Multi-objective Optimization of Graphite Powder Mixed Electric Discharge Machining Process Parameters on Inconel 625 Using Genetic Algorithm

Nickel-based superalloys have made their presence felt in the aerospace, automotive, chemical, and marine industries because of their exceptional capability to retain their mechanical properties at high temperatures and corrosion resistance. Chemical affinity, low thermal conductivity, and strain-hardening properties make them difficult to machine using typical machining techniques. Electric discharge machining (EDM) is one of the most extensively used operations for machining nickel-based superalloys. To promote the material removal rate (MRR) and surface integrity, some variations of basic EDM can be used, such as magnetic field (MFAEDM), ultrasonic vibration (UVEDM), and powder-mixed EDM (PMEDM). Graphite powder was selected for the PMEDM process in this study. MRR, surface crack density (SCD), and surface roughness (SR) were analyzed by varying the input conditions, such as powder concentration (C), current (I), pulse-on time (T), voltage (V), and duty cycle (D). The tests were planned based on the central composite design (CCD) of response surface methodology (RSM). The results indicated that graphite resulted in a higher MRR, lower SCD, and higher SR than conventional EDM. The results of 32 experimental runs were used for regression analysis to generate mathematical equations. The genetic algorithm was used for multi-objective optimization of input parameters.