Optimization of process parameters using graphene-based dielectric in electric discharge machining of AISI D2 steel

Abstract

Hard to machine materials have growing demand in industrial sector especially in nuclear, automotive, and aerospace industries for sustainable production. These materials cannot be machined by typical machining methods or conventional methods, and for machining such materials, nonconventional machining method are usually used. Electric discharge machine is widely used for machining such materials and complex geometries. This research aims to optimize the process parameters while electric discharge machining of AISI D2 steel using nanofluids. The effects of four most influencing factors including pulse-off time, discharge current, pulse-on time, and conc. of nanoparticles have been investigated. Graphene nanoplatelets mixed with kerosene oil were used as a dielectric. Box-Bhenken design based on response surface methodology (RSM) was used for experimentation. Regression models for performance measures such as material removal rate, surface roughness, and white layer thickness have been developed using RSM. ANOVA has been carried out for identifying the most significant factors. Multi-objective optimization has been carried out in terms of desirability function by establishing a compromise between maximum material removal rate and minimum surface roughness and white layer. ANOVA results shows that conc. of nanoparticles is the most significant parameter affecting the performance measures followed by the discharge current. The confirmatory tests were run for verifying and validating the results, and improvements in the performance measures such as MRR, Ra, and WLT up to 21.93 mm3/min, 3.98 μm, and 19.13 μm, respectively, at an optimum have been observed. Multi-response optimization yielded compound desirability of 85.7% for the selected levels of process parameters for machining of AISI D2 steel.