Analysis of the Frictional Properties of Carbon Nanotube-Coated Aramid Fiber-Reinforced Epoxy Composites Using Machine Learning Techniques

Abstract

This study examines the effects of mechanical behavior, thermal characteristics, and tribological variables (sliding frequency, normal load, and temperature) on the tribological performance of carbon nanotube (CNT)-coated aramid fabric-reinforced epoxy composites using a computational and data-driven machine learning (ML) approach. Predictive models for the coefficient of friction (COF) were developed based on previous tribological, mechanical, and thermal data, employing three ML algorithms: artificial neural network (ANN), gradient boosting machine (GBM), and random forest (RF). The models showed the following results—ANN: R2 = 0.9088, GBM: R2 = 0.92807, and RF: R2 = 0.85294, with the GBM model providing the best predictions. The dataset with the best performance had an error percentage of 0.003658%, while the poorest performance showed 13.56625%. Feature score analysis highlighted load, sliding frequency, and CNT content as key factors influencing COF. This data-driven ML analysis offers significant insights into the tribological behavior of fiber-reinforced polymer composites, aiding in material design and performance optimization.