Modeling and Prediction of Material Removal Rate and Surface Roughness in Surface-Electrical Discharge Diamond Grinding Process of Metal Matrix Composites

Abstract

Material removal rate (MRR) and surface roughness (SR) have always been a big deal during any manufacturing process. Metal matrix composites (MMCs) can't be effectively machined by conventional grinding and process is found to be slow when machined by electrical discharge machining (EDM). Present work is an attempt for modeling of electrical discharge diamond grinding (EDDG) in surface grinding mode which is known as the surface-electrical discharge diamond grinding (S-EDDG) process. The technique used for modeling the process is artificial neural network (ANN) through traingdx training function. Experiments were carried out on newly developed and fabricated surface grinding setup for EDDG on a die sinking EDM machine for Al-10wt%SiC and Al-10wt%Al₂O₃ composite workpiece. Prediction through modeling of S-EDDG process indicates that MRR increases as pulse current, wheel speed, workpiece speed, depth of cut increases, and decreases with increase in duty factor. The R_a increases with increase of current, duty factor, depth of cut, and workpiece speed, and decreases with increase in wheel speed.

Keywords:

• Artificial neural network
• Average surface roughness
• Material removal rate
• Metal matrix composite
• Surface-electrical discharge diamond grinding