EXPERIMENTAL INVESTIGATION OF WEAR PERFORMANCE OF Al7075/ZrO2/Gr HYBRID METAL MATRIX COMPOSITES USING TOPSIS AND ANN

This study presents the effects of zirconium dioxide (ZrO2) with 50 nm and graphite (Gr) of 40 µm on the mechanical and tribological behaviour of an aluminium-based metal matrix composite (Al7075) made by stir casting. The tribological behaviour of wear and the frictional qualities of metal matrix composites (MMCs) are further investigated by performing dry sliding wear tests utilising the pin-on-disc method. The TOPSIS Approach was employed to optimise wear parameters such as wear rate (WR) and coefficient of friction (COF). The findings of the analysis of variance demonstrated that the load and incorporation of nanoparticles had a greater influence on WR and COF, respectively. The addition of ZrO2 results in a significant improvement in wear resistance rate, and COF is significantly reduced with reinforcement of higher wt% ZrO2. Artificial neural network (ANN) optimisation is performed to check the TOPSIS outcomes and found that the optimal level and its outputs are P1V1D1M1, with WR is 0.00142 mm3/m and COF is 0.182.