Aydin, FatihDurgut, RafetMustu, MustafaDemir, Bilge2024-09-292024-09-2920230301-679X1879-2464https://doi.org/10.1016/j.triboint.2022.107945https://hdl.handle.net/20.500.14619/5401In 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.eninfo:eu-repo/semantics/closedAccessZK60CeO 2 compositesWearMachine learningWorn surfaceArticle10.1016/j.triboint.2022.1079452-s2.0-85138808768Q1177WOS:000864999100001Q1