LAUSR

In this research work, an effort was made to predict the dry sliding wear response of AlMg1SiCu alloy hybrid composites which were reinforced with 10% Silicon carbide particles (SiC) together with weight fractions of 3, 6 and 9% of self-lubricant molybdenum disulphide particles (MoS2) through melt stir casting. The wear behaviour of the hybrid composite samples was evaluated based on Box-Behnken design on pin-on-disc tribometer without lubrication. The output response weight loss was employed to train the neural network model in ANFIS back-propagation algorithm. The weight loss of 9% MoS2 hybrid composite reduced at low sliding speeds, due to the development of shallow sliding grooves and MoS2-lubricated tribolayer. Scanning electron micrographs and EDS of the AlMg1SiCu alloy hybrid composites revealed a uniform distribution of SiC and MoS2 particles. The tensile strength of the as-cast hybrid composites increases as the wt.% of MoS2 particles increases, according to the tests. However, the addition of MoS2 improved the hardness of the hybrid composites until it reached 6 wt.%, after which it decreased slightly. Weight loss and coefficient of friction decreased by addition of self-lubricant MoS2 in the matrix material. Worn-out surface of the hybrid composite shows the controlling wear mechanisms of the composites, and well-trained ANFIS model could accurately predict the responses better when compared with the response surface methodology model.