Investigating Vibration Energy Density of AA7050-SiC/Graphite Composite Using Electric Discharge Machining based on Hybrid AI Modeling and RSM

Electric Discharge Machining (EDM) is widely used for machining hard materials but faces challenges with inefficient debris removal and unstable discharge behavior. Vibration-assisted EDM improves flushing and spark stability, but a unified process vibration metric and accurate prediction framework are lacking. This study presents an interpretable, Response Surface Methodology (RSM)-driven optimization framework combined with Deep Learning (DL) for vibration-assisted EDM of AA7050-SiC/Graphite hybrid composites. Leveraging internship experience at Titan Engineering and Automation Ltd, the research integrates RSM for statistical modeling and a hybrid system combining Kolmogorov Arnold Informed Neural Network (KAINN) to capture complex discharge-vibration interactions. Partial Dependence Plots (PDP) offer interpretable feature effects, while Nutcracker Optimization Algorithm (NOA) enables multi-response optimization. Factors such as peak current, pulse on time, vibration amplitude, and frequency are optimized to predict Vibration Energy Density (VED), Modulated Frequency Ratio (MFR), Material Removal Rate (MRR), and Tool Wear Rate (TWR). Experimental results show response variations with VED ranging from 0.15 to 4.69 J/mm 3 and MRR up to 4.6 mm 3 /min. The hybrid model achieved high prediction accuracy (R 2 > 0.98) and minimal error (0.05 MSE). NOA optimization identified settings yielding high VED (3.68 J/mm 3 ), MRR (3.27 mm 3 /min), and low TWR (0.22 mm 3 /min), enhancing machining efficiency.