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The electrical discharge drilling (EDD) process is an effective machining approach to produce various holes in difficult-to-cut materials. However, the energy efficiency of the EDD operation has not thoroughly been considered in published works. The aim of the current work is to optimize varied parameters for enhancing the material removal rate (MRR), saving drilled energy (ED), and decreasing the expansion of the hole (HE) for the EDD process. Three advanced factors, including the gap voltage adjustor (GAP), capacitance parallel connection (CAP), and servo sensitivity selection (SV), are considered. The predictive models of the performances were proposed with the support of the adaptive neuro-based fuzzy inference system (ANFIS). An integrative approach combining the analytic hierarchy process (AHP) and the neighborhood cultivation genetic algorithm (NCGA) was employed to select optimal factors. The findings revealed the optimal values of the CAP, GAP, and SV were 6, 5, and 4, respectively. Moreover, the ED and HE were decreased by 16.78% and 28.68%, while the MRR was enhanced by 89.72%, respectively, as compared to the common setting values. The explored outcome can be employed as a technical solution to enhance the energy efficiency, drilled quality, and productivity of the EDD operation.

Multi-Response Optimization of Electrical Discharge Drilling Process of SS304 for Energy Efficiency, Product Quality, and Productivity