Analyzing the tribological and mechanical performance of Al-6061 with rare earth oxides: An experimental analysis

Abstract

This article aims to evaluate the effect of ceria oxide as rare earth oxides (REOs) on the tribological properties of aluminum hybrid composites with varied concentrations of reinforcing elements such as silicon carbide, aluminum oxide, and ceria oxide. In order to accomplish this, composites were produced by varying the percentage of SiC/Al2O3 in the Al-6061 matrix from 2.5 to 7.5 wt% and the quantity of CeO2 from 0.5 to 2.5 wt%. The formation of the intermetallic phase (Al4Ce3) as a result of the integration of cerium oxide into aluminum composites at concentrations between 0.5 and 2.5 wt% results in a wear rate improvement of up to 87.28%. The objective of developing Levenberg-Marquardt algorithm (LMA) neural networks is to forecast how the tribological behavior of hybrid composites would be altered by the addition of REOs based on data acquired from wear testing. The correlation value (R) and mean square error are found to be 0.987 and 4.3424e−10, respectively, which is an indication of good fit for the model with high significance. The findings indicate that the LMA neural network models accurately forecast the tribological properties of REOs–aluminum hybrid composites.