Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorda Silva, Aneirson Francisco-
Autor(es): dc.creatorMarins, Fernando Augusto Silva-
Autor(es): dc.creatorda Silva Oliveira, Jose Benedito-
Autor(es): dc.creatorDias, Erica Ximenes-
Data de aceite: dc.date.accessioned2025-08-21T20:11:24Z-
Data de disponibilização: dc.date.available2025-08-21T20:11:24Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2021-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00170-021-07644-9-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/233327-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/233327-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Production São Paulo State University, Av. Ariberto Pereira da Cunha, 333, Portal das Colinas-
Descrição: dc.descriptionDepartment of Production São Paulo State University, Av. Ariberto Pereira da Cunha, 333, Portal das Colinas-
Descrição: dc.descriptionCNPq: 302730/2018-4-
Formato: dc.format305-327-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Advanced Manufacturing Technology-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectFinite element method-
Palavras-chave: dc.subjectMulti-objective optimization-
Palavras-chave: dc.subjectOptimization via Monte Carlo simulation-
Palavras-chave: dc.subjectResponse surface methodology-
Palavras-chave: dc.subjectStamping process-
Palavras-chave: dc.subjectUncertainty-
Título: dc.titleMulti-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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