Tribological performance enhancement of SiO₂ reinforced AA8011 composites through grey fuzzy based optimization

The development of lightweight and wear-resistant materials plays a vital role in advancing sustainable engineering systems by improving energy efficiency and extending component service life. In the present study, AA8011 aluminium matrix composite reinforced with 9 wt% SiO₂ particles was fabricated using the stir casting technique and its tribological performance was systematically evaluated under dry sliding conditions. Wear experiments were conducted using a pin-on-disc tribometer with varying load (10-40 N), sliding speed (0.5-2 m/s), and sliding distance (500-2000 m). The experimental results revealed that the wear rate increased from 0.00532 mm³/m to 0.00902 mm³/m with increasing load and sliding velocity, while the coefficient of friction varied between 0.164 and 0.411. Friction force values ranged from 2.44 N to 15.04 N, indicating a strong dependence on operating conditions. To optimize the tribological responses, Taguchi L16 orthogonal array was employed for experimental design, while Grey Relational Analysis integrated with fuzzy logic was used for multi-response optimization. The Grey Relational Grade ranged from 0.339 to 0.761, whereas the Grey Fuzzy Grade varied between 0.343 and 0.717. The optimal parameter combination was identified as low load, moderate sliding speed, and intermediate sliding distance, yielding the maximum Grey Fuzzy Grade of 0.717. Analysis of variance confirmed that load was the most significant factor influencing tribological performance (p = 0.001). Validation experiments showed close agreement between predicted (0.8064) and experimental (0.7835) Grey Fuzzy Grade values with a minimal deviation of 2.84%. The results further demonstrate that the incorporation of 9 wt% SiO₂ particles reduces the minimum wear rate to 0.00532 mm³/m, a 35% improvement over the base alloy. Consequently, the optimized AA8011-SiO₂ composite exhibits superior wear resistance compared to standard operating conditions, supporting its potential for sustainable engineering applications through extended component service life and improved resource efficiency.