Prediction of wear performance of ZK60/CeO2 composites using machine learning models
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
Anahtar Kelimeler
ZK60, CeO 2 composites, Wear, Machine learning, Worn surface
Kaynak
Tribology International
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
177
