Integrating Friction Noise for In-Situ Monitoring of Polymer Wear Performance: A Machine Learning Approach in Tribology

Abstract

Abstract Friction and wear between mating surfaces significantly affect the efficiency and performance of mechanical systems. Traditional tribological research relies on post-observation methods, limiting the understanding of dynamic friction behavior. In contrast, in-situ monitoring provides real-time insights into evolving friction dynamics. This study employs machine learning to monitor polymer wear performance through friction noise. The predictive accuracy of various machine learning methods, including Extremely Randomized Trees, Gradient-Boosting Decision Trees, AdaBoost, LightGBM, Deep Forest, and Deep Neural Networks, is compared for wear type classification. Additionally, the LSBoost regression is selected as the optimal method for predicting polymer wear rates across various temperatures. The results underscore the potential of using friction noise and machine learning for real-time wear monitoring, offering valuable insights for tribological system maintenance and failure prediction.