Optimization and modelling of tribological properties in self-lubricating AA2024/nano-graphene/nano-WC hybrid composites using RSM-CCD, TOPSIS, Pareto analysis and ANFIS

The present study investigates the minimization of wear rate and frictional coefficient in AA2024 aluminium matrix composites by varying key parameters: tungsten carbide (WC) reinforcement percentage, sliding speed, load and sliding distance. The objective is to enhance the wear performance characteristics of AA-2024-based composites reinforced with nano-WC and nano-graphene particles. The composites were prepared by incorporating a constant 0.15% nano-graphene and WC at weight percentages of 1%, 2% and 3% by the stir-casting process. The Response Surface Methodology (RSM)-based central composite design technique is used to plan 30 experiments to study the dry sliding behaviour. The ANOVA technique is used to study the influence of input parameters on output responses. It reveals that the reinforcement percentage of WC is the most influential parameter. The wear rate is minimized when the reinforcement percentage increases from 1 to 2 wt.%, but it increases again when the percentage exceeds 2 wt.%. The Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) and Pareto optimality analysis determined a 2 wt.% WC reinforcement provides optimal wear resistance under sliding circumstances of a 10 N load, 1000 m sliding distance and 328 rpm speed. Further, an integrated adaptive neuro-fuzzy inference system and an exhaustive search approach are used to effectively model the dry sliding wear behaviour of AA-2024-based composites with minimal training and validation errors of 0.0397 and 0.0396. Scanning electron microscopy was employed to analyze the samples of worn surfaces, elucidating the wear mechanisms, which included delamination and abrasion deformation modes.