Work

Prediction of Polymer Interface Wear Amount Based on Noise Emission and Machine Learning Regression

Year: 2025

Type: article

Abstract: This study explores the complex nonlinear relationship between wear and noise, expanding traditional tribological methods. We conducted friction and wear experiments using two polymers and six metals across a wide temperature range, focusing on the noise generated at the friction interface. The research analyzes the wear mechanisms of polymer–metal tribopairs at low temperatures and establishes a model to clarify the relationship between wear and noise. We employed three machine learning algorithms—Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Extremely Randomized Trees (Extra Trees)—to develop a regression model that correlates noise emission with the amount of wear, enhancing feature selection and model robustness through Kernel Principal Component Analysis (KPCA).

Source: Journal of Tribology

DOI: 10.1115/1.4068603

Authors: Yizhuo Chen, Honghao Zhao, Shaoguang Feng, Wenbo Yang, Haiyang Liu, Fei Guo