ISSN: 2277-9655 [SAMBOJU* et al., 7(5): May, 2018] Impact Factor: 5.164 IC™ Value: 3.00 CODEN: IJESS7 http: // www.ijesrt.com © International Journal of Engineering Sciences & Research Technology [183] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY OPTIMIZATION OF PROCESS PARAMETERS IN ELECTRICAL DISCHARGE MACHINING PROCESS BY USING TAGUCHI METHOD S. AJAY KUMAR *1 , K. SANTOSH KUMAR 2 , K PRUDHVI RAJ 3 1 Assistant Professor, Dept. of Mechanical Engineering, MGIT, Hyderabad. 2,3 Assistant Professor, Dept. of Mechanical Engineering, MGIT, Hyderabad DOI: 10.5281/zenodo.1241436 ABSTRACT The objective of this research study is to investigate the optimal process parameters of Electric Discharge Machining on RENE80 nickel super alloy material with aluminum as a tool electrode. The effect of various process parameters on machining performance is investigated in this study. The input parameters considered are current, pulse on time and pulse off time are used for experimental work and their effect on Material Removal Rate, Tool Wear Rate and Surface Roughness. The Taguchi method is used to formulate the experimental layout, ANOVA method is used to analysis the effect of input process parameters on the machining characteristics and find the optimal process parameters of Electric Discharge Machining. The results of the present work reveal that proper selection of input parameters will play a significant role in Electric Discharge Machining. KEYWORDS: Electrical Discharge Machining, orthogonal array, metal removal rate, tool wear rate ANOVA. I. INTRODUCTION Electric Discharge machining (EDM) is a thermo-electric, non-traditional machining process used to machine precise and intricate shapes on difficult to cut materials and super tough metals such as ceramics, maraging steels, cast-alloys, titanium which are widely used in defence and aerospace industries. Electrical energy is used to generate electrical sparks and material removal mainly occurs due to localized melting and vaporization of material which is carried away by the dielectric fluid flow between the electrodes. The performance of this process is mainly influenced by many electrical parameters like, current, voltage, polarity, and pulse on time, pulse of time, electrode gap and also on non-electrical parameters like work and tool material, dielectric fluid pressure. All these electrical and non electrical parameters have a significant effect on the EDM output parameters like, Metal Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness(SR). The EDM is very complex and stochastic process and is very difficult to determine the optimal machining parameters. In the present study the output responses MRR, TWR and SR are conflicting in nature. MRR reflects the productivity and tool wear reflects the accuracy of the product. So in this work it was proposed to study the effects of different input parameter current, pulse on time and pulse off time on Material removal rate, Tool wear rate and Surface roughness with EDM oil as a dielectric. The experimental design has been done by using Taguchi technique. The response has been analyzed using S/N ratio and analysis of Variance. II. EXPERIMENTAL DETAILS The experiments were conducted on V3525 precision die sink electric discharge machine as shown in Fig.1 which consist a work table, a servo control system and a dielectric supply system. The machine has 8 current settings from 3A to 24A, 9 settings of pulse on time, 9 settings of pulse off time and spark gap of 50-75 microns The experiments are conducted on RENE80 Nickel Super alloy(Russian grade -RZ) and the work piece dimensions are 70 mm x 35 mm x 5 mm. Work piece material properties are: Hardness (HRC)= 43-45, density (g/cm 3 )=8.16, Ultimate tensile strength (Kg/mm 2) =85, Elongation % =3, Creep strength ( 0 C) = 975. Thermal conductivity (W/m 0 K)= 11.50. The tool material used is aluminum- density 2.70 gm/cm 3 and thermal conductivity 237 w/m 0 k and the machining is done with straight polarity. EDM oil Grade 30 is used as the dielectric fluid and the experiments were performed for a particular set of input parameters. The number of experiments and, input levels are decided based on the design of experiments and the input parameters and their levels are presented in Table 1. The MRR and TWR are calculated using digital balance of accuracy 1mg and