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.