/1334-2576
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author2	H. C. Ji W. C. Pei X. M. Huang W. D. Li Y. M. Li
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up_date	2024-07-03T13:33:31.338Z
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Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)

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