The surface/interface properties of materials affect their tribological behavior significantly, necessitating quantitative research. Here, a hybrid machine learning model named CS-LSBoost (Cuckoo Searching-Least Square Boosting) is proposed to evaluate and predict the tribological properties, considering surface/interface properties as input. The incorporation of the CS algorithm in the proposed model accelerates the modeling procedure and improves accuracy. In contrast to the conventional LSBoost algorithm, the CS-LSBoost algorithm exhibits superior performance that reduces the MAPE (mean average percentage error) on the CV (Cross-Validation) set in the friction task from 15.41% to 12.10% and in the wear task from 14.97% to 10.09%. When predicting the coef. friction and wear rate, the validation MAPEs on the hold-out set were only 6.31% and 9.54%, respectively. The proposed prediction model, with accurate quantitative correlation and appropriate physical interpretability between the tribological performance and the surface and interface properties of materials, can provide guidance for material optimization.
Wang et al. (Sun,) studied this question.