Investigating and predicting tribological characteristics of AZ31 alloy composites reinforced with nano-Al2O3 and micro-Sn particles: a comparative analysis using CCD-RSM and ANN models

Abstract

Abstract In this research, the central composite-based response surface methodology was adopted to select the dominant optimal input factors on wear behaviour and coefficient of friction of an AZ31-microtin/2 wt% nano-Al2O3 composite prepared through a stir casting process with different wt% of Sn. The input factors, such as wt% of Sn reinforcement, sliding distance, sliding speed, and applied load, were selected to determine their significant effects on the coefficient of friction and wear behaviour with 30 trial runs. Analysis of variance (ANOVA) results indicated that Sn reinforcement plays a significant role in the wear behaviour of the nanocomposites, followed by applied load and sliding distance. In addition, an enhancement in wear resistance was witnessed by the addition of Sn reinforcement with AZ31/nano-Al2O3 composites. The optimal process parameters as per the desirability approach were found to be a weight percentage of Sn: 8%, load: 20 N, sliding speed: 2 m s−1, and sliding distance: 1000 m. According to the ANN results, the predicted data is perfectly acceptable with the actual experimental response value. The R values for the training, validation, and testing phases are 0.96166, 0.96801, and 0.98914 for COF, and 0.97688, 0.99247, and 0.99331 for wear rate, indicating a robust correlation between predicted and actual values. The worn-out pin samples were used to examine the worn surface morphology and analyze the wear mechanism.