Modeling To Predict Cutting Force And Surface Roughness of Metal Matrix Composite Using ANN

recently, the composite material founds various applications in aerospace industries, automobile and commercial industries etc. Metal-matrix composites are combinations of two or more materials where tailored properties are achieved by systematic combinations of different constituents. The Aluminum was selected for matrix material and Silicon carbide as a reinforcing element for metal matrix composite. The stir casting method was used to make a specimen of Al-SiCp metal matrix composite. The 5, 10 and 15 wt % of particulates SiC were dispersed in the base matrix of Al 6061. The aim of the present work is to measure the values of the parameters like cutting force and surface roughness. The effect of spindle speeds, feed rates and depth of cuts on cutting forces and surface roughness are investigated in the milling operation of siliconcarbide particle reinforced aluminum metal matrix composites. In the present study, cutting force and surface roughness prediction model of Al-SiCp metal matrix composites was developed using artificial neural network. SiC percentage, spindle speed, feed rates, and depth of cut are taking as input parameters while the cutting force and surface roughness taking as output parameters for ANN model.