Estimation of wear performance of AZ91 alloy under dry sliding conditions using machine learning methods

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dc.contributor.authorAydin, Fatih
dc.contributor.authorDurgut, Rafet
dc.date.accessioned2024-09-29T16:00:57Z
dc.date.available2024-09-29T16:00:57Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe wear behavior of AZ91 alloy was investigated by considering different parameters, such as load (10-50 N), sliding speed (160-220 mm/s) and sliding distance (250-1000 m). It was found that wear volume loss increased as load increased for all sliding distances and some sliding speeds. For sliding speed of 220 mm/s and sliding distance of 1000 m, the wear volume losses under loads of 10, 20, 30, 40 and 50 N were calculated to be 15.0, 19.0, 24.3, 33.9 and 37.4 mm(3), respectively. Worn surfaces show that abrasion and oxidation were present at a load of 10 N, which changes into delamination at a load of 50 N. ANOVA results show that the contributions of load, sliding distance and sliding speed were 12.99%, 83.04% and 3.97%, respectively. The artificial neural networks (ANN), support vector regressor (SVR) and random forest (RF) methods were applied for the prediction of wear volume loss of AZ91 alloy. The correlation coefficient (R-2) values of SVR, RF and ANN for the test were 0.9245, 0.9800 and 0.9845, respectively. Thus, the ANN model has promising results for the prediction of wear performance of AZ91 alloy.en_US
dc.identifier.doi10.1016/S1003-6326(20)65482-6
dc.identifier.endpage137en_US
dc.identifier.issn1003-6326
dc.identifier.issn2210-3384
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85100693962en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage125en_US
dc.identifier.urihttps://doi.org/10.1016/S1003-6326(20)65482-6
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5449
dc.identifier.volume31en_US
dc.identifier.wosWOS:000630205900009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofTransactions of Nonferrous Metals Society of Chinaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAZ91 alloyen_US
dc.subjectwear performanceen_US
dc.subjectartificial neural networksen_US
dc.subjectsupport vector regressoren_US
dc.subjectrandom forest methoden_US
dc.titleEstimation of wear performance of AZ91 alloy under dry sliding conditions using machine learning methodsen_US
dc.typeArticleen_US

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