Predicting Wear Performance of Al6063 Hybrid Composites Reinforced With Multi-Ceramic Particles Using Experimental and ANFIS Approaches

Abstract

Aluminum 6063 matrix composites are widely employed in wear-resistant applications due to their high specific strength, lightweight nature, and excellent corrosion resistance. This study conducted a wear analysis on Al6063 composites reinforced with varying concentrations of titanium carbide (TiC), silicon nitride (Si₃N₄), and zinc oxide (ZnO) using a pin-on-disc apparatus. The investigation focused on four key input variables: applied load, sliding velocity, sliding distance, and a combined reinforcement composition (R) of TiC + ZnO + Si₃N₄. Wear performance was evaluated using two indicators—specific wear rate (SWR) and coefficient of friction (COF). The minimum SWR observed was 4.55 mm³/Nm under optimized conditions: 60 N load, 2 m/s sliding velocity, 1000 m sliding distance, and 4.5 wt% reinforcement. The lowest COF, 0.276, was achieved at a 60 N load, 4 m/s velocity, 2000 m distance, and 1.5 wt% reinforcement. The reduction in wear rate is attributed to the synergistic effect of the reinforcements, which enhance load-bearing capacity and abrasion resistance due to their hardness and thermal stability. Increased reinforcement content led to notable reductions in both SWR and COF, whereas higher loads tended to increase both responses. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed to predict output responses based on the input parameters.