Multi-objective parametric optimization on machining of Inconel-825 using wire electrical discharge machining

Wire electrical discharge machining is a thermal energy-based non-conventional machining process which can machine conductive materials with high precision. In this present work, machining of Inconel-825 was performed using wire electrical discharge machining. Multi-objective parametric optimization was performed for maximum cutting rate and minimum surface roughness using teaching-learning-based optimization, grey relational analysis, and genetic algorithm. Four wire electrical discharge machining parameters, namely spark off time (S-OFF), spark on time (S-ON), peak current (I-P), and angle of cutting (A) were considered. Comparison of optimum wire electrical discharge machining parameters through teaching-learning-based optimization, grey relational analysis, and genetic algorithm was performed. The better optimum solution for wire electrical discharge machining parameters was obtained using teaching-learning-based optimization and optimum values were at I-P (1 A), S-ON (30 mu s), S-OFF (12.5 mu s), and A (44.8 degrees) with cutting rate as 19.744 mm/min and surface roughness as 1.331 mu m. The optimum results obtained using optimization techniques were validated with the experimental results and error was observed to be within 5%. Moreover, response surface models were developed to predict the cutting rate and surface roughness in terms of wire electrical discharge machining parameters using analysis of variance.