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Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

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dc.contributor.authorMerayo, David
dc.contributor.authorRodríguez Prieto, Álvaro
dc.contributor.authorCamacho López, Ana María
dc.date.accessioned2024-05-20T11:39:04Z
dc.date.available2024-05-20T11:39:04Z
dc.date.issued2020-10-02
dc.description.abstractIn metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%.en
dc.description.versionversión publicada
dc.identifier.doi10.3390/ma13225227
dc.identifier.issn1996-1944
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12366
dc.journal.issue22
dc.journal.titleMaterials
dc.journal.volume13
dc.language.isoen
dc.publisherMDPI
dc.relation.centerE.T.S. de Ingenieros Industriales
dc.relation.departmentIngeniería de Construcción y Fabricación
dc.rightsAtribución 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.subject.keywordsaluminum
dc.subject.keywordsartificial neural network
dc.subject.keywordschemical composition
dc.subject.keywordsheat treatment
dc.subject.keywordsBrinell hardness
dc.subject.keywordsmaterial properties’ prognosis
dc.subject.keywordsyield strength
dc.subject.keywordsUTS
dc.titlePrediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloyses
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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