Predictive modeling of MRR, TWR, and SR in spark-EDM of Al-4.5Cu–SiC using ANN and GEP

Abstract

In this study, Al-4.5Cu alloy was reinforced with varying weight percentages of SiC particles (2%, 4%, 6%, and 8%) to create metal matrix composites via the stir casting method. The formation of intermetallic compounds was confirmed through energy dispersive spectroscopy and x-ray diffraction analysis. This article compares the performance of Artificial Neural Network (ANN) and Gene Expression Program ming (GEP) models in predicting the Metal Removal Rate (MRR), tool wear rate, and surface roughness in the die-sinking electro-discharge machining (EDM) process of the ex-situ developed Al-4.5%Cu–SiC composites. The study considers three machine parameters—pulse on time (TON), pulse off time (TOFF), and current (I)—along with the weight fraction of SiC particles as input variables for the models. Both ANNand GEP models demonstrated high predictive accuracy for the EDM performance metrics, with correlation coefficients (R) ranging from 0.97368 to 0.98065 for the ANN model and 0.98011 to 0.98259 for the GEP model. Notably, the GEP model exhibited superior pre dictive capability, as evidenced by its higher correlation coefficients and lower root mean square error, indicating greater effectiveness in predicting the EDM process outcomes than the ANN model.