Experimental investigation of hydrodynamical polishing of hard steel alloys using SiC abrasive and modelling with ANN

Abstract

In this research, the impact of hardness of material, tool angular speed, abrasive particle size, and slurry concentration on surface roughness during the hydrodynamic polishing process is examined. Experimentation is carried out on EN18 and EN31 steels using a drilling machine with a soft tool of polyurethane rubber and slurry (mixture of servo spin oil 12 and SiC) for machining. Taguchi's L18 orthogonal array is designed to find out the optimum process parameters. The results of the experimental investigation and parametric analysis demonstrate that the process variables have a substantial impact on the surface quality, with the abrasive particle size having a greater impact than tool speed, tool hardness, and slurry concentration. The results reveal that the chemical erosion process efficiently eliminates surface layer and the HDP procedure reduces surface roughness. The minimum surface roughness (Ra) is only 0.21 µm. Also, there are few inclusions and flaws on the machined surface. In comparison to material hardness and angular speed, abrasive particle size has a greater impact on surface roughness. Also, artificial neural network a computational technique, is used for better prediction values with a higher coefficient of determination (R2) value.