Open Access

Experimental Analysis of EDM Parameters on D2 Die Steel Using Nano-aluminum Composite Electrodes

Y. Justin Raj, Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science (SIMATS), Chennai, TN, India A. Bovas Herbert Bejaxhin, herbert.mech2007@gmail.com
Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science (SIMATS), Chennai, TN, India R. Madhumitha, ECE Department, St. Joseph’s College of Engineering, Chennai, TN, India A. S. Anitha, Department of Biotechnology, Karpaga Vinayaga College of Engineering and Technology, Padalam, TN, India Hitesh Gehani, School of Computer Science & Engineering, Shri Ramdeobaba College of Engineering and Management, Ramdeobaba University, Nagpur, MH, India Aslam Abdullah , School of Chemical Engineering, Vellore Institute of Technology, Vellore, TN, V. Naveenprabhu Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, TN, India

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Abstract

         This paper presents an experimental study on the electrical discharge machining (EDM) of AISI D2 die steel using an Al-Ni composite electrode. The investigation focuses on the influence of input parameters like current, pulse duration, and pulse interval, on key output parameters such as material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). EDM oil was employed as the dielectric fluid. Grey relational analysis (GRA) was utilized for designing and conducting the experiments using the Taguchi L9 method. The ANN model showed excellent predictive accuracy with a perfect correlation coefficient (R) of 1.00, indicating strong capability in forecasting MRR based on machining parameters. GRA further confirmed that higher current settings and longer pulse-off times effectively reduce tool wear, suggesting that the ANN model accurately reflects the conditions that minimize TWR. The ANN model achieved strong predictive accuracy for SR, with high correlation coefficients, although with slightly higher mean squared error (MSE) in testing.

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Reference

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