Multi-attribute recursive optimization (MARO) for EDM of D3 tool steel

ABSTRACT Electric Discharge Machining (EDM) is one of the most effective unconventional material removal techniques that mill electrically conductive objects, despite their hardness using electrical discharge. This elite method provides excellent accuracy and surface finish within short duration. The presented research envisages to study the effect of process variables of EDM namely Pulsating Current (I), Pulse-on-time (Ton) and Pulse-off time (Toff) on machining performance measures namely Tool Wear Rate (TWR), Surface Roughness (SR) and Material Removal Rate (MRR). The best possible condition for specimen selection is presented by a new technique known as Multi Attribute Recursive Optimization (MARO). The optimal experimental conditions were found with Ton 100 s, Toff 49.82 s, and I 4.99 A, with ideal responses of SR 0.057 µm, MRR 0.036 g/min, and TWR 3.301 g/min. For the best run identification, the METHod for Enrichment Evaluation, Preference Ranking Organization METHod (PROME-THEE) was used while Historical Data Design (HDD) was used to validate the result obtained. The integration of PROMETHEE and HDD known as MARO is identified to appreciate degree of the methods analyzed. The close convergence of PROMETHEE and HDD at 97% guarantees the accuracy of the proposed MARO technique.