Experimental investigation and predictive modeling of wear in Al7075–SiC nanocomposites

This study examines the dry sliding wear behavior of Al7075-3 wt% SiC nanocomposites using a pin-on-disc tribometer and ASTM G99 standards. Experimental results indicate that tribological responses are strongly affected by sliding distance, applied load, and sliding speed. When the sliding distance increased from 500 m to 1500 m, the wear rate increased gradually due to the longer abrasive interaction. Higher loads (10–30 N) enhance the micro-cutting and surface damage, which causes an increase in the wear rate and surface roughness. The sliding speed also influences the thermal softening of the matrix, which leads to higher wear at higher speeds. Hardness decreases slightly with increasing load and distance, indicating localized plastic deformation and reinforcement–matrix debonding. The ANOVA results indicate that the applied load is the most significant factor controlling the wear rate, followed by the sliding distance and sliding speed. Based on experimental trends and statistical analysis, the optimal parameter combination for minimum wear rate, lowest surface roughness and maximum hardness is identified as 10 N load, 1 m/s sliding speed and 1000 m sliding distance. Prediction modeling using ANN further authenticated the nonlinear effect of these parameters on wear behavior. The results show that the Al7075–SiC nanocomposites have improved wear resistance at low load and moderate sliding distance conditions, indicating their applicability for automotive and aerospace components that require lightweight materials to sustain different contact stresses and sliding conditions. Al7075 reinforced with 3 wt% nano-SiC shows improved wear resistance. Applied load is the dominant factor, contributing 76.79% to wear rate. Regression model achieved R² = 0.962, ANN model R² = 0.9976. Hybrid ANOVA–ANN approach enables accurate wear prediction. SEM confirms mild abrasion at low load, severe wear at high load.