Affiliation:
1. School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai 201418, China
Abstract
In this work, ball-on-disk wear experiments were carried out on different wear parameters such as sliding speed, sliding distance, normal load, temperature, and oil film thickness. In total, 81 different sets of wear depth data were obtained. Four different machine learning (ML) algorithms, namely Random Forest (RF), K-neighborhood (KNN), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) were applied to predict wear depth. By analyzing the performance of several ML algorithms, it is demonstrated that ball bearing wear depth can be estimated by ML models by inputting different parameter variables. A comparative analysis of the performance of the different models revealed that XGB was more accurate than the other ML models at anticipating wear depth. Further analysis of the attribute of feature importance and correlation heatmap of the Pearson correlation reveals that each input feature has an effect on wear.
Funder
National Natural Science Foundation of China
Industrial Collaborative Innovation Project of Shanghai
Leading Talents Program of Shanghai
Natural Science Foundation Project of Shanghai
Foundation of Science and Technology Commission of Shanghai Municipality
Guangdong Basic and Applied Basic Research Foundation
Project of Department of Education of Guangdong Province
Subject
Surfaces, Coatings and Films,Mechanical Engineering