Comprehensive Optimization of the Electrical Discharge Drilling in Terms of Energy Efficiency and Hole Characteristics

This work addresses a process parameter-based optimization of the electrical discharge drilling (EDD) of the hole to decrease the specific drilling energy (SDE), the dilation of the hole (DH), and the tapper ratio (TR). The input parameters are the applied current (AC), pulse on time (TON), pulse off time (TOFF), discharge voltage (VO), gap voltage adjustor (GAP), capacitance parallel connection (CAP), and servo sensitivity selection (SV). The adaptive neuro based-fuzzy inference system (ANFIS)-based models were proposed to render the relations between the process parameters and EDD performances. The weights between multi-responses are determined using the entropy method. The optimum factors were obtained by the neighborhood cultivation genetic algorithm (NCGA). The findings revealed that the proposed ANFIS models employing gaussmf membership function may help to minimize the predictive error. The optimal values of the AC, TON, TOFF, VO, GAP, CAP, and SV are 5 A, 60 µs, 50 µs, 60 V, 6, 7, and 7, respectively. The SDE, DH, and TR are reduced by 10.13%, 34.46%, and 11.63%, respectively, as compared to initial values. Moreover, a hybrid approach using the ANFIS model, entropy method, and NCGA is a prominent technique for modeling and optimizing different EDD processes.