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Powder Mixed Electrical Discharge Machining (PMEDM) is considered as one of the viable methods to machine the materials which are hard and tough but with appreciable thermo-electrical properties. In PMEDM, it has been established that machinability is enhanced by adding additives in the form of fine powders in the spark gap of EDM process. In this investigation, the role of the thermo-electrical properties of workpiece on PMEDM process has been studied. Materials with distinct thermoelectric property, namely Mild Steel and Aluminium are taken as workpiece with Alumina as additive powder for the PMEDM process. Experiments have been conducted with Taguchi L9 Orthogonal Array (OA) with Material Removal Rate (MRR) and Tool Wear Index (TWI) as responses. Gap current, duty cycle and flow rate of powder mixed dielectric liquid have been taken as process parameters at 3 levels with circular copper rod of 12 mm diameter as tool electrode. Determination of the influence of properties of the workpiece on the machinability has been performed using a custom made PMEDM setup. ANOVA and main effect plot of data means for the aforementioned conditions have been analysed. The results are noteworthy with significant influence of the properties of the workpiece on the machinability of PMEDM process.

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In this study, an attempt has been made in PMEDM process to sustain the homogeneity in the powder-dielectric mixture irrespective of the nature of the powders, their particle size, concentration etc., The traditional way of powder mixing system in Powder Mixing Electric Discharge Machining (PMEDM) has been refurbished with a novel Eductor based system along with a metering devise to ensure uniform mixing of the powers with the dielectric. Additionally sintered crucible filtration test on the sample of powder-dielectric mixture ensured the presence of known quantity of powders in the dielectric. The experiments are conducted on Titanium alloy with Gap current, Duty factor, Delivery pressure, powder types (Alumina, Silica, and copper) and concentration of these powders as variable process parameters. The output responses, namely material removal rate, tool wear index and surface finish obtained during the machining process have been optimized using AHP-TOPSIS method. The confirmation test indicated that the closeness co-efficient value for the TOPSIS analysis improved by 2.37% compared with the predicted value.