Prediction of wear behavior in corn cob ash-reinforced aluminum composites using hybrid optimization models

Ceramic-reinforced aluminum composites are utilized extensively in automotive, structural, and aviation fields due to their favorable properties. Yet, issues like low wear resistance, strength-weight ratio, and high cost often challenge them. To address these limitations, the present study investigates the sustainable, cost-effective reinforcing agent Corn Cob Ash (CCA) as reinforcement in Al6061 aluminum matrix composites (AMCs) developed by the stir-casting technique. Experimental results demonstrated that the addition of 2%, 4%, and 6% CCA enhances the composites’ hardness and compressive strength. Notably, 6% CCA reinforcement improved these properties by 42.8% and 40.29%, respectively, as compared to the base Al 6061 alloy. The coefficient of friction (COF) and wear rate (WR) were optimized through dry sliding test parameters using Taguchi’s L36 orthogonal array. The results showed that the 4% CCA composite exhibited the lowest wear rate (average wear rate 0.0055 mm 3 /m), suggesting an increase of 14.3% in wear resistance than unreinforced Al 6061 alloy. Notably, the optimum wear rate was obtained at 2 m/s sliding velocity, 10 N load, and 4 wt % CCA volume fraction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) model outperformed both the ANN and regression models, with wear rate prediction accuracy within ±2%. These findings indicate that CCA-reinforced AMCs can effectively replace traditional materials in structural and automotive applications, offering enhanced performance with eco-friendly reinforcement.