Evolutionary multi-objective optimization of energy efficiency in electrical discharge machining

Electrical energy, which in the machining zone is transformed into heat, is of key importance in electrical discharge machining (EDM). Machining performance of EDM is determined by the characteristic of discharge energy. Therefore, an experimental-analytical approach of discharge energy efficiency was analyzed. The main input parameters for controlling the discharge energy are discharge current and discharge duration. The EDM process is monitored considering the two output machining performance, i.e., material removal rate and surface roughness, which are important for increasing productivity and quality. We modeled the energy efficiency of electrical discharge machining by the use of genetic algorithm. With this action an attempt was made to find even more precise dependence of discharge energy parameters with machining performance. Finally, this was followed by optimization of the discharge energy efficiency in EDM process using multi-objective approach. Evolutionary two-objective optimization is leading to the set of optimal solutions for the discharge energy considering the two machining parameters. Using this set of solutions, EDM discharge energy parameters can be selected to achieve high material removal rate with good surface roughness.