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Data analytics-driven selection of die material in multimaterial co-extrusion of Ti-Mg alloys

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dc.contributor.authorFernández Bermejo, Daniel
dc.contributor.authorRodríguez Prieto, Álvaro
dc.contributor.authorCamacho López, Ana María
dc.date.accessioned2024-06-11T15:15:17Z
dc.date.available2024-06-11T15:15:17Z
dc.date.issued2024-03-10
dc.description.abstractAbstract: Selection of the most suitable material is one of the key decisions to be taken at the design stage of a manufacturing process. Traditional approaches as Ashby maps based on material properties are widely used in the industry. However, in the production of multimaterial components, the criteria for the selection can include antagonistic approaches. The aim of this work is the implementation of a methodology based on the results of process simulations for several materials and classify them by applying an advanced data analytics method based on Machine Learning (ML), in this case the Support Vector Regression (SVR) and Multi-Criteria Decision Making (MCDM) meth- odologies, specifically Multi-criteria Optimization and Compromise Solution (VIKOR) combined with Entropy weighting methods. In order to do this, a Finite Element Model (FEM) has been built to evaluate the extrusion force and the die wear in a multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR combined with Entropy weighting methodologies, a comparison has been established based on the material selection and complexity of the methodology used, resulting that material chosen in both methodologies is very similar and MCDM method is easier to implement because there is no need of evaluate the error of the prediction model and the time for data preprocessing is less than the time needed in SVR. This new methodology is proven to be effective as alternative to the traditional approaches and aligned with the new trends in the industry based on simulation and data analytics.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.3390/math12060813
dc.identifier.issn2227-7390
dc.identifier.urihttps://hdl.handle.net/20.500.14468/22370
dc.journal.issue6
dc.journal.titleMathematics
dc.journal.volume12
dc.language.isoen
dc.publisherMDPI
dc.relation.centerFacultades y escuelas::E.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.subject.keywordsData analytics
dc.subject.keywordsMethodologies
dc.subject.keywordsMulti-material
dc.subject.keywordsCo-extrusion
dc.subject.keywordsFEM
dc.subject.keywordsMachine Learning
dc.subject.keywordsSVR
dc.subject.keywordsMCDM
dc.titleData analytics-driven selection of die material in multimaterial co-extrusion of Ti-Mg alloyses
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublication35a15658-04bc-4665-9a1a-caa49a4d0ffa
relation.isAuthorOfPublicationebbef81e-9b79-4d38-ac0b-2069afa400b8
relation.isAuthorOfPublication45331d02-189c-4439-a246-1a0944b2185a
relation.isAuthorOfPublication.latestForDiscovery35a15658-04bc-4665-9a1a-caa49a4d0ffa
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