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dc.contributor.authorAnglada Izaguirre, Eva María
dc.contributor.authorMeléndez, Antton
dc.contributor.authorObregón, Alejandro
dc.contributor.authorVillanueva, Ester
dc.contributor.authorGarmendia Azurmendi, Ignacio ORCID
dc.date.accessioned2020-09-18T08:34:47Z
dc.date.available2020-09-18T08:34:47Z
dc.date.issued2020-08-08
dc.identifier.citationMetals 10(8) : (2020) // Article ID 1071es_ES
dc.identifier.issn2075-4701
dc.identifier.urihttp://hdl.handle.net/10810/46145
dc.description.abstractThe use of optimization algorithms to adjust the numerical models with experimental values has been applied in other fields, but the efforts done in metal casting sector are much more limited. The advances in this area may contribute to get metal casting adjusted models in less time improving the confidence in their predictions and contributing to reduce tests at laboratory scale. This work compares the performance of four algorithms (compass search, NEWUOA, genetic algorithm (GA) and particle swarm optimization (PSO)) in the adjustment of the metal casting simulation models. The case study used in the comparison is the multiscale simulation of the hypereutectic ductile iron (SGI) casting solidification. The model fitting criteria is the value of the tensile strength. Four different situations have been studied: model fitting based in 2, 3, 6 and 10 variables. Compass search and PSO have succeeded in reaching the error target in the four cases studied, while NEWUOA and GA have failed in some cases. In the case of the deterministic algorithms, compass search and NEWUOA, the use of a multiple random initial guess has been clearly beneficious.es_ES
dc.description.sponsorshipThis research was funded by the Basque Government under the ELKARTEK Program (ARGIA Project, ELKARTEK KK-2019/00068) and by the HAZITEK Program (CASTMART Project, HAZITEK ZL-2019/00562)es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectmodel fittinges_ES
dc.subjectoptimizationes_ES
dc.subjectFEMes_ES
dc.subjectmetal castinges_ES
dc.subjectSGIes_ES
dc.subjectnumerical simulationes_ES
dc.subjectcompass searches_ES
dc.subjectNEWUOAes_ES
dc.subjectgenetic algorithmes_ES
dc.subjectparticle swarm optimizationes_ES
dc.titlePerformance of Optimization Algorithms in the Model Fitting of the Multi-Scale Numerical Simulation of Ductile Iron Solidificationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2020-08-21T13:49:45Z
dc.rights.holder2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2075-4701/10/8/1071es_ES
dc.identifier.doi10.3390/met10081071
dc.departamentoesIngeniería mecánica
dc.departamentoeuIngeniaritza mekanikoa


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2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).