Yan Yingnan, Du Jiliang, Ren Shiwei, Shao Mingchao
College of Information Engineering, Lanzhou Petrochemical University of Vocational Technology, Lanzhou 730060, China.
Zhuhai Fudan Innovation Institution, Guangdong-Macao In-Depth Cooperation Zone in Hengqin, Zhuhai 519000, China.
Polymers (Basel). 2024 Jan 28;16(3):356. doi: 10.3390/polym16030356.
Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology. In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost), to predict the tribological properties of polytetrafluoroethylene (PTFE) composites under high-speed and high-temperature conditions. Firstly, PTFE composites were successfully prepared, and tribological properties under different temperature, speed, and load conditions were studied in order to explore wear mechanisms. Then, the investigation focused on establishing correlations between the friction and wear of PTFE composites by testing these parameters through the prediction of the friction coefficient and wear rate. Importantly, the correlation results illustrated that the friction coefficient and wear rate gradually decreased with the increase in speed, which was also proven by the correlation coefficient. In addition, the GBR model could effectively predict the tribological properties of the PTFE composites. Furthermore, an analysis of relative importance revealed that both load and speed exerted a greater influence on the prediction of the friction coefficient and wear rate.
由于工作条件之间存在复杂的非线性关系，摩擦学性能的预测已成为摩擦学领域的一个难题。在本研究中，我们采用了三种不同的机器学习（ML）模型，即随机森林回归（RFR）、梯度提升回归（GBR）和极端梯度提升（XGBoost），来预测聚四氟乙烯（PTFE）复合材料在高速和高温条件下的摩擦学性能。首先，成功制备了PTFE复合材料，并研究了不同温度、速度和载荷条件下的摩擦学性能，以探索磨损机制。然后，通过预测摩擦系数和磨损率来测试这些参数，重点研究建立PTFE复合材料摩擦与磨损之间的相关性。重要的是，相关结果表明，摩擦系数和磨损率随着速度的增加而逐渐降低，这也得到了相关系数的证实。此外，GBR模型能够有效地预测PTFE复合材料的摩擦学性能。此外，相对重要性分析表明，载荷和速度对摩擦系数和磨损率的预测都有较大影响。
