Optimizing the Tribological Behavior of Hybrid Copper Surface Composites Using Statistical and Machine Learning Techniques

Copper-based surface composite dispersed with varying fractions of hybrid reinforcement was fabricated through friction stir processing (FSP). Hybrid reinforcement particles were prepared from aluminum nitride (AIN) and boron nitride (BN) particles of equal weight proportion. Based on design of experiments, wear characteristics of the developed copper surface composites were estimated using pin-on-disk tribometer. Experimental parameters include volumetric fraction of hybrid reinforcement particles (5, 10, and 15 vol %), load (10, 20, 30 N), sliding velocity (1, 1.5, and 2 m/s), and sliding distance (500, 1000, and 1500 m). Microstructural characterization demonstrated uniform dispersion of hybrid reinforcement particles onto the copper surface along with good bonding. Hardness of the developed surface composites increased with respect to increase in hybrid particle dispersion when compared with copper substrate while a reduction in density values was revealed. Analysis on wear rate values proved that wear rate decreased with increase in hybrid particle dispersion and increased with increase in load, sliding velocity, and distance. Analysis of variance (ANOVA) specified load as the most significant factor over wear rate values followed by volume fractions of particle dispersion, sliding velocity, and distance. Regression model constructed was found efficient in predicting wear rate values. Analysis of worn out surfaces through scanning electron microscopy (SEM) revealed the transition of severe to mild wear with respect to increase in hybrid reinforcement particle dispersion. A feed forward back propagation algorithm-based artificial neural network (ANN) model with topology 4-7-1 was developed to predict wear rate of copper surface composites based on its control factors.