Preliminary Evaluation and Wear Properties Optimization of Boron Carbide and Molybdenum Disulphide Reinforced Copper Metal Matrix Composite Using Adaptive Neuro-fuzzy Inference System

Abstract

This paper presents a comprehensive research on the influence of boron carbide and molybdenum disulphide hybrid reinforcement on physical, mechanical and tribological characteristics of copper metal matrix composite dedicated for electrical appliances application. The synthesis of novel copper metal matrix composite reinforced with boron carbide (0 to 3%) and molybdenum disulphide (0 to 4.5%) were fabricated using liquid stir casting technique. There is an uptrend in the physical and mechanical characteristics of the copper metal matrix composite with higher reinforcement addition in comparison to pure copper. The Taguchi experimental design is applied to plan the experiments and S/N ratio is used to determine optimal factor settings for minimum wear loss of the copper metal matrix composite. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) model is developed to predict the wear loss with respect to changes in input parameters viz. composition, load, sliding speed, and sliding distance. The confirmation experiments results reveal conformity between the ANFIS model and experimental results with 97.99% accuracy implying that the established ANFIS model can be precisely used for predicting the wear loss. Moreover, the novel copper metal matrix composite reinforced with boron carbide and molybdenum disulphide shows enhanced wear resistance in respect to unreinforced composite that make it appropriate for electrical appliances application.