Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s00170-021-07644-9 http://hdl.handle.net/11449/233327 |
Resumo: | The response surface methodology (RSM), which uses a quadratic empirical function as an approximation to the original function and allows the identification of relationships between independent variables xi and dependent variables ys associated with multiple responses, stands out. The main contribution of the present study is to propose an innovative procedure for the optimization of experimental problems with multiple responses, which considers the insertion of uncertainties in the coefficients of the obtained empirical functions in order to adequately represent real situations. This new procedure, which combines RSM with the finite element (FE) method and the Monte Carlo simulation optimization (OvMCS), was applied to a real stamping process of a Brazilian multinational automotive company. For RSM with multiple responses, were compared the results obtained using the agglutination methods: compromise programming, desirability function (DF), and the modified desirability function (MDF). The functions were optimized by applying the generalized reduced gradient (GRG) algorithm, which is a classic procedure widely adopted in this type of experimental problem, without the uncertainty in the coefficients of independent factors. The advantages offered by this innovative procedure are presented and discussed, as well as the statistical validation of its results. It can be highlighted, for example, that the proposed procedure reduces, and sometimes eliminates, the need for additional confirmation experiments, as well as a better adjustment of factor values and response variable values when comparing to the results of RSM with classic multiple responses. The new proposed procedure added relevant and useful information to the managers responsible for the studied stamping process. Moreover, the proposed procedure facilitates the improvement of the process, with lower associated costs. |
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Repositório Institucional da UNESP |
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Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertaintyFinite element methodMulti-objective optimizationOptimization via Monte Carlo simulationResponse surface methodologyStamping processUncertaintyThe response surface methodology (RSM), which uses a quadratic empirical function as an approximation to the original function and allows the identification of relationships between independent variables xi and dependent variables ys associated with multiple responses, stands out. The main contribution of the present study is to propose an innovative procedure for the optimization of experimental problems with multiple responses, which considers the insertion of uncertainties in the coefficients of the obtained empirical functions in order to adequately represent real situations. This new procedure, which combines RSM with the finite element (FE) method and the Monte Carlo simulation optimization (OvMCS), was applied to a real stamping process of a Brazilian multinational automotive company. For RSM with multiple responses, were compared the results obtained using the agglutination methods: compromise programming, desirability function (DF), and the modified desirability function (MDF). The functions were optimized by applying the generalized reduced gradient (GRG) algorithm, which is a classic procedure widely adopted in this type of experimental problem, without the uncertainty in the coefficients of independent factors. The advantages offered by this innovative procedure are presented and discussed, as well as the statistical validation of its results. It can be highlighted, for example, that the proposed procedure reduces, and sometimes eliminates, the need for additional confirmation experiments, as well as a better adjustment of factor values and response variable values when comparing to the results of RSM with classic multiple responses. The new proposed procedure added relevant and useful information to the managers responsible for the studied stamping process. Moreover, the proposed procedure facilitates the improvement of the process, with lower associated costs.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Production São Paulo State University, Av. Ariberto Pereira da Cunha, 333, Portal das ColinasDepartment of Production São Paulo State University, Av. Ariberto Pereira da Cunha, 333, Portal das ColinasCNPq: 302730/2018-4Universidade Estadual Paulista (UNESP)da Silva, Aneirson Francisco [UNESP]Marins, Fernando Augusto Silva [UNESP]da Silva Oliveira, Jose Benedito [UNESP]Dias, Erica Ximenes [UNESP]2022-05-01T07:58:44Z2022-05-01T07:58:44Z2021-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article305-327http://dx.doi.org/10.1007/s00170-021-07644-9International Journal of Advanced Manufacturing Technology, v. 117, n. 1-2, p. 305-327, 2021.1433-30150268-3768http://hdl.handle.net/11449/23332710.1007/s00170-021-07644-92-s2.0-85111529588Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Advanced Manufacturing Technologyinfo:eu-repo/semantics/openAccess2024-07-02T17:37:08Zoai:repositorio.unesp.br:11449/233327Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:54:36.584534Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty |
title |
Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty |
spellingShingle |
Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty da Silva, Aneirson Francisco [UNESP] Finite element method Multi-objective optimization Optimization via Monte Carlo simulation Response surface methodology Stamping process Uncertainty |
title_short |
Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty |
title_full |
Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty |
title_fullStr |
Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty |
title_full_unstemmed |
Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty |
title_sort |
Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty |
author |
da Silva, Aneirson Francisco [UNESP] |
author_facet |
da Silva, Aneirson Francisco [UNESP] Marins, Fernando Augusto Silva [UNESP] da Silva Oliveira, Jose Benedito [UNESP] Dias, Erica Ximenes [UNESP] |
author_role |
author |
author2 |
Marins, Fernando Augusto Silva [UNESP] da Silva Oliveira, Jose Benedito [UNESP] Dias, Erica Ximenes [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
da Silva, Aneirson Francisco [UNESP] Marins, Fernando Augusto Silva [UNESP] da Silva Oliveira, Jose Benedito [UNESP] Dias, Erica Ximenes [UNESP] |
dc.subject.por.fl_str_mv |
Finite element method Multi-objective optimization Optimization via Monte Carlo simulation Response surface methodology Stamping process Uncertainty |
topic |
Finite element method Multi-objective optimization Optimization via Monte Carlo simulation Response surface methodology Stamping process Uncertainty |
description |
The response surface methodology (RSM), which uses a quadratic empirical function as an approximation to the original function and allows the identification of relationships between independent variables xi and dependent variables ys associated with multiple responses, stands out. The main contribution of the present study is to propose an innovative procedure for the optimization of experimental problems with multiple responses, which considers the insertion of uncertainties in the coefficients of the obtained empirical functions in order to adequately represent real situations. This new procedure, which combines RSM with the finite element (FE) method and the Monte Carlo simulation optimization (OvMCS), was applied to a real stamping process of a Brazilian multinational automotive company. For RSM with multiple responses, were compared the results obtained using the agglutination methods: compromise programming, desirability function (DF), and the modified desirability function (MDF). The functions were optimized by applying the generalized reduced gradient (GRG) algorithm, which is a classic procedure widely adopted in this type of experimental problem, without the uncertainty in the coefficients of independent factors. The advantages offered by this innovative procedure are presented and discussed, as well as the statistical validation of its results. It can be highlighted, for example, that the proposed procedure reduces, and sometimes eliminates, the need for additional confirmation experiments, as well as a better adjustment of factor values and response variable values when comparing to the results of RSM with classic multiple responses. The new proposed procedure added relevant and useful information to the managers responsible for the studied stamping process. Moreover, the proposed procedure facilitates the improvement of the process, with lower associated costs. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-01 2022-05-01T07:58:44Z 2022-05-01T07:58:44Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s00170-021-07644-9 International Journal of Advanced Manufacturing Technology, v. 117, n. 1-2, p. 305-327, 2021. 1433-3015 0268-3768 http://hdl.handle.net/11449/233327 10.1007/s00170-021-07644-9 2-s2.0-85111529588 |
url |
http://dx.doi.org/10.1007/s00170-021-07644-9 http://hdl.handle.net/11449/233327 |
identifier_str_mv |
International Journal of Advanced Manufacturing Technology, v. 117, n. 1-2, p. 305-327, 2021. 1433-3015 0268-3768 10.1007/s00170-021-07644-9 2-s2.0-85111529588 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal of Advanced Manufacturing Technology |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
305-327 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129138427428864 |