Open Access

Optimization of Material Removal Rate and Electrode Wear Rate in EDM Machining of D2 Steel Using Al–Cu–SiC Nanocomposite Powder Metallurgy Electrodes: a Taguchi and ANN-based Approach

K. Reetabai, Department of Biotechnology, Karpaga Vinayaga College of Engineering and Technology, Chengalpattu, TN, India V. Naveenprabhu, Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, TN, India Rajesh Mohan, Department of Mechanical Engineering, Karpagam College of Engineering, Coimbatore, TN, India Vinitha Nithianantharaj, Department of Artificial Intelligence and Data Science, Sri Sai Ram Institute of Technology, West Tambaram, Chennai, TN, India Venkat Prasat Sridhar, Department of Mechanical Engineering, Rajalakshmi Engineering College, Thandalam, Chennai, TN, India S. Naveen, naveenmurali33@gmail.com
Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, TN, India Justin Sam George Department of Civil Engineering, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany

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Abstract

In this research study, a novel Al–Cu–SiC composite tool, created using the powder metallurgy (P/M) technique, was used for the electrical discharge machining of D2 steel, and the effects of important process parameters were investigated. The experimental runs were organized using a Taguchi-based design, and prediction models were constructed using artificial neural network techniques. This study was focused on machining performance, employing material removal rate (MRR) and electrode wear rate (EWR) as essential performance indicators and peak current, dielectric flushing pressure, and pulse on time as crucial input parameters. To evaluate the significance and suitability of the regression models created, an Analysis of Variance was conducted. The results showed that Al–Cu–SiC P/M electrodes were more sensitive to peak current and pulse duration. Increasing pulse duration significantly influenced MRR and EWR, with optimal values (MRR of 0.5880 g/min and minimal EWR) attained at a current of 9 A and a pulse length of 80 μs. Taguchi analysis identified pulse duration as the primary determinant; Regression equations emphasized pulse duration and current as critical factors for MRR, whereas ANN optimization effectively forecasted EDM results (R > 0.97), demonstrating decreased errors and reliable model performance across datasets.

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