Development of Tribological Performance Optimization of Al–Cu–Mg–SiC–Graphite–Peanut Shell Bio-Hybrid Composites Using Hybrid Spherical Fuzzy Multi-Criteria Analysis

The demand for lightweight, sustainable materials has driven the development of Al–Cu–Mg–SiC–Graphite–Peanut Shell bio-hybrid composites, since conventional Al alloys often suffer from inadequate wear resistance and high friction. Reinforcing with SiC, graphite, and agricultural waste enhances hardness, self-lubrication, and eco-friendliness. Yet, optimizing tribological parameters like load, speed, and sliding distance is complex. This study investigates the tribological behavior of Al–Cu–Mg–SiC–Graphite–Peanut Shell bio-hybrid composites under varying operational conditions. Experimental trials were conducted using load, speed, sliding distance, and composite type as input process parameters, while wear rate, coefficient of friction, material loss, and frictional force were analyzed as output responses. Results demonstrated that higher loads and lower speeds increased wear and frictional losses, while the bio-hybrid formulation (Sample B) exhibited the lowest wear rate (0.011–0.015 mm/h), friction coefficient (0.38–0.41), and material loss, confirming superior self-lubrication and particle stability. Spherical Fuzzy multi-criteria decision analysis (SF-MCDA) effectively ranked alternatives, identifying optimal tribological conditions for minimal wear and controlled friction. SF-AHP captures expert priorities under uncertainty and sets rational weights. SF-TOPSIS (SWGM) ranks real experimental alternatives without linearizing fuzzy judgments. The findings confirm that integrating agro-waste peanut shell ash with SiC and graphite reinforcement yields composites with enhanced wear resistance, eco-sustainability, and potential for automotive and aerospace applications.