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Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data

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dc.contributor.authorMerayo Fernández, David
dc.contributor.authorRodríguez Prieto, Álvaro
dc.contributor.authorCamacho López, Ana María
dc.date.accessioned2024-05-20T11:39:12Z
dc.date.available2024-05-20T11:39:12Z
dc.date.issued2020-07-06
dc.description.abstractAluminum alloys are among the most widely used materials in demanding industries such as aerospace, automotive or food packaging and, therefore, it is essential to predict the behavior and properties of each component. Tools based on artificial intelligence can be used to face this complex problem. In this work, a computer-aided tool is developed to predict relevant mechanical properties of aluminum alloys—Young’s modulus, yield stress, ultimate tensile strength and elongation at break. These predictions are based on the alloy chemical composition and tempers, and are employed to estimate the bilinear approximation of the stress-strain curve, very useful as a decision tool that helps in the selection of materials. The system is based on the use of artificial neural networks supported by a big data collection about technological characteristics of thousands of commercial materials. Thus, the volume of data exceeds 5𝑘 entries. Once the relevant data have been retrieved, filtered and organized, an artificial neural network is defined and, after the training, the system is able to make predictions about the material properties with an average confidence greater than 95% . Finally, the trained network is employed to show how it can be used to support decisions about engineering applications.en
dc.description.versionversión publicada
dc.identifier.doi10.3390/met10070904
dc.identifier.issn2075-4701
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12369
dc.journal.issue7
dc.journal.titleMetals
dc.journal.volume10
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 alloy
dc.subject.keywordsbig data
dc.subject.keywordsartificial intelligence
dc.subject.keywordsmulti-layer artificial neural network
dc.subject.keywordspython
dc.subject.keywordsstress-strain curve
dc.subject.keywordsmaterial selection
dc.subject.keywordsdecision support system
dc.subject.keywordsmaterial characterization
dc.titlePrediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Dataes
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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