Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems

Abstract This research work investigates the mechanical and wear behaviour of graphene (C) and zirconium di-oxide (ZrO₂) reinforced Aluminium alloy 6061 hybrid nano composites (AMMHNCs) fabricated by ultrasonic-assisted stir casting method. Graphene and ZrO₂ are selected as reinforcements for increasing the wear resistance and hardness of the base alloy AA6061. The mixing proportions of graphene and ZrO₂ reinforced with AA6061 in weight are 100% AA6061/0% Graphene/0% ZrO₂, 98.5% AA6061/0.5% Graphene/1% ZrO₂, 97.5% AA6061/0.5% Graphene/2% ZrO₂, 98% AA6061/1% Graphene/1% ZrO₂, 97% AA6061/1% Graphene/2% ZrO_2. Microstructural study was carried out using optical and scanning electron microscopic images to analyse the dispersion of reinforcements in the composite. The results shown that, ultrasonic-assisted stir casting method improves the uniformity in dispersion of reinforcements. The hardness, tensile, impact and wear test were carried out based on ASTM standards to analyse the properties in the proposed composite specimens. It was observed that, the hardness, tensile strength and impact strength are increases by 21.88%, 69.42% and 78.57% respectively and percentage elongation is decreased by 63.52% with the increase of reinforcements. Wear resistance increases with the increase of reinforcements. In order to analyse the wear behaviour originality of new composite under wear test parameters, Artificial Neural Network (ANN) and Artificial Neuro Fuzzy Inference Systems (ANFIS) models were used to predict the wear rate for experimented and non-experimented parameters. The prediction analysis was useful in studying the wear behaviour of the composite. Comparative analysis for ANN and ANFIS was performed and the results shown that, ANFIS model predicted with accuracy of R² with 99.9%.