Application of symbolic regression for constitutive modeling of plastic deformation

In numerical process simulations, in-depth knowledge about material behavior during processing in the form of trustworthy material models is crucial. Among the different constitutive models used in the literature one can distinguish a physics-based approach (white-box model), which considers the evo...

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Autores principales:	Evgeniya Kabliman, Ana Helena Kolody, Johannes Kronsteiner, Michael Kommenda, Gabriel Kronberger
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Material constitutive equations Machine learning Symbolic regression Data-driven modelling Physics-based modelling Finite element analysis Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/b74db3cc9c924642a5f9f34beb989d4e
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id	oai:doaj.org-article:b74db3cc9c924642a5f9f34beb989d4e
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spelling	oai:doaj.org-article:b74db3cc9c924642a5f9f34beb989d4e2021-12-01T05:06:12ZApplication of symbolic regression for constitutive modeling of plastic deformation2666-496810.1016/j.apples.2021.100052https://doaj.org/article/b74db3cc9c924642a5f9f34beb989d4e2021-06-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666496821000182https://doaj.org/toc/2666-4968In numerical process simulations, in-depth knowledge about material behavior during processing in the form of trustworthy material models is crucial. Among the different constitutive models used in the literature one can distinguish a physics-based approach (white-box model), which considers the evolution of material internal state variables, such as mean dislocation density, and data-driven models (grey or even black-box). Typically, parameters in physics-based models such as physical constants or material parameters, are interpretable and have a physical meaning. However, even physics-based models often contain calibration coefficients that are fitted to experimental data. In the present work, we investigate the applicability of symbolic regression for (1) predicting calibration coefficients of a physics-based model and (2) for deriving a constitutive model directly from measurement data. Our goal is to find mathematical expressions, which can be integrated into numerical simulation models. For this purpose, we have chosen symbolic regression to derive the constitutive equations based on data from compression testing with varying process parameters. To validate the derived constitutive models, we have implemented them into a FE solver (herein, LS-DYNA®), and calculated the force-displacement curves. The comparison with experiments shows a reasonable agreement for both data-driven and physics-based (with fitted and learned calibration parameters) models.Evgeniya KablimanAna Helena KolodyJohannes KronsteinerMichael KommendaGabriel KronbergerElsevierarticleMaterial constitutive equationsMachine learningSymbolic regressionData-driven modellingPhysics-based modellingFinite element analysisEngineering (General). Civil engineering (General)TA1-2040ENApplications in Engineering Science, Vol 6, Iss , Pp 100052- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Material constitutive equations Machine learning Symbolic regression Data-driven modelling Physics-based modelling Finite element analysis Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Material constitutive equations Machine learning Symbolic regression Data-driven modelling Physics-based modelling Finite element analysis Engineering (General). Civil engineering (General) TA1-2040 Evgeniya Kabliman Ana Helena Kolody Johannes Kronsteiner Michael Kommenda Gabriel Kronberger Application of symbolic regression for constitutive modeling of plastic deformation
description	In numerical process simulations, in-depth knowledge about material behavior during processing in the form of trustworthy material models is crucial. Among the different constitutive models used in the literature one can distinguish a physics-based approach (white-box model), which considers the evolution of material internal state variables, such as mean dislocation density, and data-driven models (grey or even black-box). Typically, parameters in physics-based models such as physical constants or material parameters, are interpretable and have a physical meaning. However, even physics-based models often contain calibration coefficients that are fitted to experimental data. In the present work, we investigate the applicability of symbolic regression for (1) predicting calibration coefficients of a physics-based model and (2) for deriving a constitutive model directly from measurement data. Our goal is to find mathematical expressions, which can be integrated into numerical simulation models. For this purpose, we have chosen symbolic regression to derive the constitutive equations based on data from compression testing with varying process parameters. To validate the derived constitutive models, we have implemented them into a FE solver (herein, LS-DYNA®), and calculated the force-displacement curves. The comparison with experiments shows a reasonable agreement for both data-driven and physics-based (with fitted and learned calibration parameters) models.
format	article
author	Evgeniya Kabliman Ana Helena Kolody Johannes Kronsteiner Michael Kommenda Gabriel Kronberger
author_facet	Evgeniya Kabliman Ana Helena Kolody Johannes Kronsteiner Michael Kommenda Gabriel Kronberger
author_sort	Evgeniya Kabliman
title	Application of symbolic regression for constitutive modeling of plastic deformation
title_short	Application of symbolic regression for constitutive modeling of plastic deformation
title_full	Application of symbolic regression for constitutive modeling of plastic deformation
title_fullStr	Application of symbolic regression for constitutive modeling of plastic deformation
title_full_unstemmed	Application of symbolic regression for constitutive modeling of plastic deformation
title_sort	application of symbolic regression for constitutive modeling of plastic deformation
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/b74db3cc9c924642a5f9f34beb989d4e
work_keys_str_mv	AT evgeniyakabliman applicationofsymbolicregressionforconstitutivemodelingofplasticdeformation AT anahelenakolody applicationofsymbolicregressionforconstitutivemodelingofplasticdeformation AT johanneskronsteiner applicationofsymbolicregressionforconstitutivemodelingofplasticdeformation AT michaelkommenda applicationofsymbolicregressionforconstitutivemodelingofplasticdeformation AT gabrielkronberger applicationofsymbolicregressionforconstitutivemodelingofplasticdeformation
_version_	1718405566700716032

Application of symbolic regression for constitutive modeling of plastic deformation

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