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.
