Abstract
Inconel 625 is a nickel-based super alloy widely used in the aerospace, automotive, and marine industries because of its excellent resistance to high temperatures, corrosion, and oxidation. Despite these advantages, its high mechanical strength, hardness, and low thermal conductivity make machining difficult, particularly in micro-electrical discharge machining (micro-EDM) drilling. Micro-EDM is a non-traditional machining process that removes material from electrically conductive work pieces by means of controlled electrical discharges between a tool electrode and a work piece immersed in a dielectric fluid. The process produces localized melting and evaporation, which gradually creates micro-grooves that shape complex and precise features. Machining Inconel 625 by micro-EDM presents challenges such as rework layer formation, heat-affected zones (HAZ), poor surface quality, high tool wear, and reduced dimensional accuracy. These problems are exacerbated in micro-drilling applications where tight parameter control is required for small geometric features. Critical process parameters include discharge current, pulse-on time, pulse-off time, gap voltage, capacitance, electrode material, dielectric properties, and EDM feed rate. Traditional trial-and-error optimization methods are inefficient for such precision-based machining. Therefore, advanced techniques such as evolutionary algorithms, neural networks, response surface methodology (RSM), and MOORA-based multi-objective optimization are used. Among the evaluated parameters, EDM feed rate achieved the highest evaluation value, while pulse-off time showed the least influence on improving machining performance.