Experimental Investigation of Rotary EDM on Material Removal Rate and Surface Roughness of Machining EN31 Steel

Abstract

The Tool Rotation EDM process parameters are optimized in this research through using the multi-response optimization Grey Relational Analysis (GRA) approach instead of single-response optimization. Three parameters were analysed at three different levels and utilized for the experimental design using Grey Relational Analysis. The current study suggests combining tool rotation and mixing silicon carbide powder with kerosene as a dielectric solution in electrical discharge machining of EN31 steel with copper electrode. To examine the impacts on material removal rate and surface roughness of EN31 steel, machining parameters such as tool rotation (Trpm = 1200, 1500, 1800 rpm), peak current (Pc = 8, 10, 12 amp), and abrasive - silicon carbide (SiC) grit size (Ac = 60, 80, 100 μm) with Copper Electrode of diameter 20 mm. To study three components at three levels, a complete factorial experiment was performed. FESEM, EDS, and XRD techniques were employed to investigate and assess the workpiece's surface shape as well as powder characteristics. Using Minitab17 and L27 orthogonal arrays to design experiments, it has been discovered that there is a correlation between the process parameter and the responses in REDM. It has discovered that the best results were obtained with the ideal values for the surface roughness parameter. Three components were investigated in complete factorial study at three distinct levels. The work piece's surface morphology was examined using FESEM and EDS methods. Genetic algorithms were applied in multi-objective optimization techniques.