Publicación:
Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks

dc.contributor.authorMerayo, David
dc.contributor.authorRodríguez Prieto, Álvaro
dc.contributor.authorCamacho López, Ana María
dc.date.accessioned2024-05-20T11:38:57Z
dc.date.available2024-05-20T11:38:57Z
dc.date.issued2020-01-10
dc.description.abstractIn this work, a computer-aided tool is developed to predict relevant physical and mechanical properties that are involved in the selection tasks of metallic materials. The system is based on the use of artificial neural networks supported by big data collection of information about the technological characteristics of thousands of materials. Thus, the volume of data exceeds 43k. The system can access an open online material library (a website where material data are recorded), download the required information, read it, filter it, organise it and move on to the step based on artificial intelligence. An artificial neural network (ANN) is built with thousands of perceptrons, whose topology and connections have been optimised to accelerate the training and predictive capacity of the ANN. After the corresponding training, the system is able to make predictions about the material density and Young's modulus with average confidences greater than 99% and 98%, respectively.en
dc.description.versionversión publicada
dc.identifier.doihttp://doi.org/10.1109/ACCESS.2020.2965769
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12363
dc.journal.titleIEEE Access
dc.journal.volume8
dc.language.isoen
dc.publisherIEEE
dc.relation.centerE.T.S. de Ingenieros Industriales
dc.relation.departmentIngeniería de Construcción y Fabricación
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.titlePrediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networkses
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
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relation.isAuthorOfPublication45331d02-189c-4439-a246-1a0944b2185a
relation.isAuthorOfPublication.latestForDiscoveryebbef81e-9b79-4d38-ac0b-2069afa400b8
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