Application of Desirability-Based Multiobjective Optimization Techniques to Study the Wear of Al Self-Lubricating Hybrid Nanocomposites

Abstract

In this study, novel Al6061–SiC nanocomposites and Al6061–SiC–Gr hybrid nanocomposites were fabricated by ultrasonic cavitation method by adding silicon carbide (SiC) of 0.8 and 1.6% and graphite (Gr) of 0.5 and 1.0% by weight basis for each casting. A Three-level Box–Behnken design of experiment was developed using response surface methodology. Dry sliding wear tests were performed as per the experimental design using a pin-on disc set-up at room temperature. Analysis of variance (ANOVA) was applied to investigate the influence of process parameters viz., load, sliding distance, wt% reinforcement and their interactions on specific wear rate and coefficient of friction. Further, a mathematical model was formulated by applying response surface method in order to estimate the tribology characteristics such as wear and COF of the hybrid nanocomposites. The specific wear rate and coefficient of friction were significantly influenced by % of SiC followed by % of Gr, load and sliding distance. The wear test parameters were optimized for minimizing specific wear rate and COF using desirability function approach. A set of optimum parameter of combination for AMMNC was identified as: SiC 1.36wt%; Gr 0.63 wt%; load 35.65 N and sliding distance 2848 m with specific wear rate of 0.517 g/N-m; coefficient of friction 0.181. The AFM image of Al6061–1.36SiC–0.63Gr hybrid nanocomposite at optimized condition confirmed the improvement in the wear surface smoothness of the hybrid nanocomposite compared to Al6061–SiC nanocomposites.