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 dioxide (ZrO2) reinforced Aluminium alloy 6061 hybrid nanocomposites (AMMHNCs) fabricated by the 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% ZrO2. A microstructural study was carried out using optical and scanning electron microscopic images to analyze the dispersion of reinforcements in the composite. The results show that the ultrasonic-assisted stir casting method improves the uniformity in the dispersion of reinforcements. The hardness, tensile, impact, and wear tests were carried out based on ASTM standards to analyze the properties of the proposed composite specimens. It was observed that the hardness, tensile strength, and impact strength increased by 21.88%, 69.42%, and 78.57% respectively, and percentage elongation decreased by 63.52% with the increase of reinforcements. Wear resistance increases with the increase of reinforcements. In order to analyze the wear behavior originality of new composite underwear 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 behavior of the composite. Comparative analysis for ANN and ANFIS was performed and the results have shown that the ANFIS model predicted with an accuracy of R2 with 99.9%.

Tribological analysis of laser deposited SS316L/Co27Cr6Mo functionally graded materials using adaptive neuro-fuzzy inference system

“…It was observed that an increase in reinforcements resulted in improved mechanical properties, whereas percentage elongation decreased. Furthermore, wear resistance was found to improve with additional reinforcements (Masooth 2022).…”