Material removal rate (MRR) and surface roughness (Ra) of sink electric discharge machining (EDM) were investigated using experimental design and discharge energy method. This study is focused on the effects of the peak current, on time, and off time, during the discharging of the HP4MA material. Mathematical models were developed for the prediction of the Ra and MRR based on the response surface methodology (RSM). was proposed using the discharge energy method that was compared to the RSM model and the experimental results. The results indicated that both models can be used to predict the MRR and Ra of the EDM accurately.

Conventional die-sinking electrical discharge machining (EDM) employs a single electrode operating under constant discharge conditions. We explored a two-electrode scenario, in which roughing and finishing were coupled. We developed a multiple discharge step (MDS) method that uses three discharge depths. The discharge current is highest in step 1 and lowest in step 3. Response surface methodology (RSM) was employed to optimize the discharge conditions. Experimentally, the MDS method combined with RSM decreased electrode edge wear and surface roughness compared to the conventional method, with no increase in the average discharge current.

The objective of this study is to generalize the micro surface topography prediction algorithm researched in the laboratory to mold machining industry. The micro surface topography including the geometry of the tool, surface inclination angle, and cutting conditions was simulated using developed software. A satisfactory agreement was observed between the literature and the simulation results. In the factory, the average surface roughness of the machined surfaces was measured using surface roughness measuring tester SJ-210, and the 3D surface topography was measured using replica tape and the New View 7300 system in the laboratory. The error of the measured areal surface roughness of the molds with respect to the prediction result is 30%. The comparison results indicated that the established micro surface topography software should be improved to predict the surface roughness of the mold in industry.