Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms

Detalhes bibliográficos
Autor(a) principal: Chaparro, B. M.
Data de Publicação: 2008
Outros Autores: Thuillier, S., Menezes, L. F., Manach, P. Y., Fernandes, J. V.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: https://hdl.handle.net/10316/4183
https://doi.org/10.1016/j.commatsci.2008.03.028
Resumo: This paper presents two procedures for the identification of material parameters, a genetic algorithm and a gradient-based algorithm. These algorithms enable both the yield criterion and the work hardening parameters to be identified. A hybrid algorithm is also used, which is a combination of the former two, in such a way that the result of the genetic algorithm is considered as the initial values for the gradient-based algorithm. The objective of this approach is to improve the performance of the gradient-based algorithm, which is strongly dependent on the initial set of results. The constitutive model used to compare the three different optimization schemes uses the Barlat'91 yield criterion, an isotropic Voce type law and a kinematic Lemaitre and Chaboche law, which is suitable for the case of aluminium alloys. In order to analyse the effectiveness of this optimization procedure, numerical and experimental results for an EN AW-5754 aluminium alloy are compared.
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spelling Material parameters identification: Gradient-based, genetic and hybrid optimization algorithmsPlasticityAnisotropyParameter identificationStampingOptimizationYield criteriaWork hardeningThis paper presents two procedures for the identification of material parameters, a genetic algorithm and a gradient-based algorithm. These algorithms enable both the yield criterion and the work hardening parameters to be identified. A hybrid algorithm is also used, which is a combination of the former two, in such a way that the result of the genetic algorithm is considered as the initial values for the gradient-based algorithm. The objective of this approach is to improve the performance of the gradient-based algorithm, which is strongly dependent on the initial set of results. The constitutive model used to compare the three different optimization schemes uses the Barlat'91 yield criterion, an isotropic Voce type law and a kinematic Lemaitre and Chaboche law, which is suitable for the case of aluminium alloys. In order to analyse the effectiveness of this optimization procedure, numerical and experimental results for an EN AW-5754 aluminium alloy are compared.http://www.sciencedirect.com/science/article/B6TWM-4SJGWMW-1/1/01e8be60ce61e8fc30473d85439fbe392008-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleaplication/PDFhttps://hdl.handle.net/10316/4183https://hdl.handle.net/10316/4183https://doi.org/10.1016/j.commatsci.2008.03.028engComputational Materials Science. In Press, Corrected Proof:Chaparro, B. M.Thuillier, S.Menezes, L. F.Manach, P. Y.Fernandes, J. V.info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2020-11-06T16:48:54Zoai:estudogeral.uc.pt:10316/4183Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T05:19:35.542512Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms
title Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms
spellingShingle Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms
Chaparro, B. M.
Plasticity
Anisotropy
Parameter identification
Stamping
Optimization
Yield criteria
Work hardening
title_short Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms
title_full Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms
title_fullStr Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms
title_full_unstemmed Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms
title_sort Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms
author Chaparro, B. M.
author_facet Chaparro, B. M.
Thuillier, S.
Menezes, L. F.
Manach, P. Y.
Fernandes, J. V.
author_role author
author2 Thuillier, S.
Menezes, L. F.
Manach, P. Y.
Fernandes, J. V.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Chaparro, B. M.
Thuillier, S.
Menezes, L. F.
Manach, P. Y.
Fernandes, J. V.
dc.subject.por.fl_str_mv Plasticity
Anisotropy
Parameter identification
Stamping
Optimization
Yield criteria
Work hardening
topic Plasticity
Anisotropy
Parameter identification
Stamping
Optimization
Yield criteria
Work hardening
description This paper presents two procedures for the identification of material parameters, a genetic algorithm and a gradient-based algorithm. These algorithms enable both the yield criterion and the work hardening parameters to be identified. A hybrid algorithm is also used, which is a combination of the former two, in such a way that the result of the genetic algorithm is considered as the initial values for the gradient-based algorithm. The objective of this approach is to improve the performance of the gradient-based algorithm, which is strongly dependent on the initial set of results. The constitutive model used to compare the three different optimization schemes uses the Barlat'91 yield criterion, an isotropic Voce type law and a kinematic Lemaitre and Chaboche law, which is suitable for the case of aluminium alloys. In order to analyse the effectiveness of this optimization procedure, numerical and experimental results for an EN AW-5754 aluminium alloy are compared.
publishDate 2008
dc.date.none.fl_str_mv 2008-09-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv https://hdl.handle.net/10316/4183
https://hdl.handle.net/10316/4183
https://doi.org/10.1016/j.commatsci.2008.03.028
url https://hdl.handle.net/10316/4183
https://doi.org/10.1016/j.commatsci.2008.03.028
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Computational Materials Science. In Press, Corrected Proof:
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