Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge

Electrochemical machining (ECM) is a preferred advanced process for Monel 400 alloys. During the machining, toxic nickel hydroxides in sludge are formed. Therefore, it becomes necessary to determine optimum ECM parameters that minimize presence (NP) emission while maximizing material removal rate (MRR). In this investigation, predominant parameters, such as applied voltage, flow rate, and electrolyte concentration, were controlled study their effect on performance measures (i.e., MRR NP). A meta-heuristic algorithm, grey wolf optimizer (GWO), was used multi-objective optimization of ECM, its results compared with moth-flame (MFO) particle swarm (PSO) algorithms. It observed from surface, main, interaction plots experimentation all variables influenced objectives significantly. The TOPSIS algorithm employed convert multiple into single objective convergence plot model, PSO converged very quickly 10 iterations, GWO MFO took 14 64 respectively. case NP tool only 6 iterations converge, whereas 48 88 However, both obtained same solutions EC = 132.014 g/L, V 2406 V, FR 2.8455 L/min best conflicting performances 0.242 g/min 57.7202 PPM). Hence, confirmed these metaheuristic algorithms more suitable finding alloys ECM. This work explores greater scope better performance.