Optimization of Material Removal Rate and Electrode Wear Rate in EDM Machining of D2 Steel Using Al–Cu–SiC Nanocomposite P/M Electrodes: A Taguchi and ANN-Based Approach
J. Environ. Nanotechnol., Volume 13, No 4 (2024) pp. 499-509
Abstract
This research study uses a novel Al–Cu–SiC composite tool for EDM that was created using powder metallurgy (P/M) methods to investigate the effects of important process parameters on the electrical discharge machining of D2 steel. The experimental runs were organized using a Taguchi-based design, and prediction models were constructed using artificial neural network techniques. This study focuses on machining performance, employing material removal rate and tool wear rate 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 show that, Al–Cu–SiC P/M electrodes are more sensitive to peak current and pulse duration. Increasing pulse duration significantly influences 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 identifies pulse duration as the primary determinant. Regression equations emphasize pulse duration and current as critical factors for MRR, whereas ANN optimization effectively forecasts EDM results (R > 0.97), demonstrating decreased errors and reliable model performance across datasets.
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