Shaik Khadar Basha1,2* , M.V. Jagannadha Raju3, Murahari Kolli4


1Research Scholar, Department of Mechanical Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India-522502

2Assistant professor, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P, India

3Department of Mechanical Engineering, Andhra University, Visakhapatnam Andhra Pradesh, India-522502

4Department of Mechanical Engineering, Lakkireddy Bali Reddy College of Engineering, Andhra Pradesh, India-521230

*Corresponding Author email: bashaz

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Electrical discharge machining (EDM) is used extensively to machine hard to cut materials like nickel, titanium and super alloys with higher dimensional accuracy and surface finish. In this present study, a multi response optimization on the basis of ratio analysis (MOORA) is proposed for finding optimum process parameter of Electrical Discharge machining (EDM) during machining of Inconel X-750 material which is used mostly in thermal erosion process like nuclear plants, aerospace industry. In this study, response surface methodology (RSM) with Box-Behnken approach has been utilized for selecting the appropriate process parameters by using three level four factor (discharge current (Ip), voltage (V), pulse on time (Ton) and pulse off time (Toff)) using tungsten copper electrode (W-Cu). The machining responses considered are material removal rate (MRR), surface roughness (SR) and tool wear rate (TWR). The satisfactoriness of developed mathematical model has been tested with the use of analysis of variance (ANOVA). Further mathematical equations are generated using the statistical software MINITAB 17. The experimental data used for adequate regression model to optimize the process using MOORA. Further the surface characteristics of the machined surface are identified using scanning electron microscope (SEM) and elements with their peak values are revealed with EDX analysis.