AI-Driven Framework for Optimizing and Predicting Hole Geometry in Multilayer Composite Substrates for Laser Drilling

In laser drilling, precise parameter optimization is essential to achieving the desired hole characteristics. This study investigates the influence of the pulse width and pulse spacing on the machined hole geometry and proposes an artificial intelligence-based framework to predict hole shapes in multilayer composite substrates. The distribution of hole diameters resulting from CO 2 laser machining was evaluated via response surface methodology, considering variations in the pulse width and irradiation time. The results demonstrated a strong dependency of the hole diameters on the laser conditions and revealed significant autocorrelation among the machined-hole parameters.