Theoretical and Experimental Investigation of the Material Removal Rate, Surface Roughness, and Tool Wear Ratio in Electrical Discharge Machining

Material removal rate (MRR), tool wear ratio (TWR) and surface roughness (Ra) is major parameters that affect the quality of electro discharging machining (EDM). Recently many research works have done to optimize these important factors. A significant number of techniques such as: Fuzzy logic, Artificial neural networks (ANN), Response surface method (RSM), Grey relational analysis (GRA) and Taguchi method have been applied to optimize the mentioned parameters. As an instance some researches have used ANN and Taguchi method to predict and optimize the Ra, TWR and MRR in electro discharge machining of Titanium alloy, AISI 2312 and AISI 1040 tool steel. The AISI-D6 steel is extensively used as a dies and molds material. In this current study, the Electric Discharge Machining (EDM) of the said material is carried out following a test plan composed of 32 runs. The effects of some important operating parameters, namely pulse on-time (Ton), pulse current (I) and voltage (V), on the performance measures of EDM process such as Material Removal Rate (MRR), Tool Wear Ratio (TWR), and Average Surface Roughness (Ra) are quantified. Also in this investigation, response surface method (RSM) was used to predict and optimize the material removal rate, tool wear ratio and surface roughness during electrical discharge machining of AISI D6 tool steel. Pulse on time, pulse current, and voltage were considered as input process parameters. Furthermore, the analysis of variance was employed for checking the developed model results. The results revealed that higher values of pulse on time resulted in higher values of material removal rate and lower amounts of tool wear ratio. In addition, increasing the pulse current caused to higher amounts of both material removal rate and tool wear ratio. Moreover, the higher the input voltage, the lower the both material removal rate and tool wear ratio. The optimal condition to obtain a maximum of material removal rate and a minimum of tool wear rate was 40 μs, 14 A and 150 V, respectively for the pulse on time, pulse current and input voltage. Also, the pulse on time was the most effective parameter influencing the roughness. It was found that the higher values of pulse on time and pulse current and lower values of input voltage caused to in higher amounts of surface roughness. The optimal condition to obtain a minimum of surface roughness was 10.22 μs, 8.02 A and 174.74 V, respectively for the pulse on time, pulse current and input voltage. Even though the prior investigators explored mathematical models in the case of some alloys, a research into the establishing mathematical relationships between the input parameters and output responses during EDM of AISI D6 tool steel is lacking. Therefore, the aim of this study was to apply RSM in conjunction with full factorial design, to establish the functional relationships for EDM of parameters i.e. pulse on time, pulse current and voltage, and responses of AISI D6 tool steel i.e. material removal rate, tool wear ratio and surface roughness. In order to guide the process users, process maps (i.e., parameter-effect correlations) are generated. It is found that the parametric effects are quite contradictory in nature. Further, to obtain a trade-off among various performance measures, it is proposed to choose intermediate values of parameters as given in the manuscript. Outcomes of the performed research demonstrated that the mathematical model can be developed for different work piece and electrode materials for EDM processes, also there is a need of microscopic studies to clarify the variation in parameters that may affect the microstructure of the work-piece.