Multiobjective Optimization of Electrical Discharge Machining Process Using a Hybrid Method

Abstract

Electrical discharge machining (EDM) is a widely used process in manufacturing industries for high-precision machining of all types of conductive materials. Material of any hardness can be machined as long as material can conduct electricity. Proper selection of input parameters is one of the most important aspects in the die sinking EDM, as these conditions determine important characteristics such as surface roughness and material removal rate (MRR). In the present work, empirical models have been developed for relating the surface roughness and MRR to machining parameters like pulse-on time, pulse-off time, and discharge current. Response surface methodology (RSM) has been applied for developing the models using the technique of design of experiments (DOE) and multilinear regression analysis. The developed empirical models are used for optimization. Since the influence of machining parameters on surface roughness and MRR are conflicting in nature, there is no single combination of machining parameters, which provides the best machining performance. A multiobjective optimization method, nondominating sorting genetic algorithm-II, is used to obtain the Pareto-optimal set of solutions.

Keywords:

• Algorithm
• Discharge
• Electrical
• Empirical
• Genetic
• Machining
• Methodology optimization
• Modeling
• Response
• Surface

Notes

SS = Sum of squares; DF = Degrees of freedom; MS = Mean squares; *Significant terms.

SS = Sum of squares; DF = Degrees of freedom; MS = Mean squares; *Significant terms.