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Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning

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dc.contributor.authorMerayo, David
dc.contributor.authorRodríguez Prieto, Álvaro
dc.contributor.authorCamacho López, Ana María
dc.date.accessioned2025-11-04T11:27:22Z
dc.date.available2025-11-04T11:27:22Z
dc.date.issued2021-08-16
dc.descriptionThe registered version of this article, first published in “Metals, 11, 2021", is available online at the publisher's website: MDPI, https://doi.org/10.3390/met11081289
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en “Metals, 11, 2021", está disponible en línea en el sitio web del editor: MDPI, https://doi.org/10.3390/met11081289
dc.description.abstractThe ability of a metal to be subjected to forming processes depends mainly on its plastic behavior and, thus, the mechanical properties belonging to this region of the stress–strain curve. Forming techniques are among the most widespread metalworking procedures in manufacturing, and aluminum alloys are of great interest in fields as diverse as the aerospace sector or the food industry. A precise characterization of the mechanical properties is crucial to estimate the forming capability of equipment, but also for a robust numerical modeling of metal forming processes. Characterizing a material is a very relevant task in which large amounts of resources are invested, and this paper studies how to optimize a multilayer neural network to be able to make, through machine learning, precise and accurate predictions about the mechanical properties of wrought aluminum alloys. This study focuses on the determination of the ultimate tensile strength, closely related to the strain hardening of a material; more precisely, a methodology is developed that, by randomly partitioning the input dataset, performs training and prediction cycles that allow estimating the average performance of each fully-connected topology. In this way, trends are found in the behavior of the networks, and it is established that, for networks with at least 150 perceptrons in their hidden layers, the average predictive error stabilizes below 4%. Beyond this point, no really significant improvements are found, although there is an increase in computational requirements.en
dc.description.provenanceMade available in DSpace on 2025-11-04T11:27:22Z (GMT). No. of bitstreams: 1 Camacho_Lopez_Ana_Maria_Topological_Optimizat_ANA MARIA CAMACHO LO.pdf: 729283 bytes, checksum: 4ca4b284914ba96df7a729e1a419deaf (MD5) Previous issue date: 2021-08-16en
dc.description.versionversión publicada
dc.identifier.citationMerayo, D., Rodríguez-Prieto, A., & Camacho, A. M. (2021). Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning. Metals, 11(8), 1289. https://doi.org/10.3390/met11081289
dc.identifier.doihttps://doi.org/10.3390/met11081289
dc.identifier.issn2075-4701
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30735
dc.journal.issue8
dc.journal.titleMetals
dc.journal.volume11
dc.language.isoen
dc.page.initial1289
dc.publisherMDPI
dc.relation.centerE.T.S. de Ingenieros Industriales
dc.relation.departmentIngeniería de Construcción y Fabricación
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject3305 Tecnología de la construcción
dc.subject.keywordsaluminum alloyen
dc.subject.keywordsartificial neural networken
dc.subject.keywordsmechanical propertyen
dc.subject.keywordsUTSen
dc.subject.keywordsmachine learningen
dc.subject.keywordstopological optimizationen
dc.subject.keywordsmetal formingen
dc.titleTopological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learningen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationebbef81e-9b79-4d38-ac0b-2069afa400b8
relation.isAuthorOfPublication45331d02-189c-4439-a246-1a0944b2185a
relation.isAuthorOfPublication.latestForDiscoveryebbef81e-9b79-4d38-ac0b-2069afa400b8
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