An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials

Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplas...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Xin [verfasserIn] Li, Ziqi [verfasserIn] Chen, Yang [verfasserIn] Zhang, Chao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Data-driven constitutive model Elastoplastic model Strain reconfiguration Strain rate effect Temperature effect

Übergeordnetes Werk:	Enthalten in: European journal of mechanics / A - Paris : Elsevier, 1998, 100
Übergeordnetes Werk:	volume:100

DOI / URN:	10.1016/j.euromechsol.2023.104996

Katalog-ID:	ELV01038006X

Internformat


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520			\|a Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplastic material through the integration of a data-driven concept with fundamental plasticity theory. A novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This developed data-driven constitutive model is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including five different loading rates, four different temperatures, and thirteen different stress states. The excellent correlation with the experimental results demonstrates the high accuracy and generality of the presented approach, especially its capability for predicting unknown nonlinear stress–strain response. The presented theory reveals the great potential of employing such a data-driven approach in computational mechanics.
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publishDate	2023
allfields	10.1016/j.euromechsol.2023.104996 doi (DE-627)ELV01038006X (ELSEVIER)S0997-7538(23)00088-8 DE-627 ger DE-627 rda eng 530 VZ 50.31 bkl 51.32 bkl 33.11 bkl Li, Xin verfasserin aut An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplastic material through the integration of a data-driven concept with fundamental plasticity theory. A novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This developed data-driven constitutive model is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including five different loading rates, four different temperatures, and thirteen different stress states. The excellent correlation with the experimental results demonstrates the high accuracy and generality of the presented approach, especially its capability for predicting unknown nonlinear stress–strain response. The presented theory reveals the great potential of employing such a data-driven approach in computational mechanics. Data-driven constitutive model Elastoplastic model Strain reconfiguration Strain rate effect Temperature effect Li, Ziqi verfasserin aut Chen, Yang verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of mechanics / A Paris : Elsevier, 1998 100 Online-Ressource (DE-627)320593843 (DE-600)2019284-8 (DE-576)116451807 1873-7285 nnns volume:100 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.31 Technische Mechanik VZ 51.32 Werkstoffmechanik VZ 33.11 Mechanik VZ AR 100
spelling	10.1016/j.euromechsol.2023.104996 doi (DE-627)ELV01038006X (ELSEVIER)S0997-7538(23)00088-8 DE-627 ger DE-627 rda eng 530 VZ 50.31 bkl 51.32 bkl 33.11 bkl Li, Xin verfasserin aut An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplastic material through the integration of a data-driven concept with fundamental plasticity theory. A novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This developed data-driven constitutive model is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including five different loading rates, four different temperatures, and thirteen different stress states. The excellent correlation with the experimental results demonstrates the high accuracy and generality of the presented approach, especially its capability for predicting unknown nonlinear stress–strain response. The presented theory reveals the great potential of employing such a data-driven approach in computational mechanics. Data-driven constitutive model Elastoplastic model Strain reconfiguration Strain rate effect Temperature effect Li, Ziqi verfasserin aut Chen, Yang verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of mechanics / A Paris : Elsevier, 1998 100 Online-Ressource (DE-627)320593843 (DE-600)2019284-8 (DE-576)116451807 1873-7285 nnns volume:100 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.31 Technische Mechanik VZ 51.32 Werkstoffmechanik VZ 33.11 Mechanik VZ AR 100
allfields_unstemmed	10.1016/j.euromechsol.2023.104996 doi (DE-627)ELV01038006X (ELSEVIER)S0997-7538(23)00088-8 DE-627 ger DE-627 rda eng 530 VZ 50.31 bkl 51.32 bkl 33.11 bkl Li, Xin verfasserin aut An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplastic material through the integration of a data-driven concept with fundamental plasticity theory. A novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This developed data-driven constitutive model is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including five different loading rates, four different temperatures, and thirteen different stress states. The excellent correlation with the experimental results demonstrates the high accuracy and generality of the presented approach, especially its capability for predicting unknown nonlinear stress–strain response. The presented theory reveals the great potential of employing such a data-driven approach in computational mechanics. Data-driven constitutive model Elastoplastic model Strain reconfiguration Strain rate effect Temperature effect Li, Ziqi verfasserin aut Chen, Yang verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of mechanics / A Paris : Elsevier, 1998 100 Online-Ressource (DE-627)320593843 (DE-600)2019284-8 (DE-576)116451807 1873-7285 nnns volume:100 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.31 Technische Mechanik VZ 51.32 Werkstoffmechanik VZ 33.11 Mechanik VZ AR 100
allfieldsGer	10.1016/j.euromechsol.2023.104996 doi (DE-627)ELV01038006X (ELSEVIER)S0997-7538(23)00088-8 DE-627 ger DE-627 rda eng 530 VZ 50.31 bkl 51.32 bkl 33.11 bkl Li, Xin verfasserin aut An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplastic material through the integration of a data-driven concept with fundamental plasticity theory. A novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This developed data-driven constitutive model is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including five different loading rates, four different temperatures, and thirteen different stress states. The excellent correlation with the experimental results demonstrates the high accuracy and generality of the presented approach, especially its capability for predicting unknown nonlinear stress–strain response. The presented theory reveals the great potential of employing such a data-driven approach in computational mechanics. Data-driven constitutive model Elastoplastic model Strain reconfiguration Strain rate effect Temperature effect Li, Ziqi verfasserin aut Chen, Yang verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of mechanics / A Paris : Elsevier, 1998 100 Online-Ressource (DE-627)320593843 (DE-600)2019284-8 (DE-576)116451807 1873-7285 nnns volume:100 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.31 Technische Mechanik VZ 51.32 Werkstoffmechanik VZ 33.11 Mechanik VZ AR 100
