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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorDuo, Aitor
dc.contributor.authorAguirre, Aitor
dc.contributor.otherFernández López, Patricia
dc.contributor.otherAlves, Sofia A.
dc.contributor.otherRogov, Aleksey
dc.contributor.otherYerokhin, Aleksey
dc.contributor.otherQuintana, Iban
dc.date.accessioned2024-10-16T13:24:44Z
dc.date.available2024-10-16T13:24:44Z
dc.date.issued2024
dc.identifier.issn2079-6412en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178050en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6663
dc.description.abstractPEO constitutes a promising surface technology for the development of protective and functional ceramic coatings on lightweight alloys. Despite its interesting advantages, including enhanced wear and corrosion resistances and eco-friendliness, the industrial implementation of PEO technology is limited by its relatively high energy consumption. This study explores the development and optimization of novel PEO processes by means of machine learning (ML) to improve the coating thickness. For this purpose, ML models random forest and XGBoost were employed to predict the thickness of the developed PEO coatings based on the key process variables (frequency, current density, and electrolyte composition). The predictive performance was significantly improved by including the composition of the used electrolyte in the models. Furthermore, Shapley values identified the pulse frequency and the TiO2 concentration in the electrolyte as the most influential variables, with higher values leading to increased coating thickness. The residual analysis revealed a certain heteroscedasticity, which suggests the need for additional samples with high thickness to improve the accuracy of the model. This study reveals the potential of artificial intelligence (AI)-driven optimization in PEO processes, which could pave the way for more efficient and cost-effective industrial applications. The findings achieved further emphasize the significance of integrating interactions between variables, such as frequency and TiO2 concentration, into the design of processing operations.en
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2024 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectplasma electrolytic oxidation (PEO)en
dc.subjectMachine learningen
dc.subjectprediction modelsen
dc.subjectAlloysen
dc.subjectcoatingen
dc.subjectprocess digitalizationen
dc.titleData-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insightsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceCoatingsen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/coatings14080979en
local.contributor.otherinstitutionhttps://ror.org/033vryh36en
local.contributor.otherinstitutionhttps://ror.org/027m9bs27en
local.source.detailsVol. 14. N. 8. N.art. 979, 2024
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en
dc.unesco.tesaurohttp://vocabularies.unesco.org/thesaurus/concept5015en
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oaire.funderNameGobierno Vascoen
oaire.funderIdentifierhttps://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086en
oaire.fundingStreamElkartek 2022en
oaire.awardNumberKK-2022-00109en
oaire.awardTitleSuperficies multifuncionales en la frontera del conocimiento (FRONT22)en
oaire.awardURISin informaciónen
dc.unesco.clasificacionhttp://skos.um.es/unesco6/3312en
dc.unesco.clasificacionhttp://skos.um.es/unesco6/120304en
dc.unesco.clasificacionhttp://skos.um.es/unesco6/330310en


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International