Prediction of Tribological Behavior of Acrylonitrile Butadiene Styrene Polymer Matrix Composites Employing Copper Powders

This research examines the impact of different amounts of copper (Cu) powder on the wear characteristics of acrylonitrile butadiene styrene (ABS)–Cu composites. Various formulations of ABS–Cu composites have been produced using injection molding, with different amounts of surfactant. Wear properties were evaluated by conducting tribological testing in accordance with ASTM standards. The findings indicated a decrease in wear loss, particularly when using a mixture consisting of 23% ABS, 70% Cu, and 7% surfactant. Machine learning regression algorithms successfully forecasted wear behavior with R-squared values over 0.97. The models used in the analysis included linear, stepwise linear, tree, support vector machine (SVM), efficient linear, Gaussian progression, ensemble, and neural network regression models. This research emphasizes the significance of composite materials in fulfilling contemporary technical requirements. The acquired insights enable the development of materials with customized wear characteristics. These findings have important consequences for a range of industrial applications.