INVESTIGATION AND OPTIMIZATION OF TRIBO-MECHANICAL BEHAVIOR OF SQUEEZE CASTED AL-Si PISTON ALLOY-BASED METAL MATRIX COMPOSITE USING RESPONSE SURFACE METHODOLOGY AND NEURAL NETWORK

The piston in the automobile engine must withstand high stress and temperatures and also have low weight. The alloy of Al-Si is the most commonly used in the piston. The reinforcement using ceramics, fibres or nanoparticles will increase the properties of the piston alloy. In this work, the piston alloy is fabricated as a metal matrix composite of Al-Si reinforced by TiC-MoS2 using the squeeze casting method. The squeeze casting effect on the matrix and the reinforcement is studied using tensile testing and microstructure analysis. The strength hardness obtained from the experimentation gives the highest tensile strength of 330 MPa and the hardness of 110HBN. The fractography and morphology are investigated using SEM (scanning electron microscope), resulting in the lowest porosity of 3.21% obtained in the composite material. The tribological behavior was also investigated at the condition of dry Sliding using pin on disc tribometer gives the lowest coefficient friction of 0.31 and the wear rate of 0.0051 mm3/m. The experiment is numerically designed and optimized using the response surface methodology (RSM). The obtained value is predicted and validated using a hybrid approach of deep belief network-reptile swarm algorithm (DBN-RSA). The regression of the parameters is about 99.97% showing that the model is very accurate to the experimental results. The RMSE of the proposed method implies less error and shows the accuracy level of the parameters. The result shows that the designed model is best fitted for the tribological and mechanical properties investigation of metal matrix composite.