Chen, Yizhuo;
Zhao, Honghao;
Feng, Shaoguang;
Yang, Wenbo;
Liu, Haiyang;
Guo, Fei;
(2026)
Prediction of Polymer Interface Wear Amount Based on Noise Emission and Machine Learning Regression.
Journal of Tribology
, 148
(1)
, Article 011703. 10.1115/1.4068603.
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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).
| Type: | Article |
|---|---|
| Title: | Prediction of Polymer Interface Wear Amount Based on Noise Emission and Machine Learning Regression |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1115/1.4068603 |
| Publisher version: | https://doi.org/10.1115/1.4068603 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10214896 |
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