Experimental investigation and parameter optimization of near-dry wire-cut electrical discharge machining using multi-objective evolutionary algorithm

Abstract

The near-dry wire-cut electrical discharge machining (WEDM) process is an environment-friendly manufacturing process, in which there is no harmful effect to the operators. The authors focus on the non-polluting ways to cut the materials and to meet the technical requirements like high material removal rate (MRR) and low surface roughness (Ra). In the near-dry WEDM, the finite discrete periodic series sparks between the wire electrode and conducting work material separated by minimum quantity of deionized water mixed with compressed air (air-mist) as a dielectric medium. In the present research, parametric analysis of the process has been performed with the molybdenum wire tool and high speed steel (HSS-M2) work piece. Experiments have been performed using air-mist as the dielectric medium to study the impact of gap voltage, pulse-on time, pulse-off time, air-mist pressure and discharge current on the MRR and Ra using the mixed orthogonal (L18) array-Taguchi method. Taguchi based analysis of variance test was performed to identify the significant parameters. The gap voltage, pulse-on time, discharge current and air-mist pressure were found to have momentous effects on MRR and Ra. The best regression models for MRR and Ra have been developed by regression analysis. The optimal rough and finish cutting parameters have been predicted by Pareto-front using the multi-objective evolutionary algorithm (MOEA).