Mohammed Abdul Jawad, Mohammed Anwaruddin Siddiqui, Mohammed Abdul Samad
Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Mechanical Engineering Department, Wichita State University, Wichita, KS 67260, USA.
Polymers (Basel). 2023 Oct 11;15(20):4057. doi: 10.3390/polym15204057.
Polymer composites are a class of material that are gaining a lot of attention in demanding tribological applications due to the ability of manipulating their performance by changing various factors, such as processing parameters, types of fillers, and operational parameters. Hence, a number of samples under different conditions need to be repeatedly produced and tested in order to satisfy the requirements of an application. However, with the advent of a new field of triboinformatics, which is a scientific discipline involving computer technology to collect, store, analyze, and evaluate tribological properties, we presently have access to a variety of high-end tools, such as various machine learning (ML) techniques, which can significantly aid in efficiently gauging the polymer's characteristics without the need to invest time and money in a physical experimentation. The development of an accurate model specifically for predicting the properties of the composite would not only cheapen the process of product testing, but also bolster the production rates of a very strong polymer combination. Hence, in the current study, the performance of five different machine learning (ML) techniques is evaluated for accurately predicting the tribological properties of ultrahigh molecular-weight polyethylene (UHMWPE) polymer composites reinforced with silicon carbide (SiC) nanoparticles. Three input parameters, namely, the applied pressure, holding time, and the concentration of SiCs, are considered with the specific wear rate (SWR) and coefficient of friction (COF) as the two output parameters. The five techniques used are support vector machines (SVMs), decision trees (DTs), random forests (RFs), k-nearest neighbors (KNNs), and artificial neural networks (ANNs). Three evaluation statistical metrics, namely, the coefficient of determination (R-value), mean absolute error (MAE), and root mean square error (RMSE), are used to evaluate and compare the performances of the different ML techniques. Based upon the experimental dataset, the SVM technique was observed to yield the lowest error rates-with the RMSE being 2.09 × 10 and MAE being 2 × 10 for COF and for SWR, an RMSE of 2 × 10 and MAE of 1.6 × 10 were obtained-and highest R-values of 0.9999 for COF and 0.9998 for SWR. The observed performance metrics shows the SVM as the most reliable technique in predicting the tribological properties-with an accuracy of 99.99% for COF and 99.98% for SWR-of the polymer composites.
聚合物复合材料是一类在苛刻的摩擦学应用中备受关注的材料，因为它能够通过改变各种因素（如加工参数、填料类型和操作参数）来调控其性能。因此，为了满足应用需求，需要在不同条件下反复制备和测试多个样品。然而，随着摩擦信息学这一涉及计算机技术来收集、存储、分析和评估摩擦学特性的新领域的出现，我们目前可以使用各种高端工具，如各种机器学习（ML）技术，这些技术可以显著帮助我们高效地评估聚合物的特性，而无需在物理实验上投入时间和金钱。开发一个专门用于预测复合材料性能的精确模型，不仅可以降低产品测试成本，还可以提高一种非常坚固的聚合物组合的生产率。因此，在当前的研究中，评估了五种不同的机器学习（ML）技术对准确预测用碳化硅（SiC）纳米颗粒增强的超高分子量聚乙烯（UHMWPE）聚合物复合材料的摩擦学性能的效果。考虑了三个输入参数，即施加压力、保持时间和SiC的浓度，并将比磨损率（SWR）和摩擦系数（COF）作为两个输出参数。所使用的五种技术是支持向量机（SVM）、决策树（DT）、随机森林（RF）、k近邻（KNN）和人工神经网络（ANN）。使用三个评估统计指标，即决定系数（R值）、平均绝对误差（MAE）和均方根误差（RMSE）来评估和比较不同ML技术的性能。基于实验数据集，观察到SVM技术产生的误差率最低——对于COF，RMSE为2.09×10，MAE为2×10；对于SWR，RMSE为2×10，MAE为1.6×10——并且COF的最高R值为0.9999，SWR的最高R值为0.9998。观察到的性能指标表明，SVM是预测聚合物复合材料摩擦学性能最可靠的技术——对于COF的准确率为99.99%，对于SWR的准确率为99.98%。
