Prediction of wear performance of ZK60/CeO2 composites using machine learning models

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dc.authoriddemir, BILGE/0000-0002-3617-9749
dc.contributor.authorAydin, Fatih
dc.contributor.authorDurgut, Rafet
dc.contributor.authorMustu, Mustafa
dc.contributor.authorDemir, Bilge
dc.date.accessioned2024-09-29T16:00:51Z
dc.date.available2024-09-29T16:00:51Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn this study, ZK60 magnesium matrix composites were produced with different content of CeO2 (0.25, 0.5 and 1 wt%) by hot pressing. The wear behaviour of the samples was investigated under loads of 5 N, 10 N, 20 N and 30 N, at sliding speeds of 75 mm/s, 110 mm/s and 145 mm/s. The worn surfaces, wear debris, and counterface material was analysed to reveal the wear mechanisms. Five machine learning algorithms were established to compare their prediction abilities of wear behaviour on a limited dataset measured under different test operations. The hyperparameter tuning phase of each model was conducted to provide a fair comparison. The prediction results were examined under various statistical measures. In the light of prediction results, the superior model was determined.en_US
dc.identifier.doi10.1016/j.triboint.2022.107945
dc.identifier.issn0301-679X
dc.identifier.issn1879-2464
dc.identifier.scopus2-s2.0-85138808768en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.triboint.2022.107945
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5401
dc.identifier.volume177en_US
dc.identifier.wosWOS:000864999100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofTribology Internationalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectZK60en_US
dc.subjectCeO 2 compositesen_US
dc.subjectWearen_US
dc.subjectMachine learningen_US
dc.subjectWorn surfaceen_US
dc.titlePrediction of wear performance of ZK60/CeO2 composites using machine learning modelsen_US
dc.typeArticleen_US

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