Wear resistivity and ANFIS modeling for hybrid aluminum metal matrix composites at elevated temperatures

Abstract Hybrid aluminium metal matrix composites (HAMMCs) are increasingly popular in aerospace and automotive sectors because of their incomparable properties. This study investigates the wear characteristics of stir cast AA7075 reinforced with 2–4 wt.% of titanium diboride (TiB 2 ), boron carbide (B 4 C) and a constant 2 wt.% of molybdenum disulfide (MoS 2 ) as a solid lubricant. Wear tests were conducted at elevated temperatures (100–200 °C), applied loads (10–30 N), sliding distances (500–1500 m) and sliding velocities (2–6 m s −1 ). Taguchi L27 orthogonal array was employed for experimental design to evaluate the influence of process parameters on the response variables. The worn surface characteristics, wear mechanism and microstructural features were examined by field emission scanning electron microscopy (FESEM) equipped with energy dispersive X-ray spectroscopy (EDX). Additionally, elemental mapping confirmed the homogeneous reinforcement distribution and enhanced the composite structural integrity. The friction force, wear rate and coefficient of friction (CoF) were predicted by using adaptive neuro-fuzzy inference system (ANFIS). Results showed high predictive accuracy, with ANFIS achieving 99.99 % for wear rate, 99.89 % for friction force and 98.71 % for CoF, validating the model against experimental and Taguchi outcomes. These findings underline HAMMCs potential for high performance applications in challenging environments.