Deshpande Abhijeet R, Kulkarni Atul P, Wasatkar Namrata, Gajalkar Vaibhav, Abdullah Masuk
Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune 411046, India.
Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune 411046, India.
Polymers (Basel). 2024 Sep 22;16(18):2666. doi: 10.3390/polym16182666.
Wear is induced when two surfaces are in relative motion. The wear phenomenon is mostly data-driven and affected by various parameters such as load, sliding velocity, sliding distance, interface temperature, surface roughness, etc. Hence, it is difficult to predict the wear rate of interacting surfaces from fundamental physics principles. The machine learning (ML) approach has not only made it possible to establish the relation between the operating parameters and wear but also helps in predicting the behavior of the material in polymer tribological applications. In this study, an attempt is made to apply different machine learning algorithms to the experimental data for the prediction of the specific wear rate of glass-filled PTFE (Polytetrafluoroethylene) composite. Orthogonal array L25 is used for experimentation for evaluating the specific wear rate of glass-filled PTFE with variations in the operating parameters such as applied load, sliding velocity, and sliding distance. The experimental data are analysed using ML algorithms such as linear regression (LR), gradient boosting (GB), and random forest (RF). The R value is obtained as 0.91, 0.97, and 0.94 for LR, GB, and RF, respectively. The R value of the GB model is the highest among the models, close to 1.0, indicating an almost perfect fit on the experimental data. Pearson's correlation analysis reveals that load and sliding distance have a considerable impact on specific wear rate as compared to sliding velocity.
当两个表面处于相对运动时就会产生磨损。磨损现象大多由数据驱动，并受诸如载荷、滑动速度、滑动距离、界面温度、表面粗糙度等各种参数的影响。因此，很难从基本物理原理预测相互作用表面的磨损率。机器学习（ML）方法不仅使得建立操作参数与磨损之间的关系成为可能，而且有助于预测聚合物摩擦学应用中材料的行为。在本研究中，尝试将不同的机器学习算法应用于实验数据，以预测玻璃填充聚四氟乙烯（PTFE）复合材料的比磨损率。正交阵列L25用于实验，以评估玻璃填充PTFE在诸如施加的载荷、滑动速度和滑动距离等操作参数变化时的比磨损率。使用线性回归（LR）、梯度提升（GB）和随机森林（RF）等机器学习算法对实验数据进行分析。LR、GB和RF的R值分别为0.91、0.97和0.94。GB模型的R值在这些模型中最高，接近1.0，表明与实验数据几乎完美拟合。Pearson相关性分析表明，与滑动速度相比，载荷和滑动距离对比磨损率有相当大的影响。