allfieldsSound	10.1016/j.euromechsol.2023.104996 doi (DE-627)ELV01038006X (ELSEVIER)S0997-7538(23)00088-8 DE-627 ger DE-627 rda eng 530 VZ 50.31 bkl 51.32 bkl 33.11 bkl Li, Xin verfasserin aut An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplastic material through the integration of a data-driven concept with fundamental plasticity theory. A novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This developed data-driven constitutive model is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including five different loading rates, four different temperatures, and thirteen different stress states. The excellent correlation with the experimental results demonstrates the high accuracy and generality of the presented approach, especially its capability for predicting unknown nonlinear stress–strain response. The presented theory reveals the great potential of employing such a data-driven approach in computational mechanics. Data-driven constitutive model Elastoplastic model Strain reconfiguration Strain rate effect Temperature effect Li, Ziqi verfasserin aut Chen, Yang verfasserin aut Zhang, Chao verfasserin aut Enthalten in European journal of mechanics / A Paris : Elsevier, 1998 100 Online-Ressource (DE-627)320593843 (DE-600)2019284-8 (DE-576)116451807 1873-7285 nnns volume:100 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.31 Technische Mechanik VZ 51.32 Werkstoffmechanik VZ 33.11 Mechanik VZ AR 100
language	English
source	Enthalten in European journal of mechanics / A 100 volume:100
sourceStr	Enthalten in European journal of mechanics / A 100 volume:100
format_phy_str_mv	Article
bklname	Technische Mechanik Werkstoffmechanik Mechanik
institution	findex.gbv.de
topic_facet	Data-driven constitutive model Elastoplastic model Strain reconfiguration Strain rate effect Temperature effect
dewey-raw	530
isfreeaccess_bool	false
container_title	European journal of mechanics / A
authorswithroles_txt_mv	Li, Xin @@aut@@ Li, Ziqi @@aut@@ Chen, Yang @@aut@@ Zhang, Chao @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320593843
dewey-sort	3530
id	ELV01038006X
language_de	englisch
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author	Li, Xin
spellingShingle	Li, Xin ddc 530 bkl 50.31 bkl 51.32 bkl 33.11 misc Data-driven constitutive model misc Elastoplastic model misc Strain reconfiguration misc Strain rate effect misc Temperature effect An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials
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topic_title	530 VZ 50.31 bkl 51.32 bkl 33.11 bkl An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials Data-driven constitutive model Elastoplastic model Strain reconfiguration Strain rate effect Temperature effect
topic	ddc 530 bkl 50.31 bkl 51.32 bkl 33.11 misc Data-driven constitutive model misc Elastoplastic model misc Strain reconfiguration misc Strain rate effect misc Temperature effect
topic_unstemmed	ddc 530 bkl 50.31 bkl 51.32 bkl 33.11 misc Data-driven constitutive model misc Elastoplastic model misc Strain reconfiguration misc Strain rate effect misc Temperature effect
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title	An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials
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title_full	An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials
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title_sort	an enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials
title_auth	An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials
abstract	Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplastic material through the integration of a data-driven concept with fundamental plasticity theory. A novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This developed data-driven constitutive model is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including five different loading rates, four different temperatures, and thirteen different stress states. The excellent correlation with the experimental results demonstrates the high accuracy and generality of the presented approach, especially its capability for predicting unknown nonlinear stress–strain response. The presented theory reveals the great potential of employing such a data-driven approach in computational mechanics.
abstractGer	Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplastic material through the integration of a data-driven concept with fundamental plasticity theory. A novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This developed data-driven constitutive model is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including five different loading rates, four different temperatures, and thirteen different stress states. The excellent correlation with the experimental results demonstrates the high accuracy and generality of the presented approach, especially its capability for predicting unknown nonlinear stress–strain response. The presented theory reveals the great potential of employing such a data-driven approach in computational mechanics.
abstract_unstemmed	Data-driven and machine-learning based approaches provide a highly compatible and efficient fundamentals for the mechanical constitutive modeling of engineering materials. In this work, an enhanced data-driven constitutive model is developed to predict the stress–strain relationship of an elastoplastic material through the integration of a data-driven concept with fundamental plasticity theory. A novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This developed data-driven constitutive model is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including five different loading rates, four different temperatures, and thirteen different stress states. The excellent correlation with the experimental results demonstrates the high accuracy and generality of the presented approach, especially its capability for predicting unknown nonlinear stress–strain response. The presented theory reveals the great potential of employing such a data-driven approach in computational mechanics.
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title_short	An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials
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doi_str	10.1016/j.euromechsol.2023.104996
up_date	2024-07-06T17:47:05.745Z
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An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials

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