Realizing the Potential of Physics-Informed Neural Network in Modelling Laser Drilling Process

This research introduces a novel paradigm by integrating physics-based models with neural networks, forming a cohesive and adaptive framework for laser drilling.The Physics-Informed Neural Network (PINN) leverages the underlying physical principles governing laser-material interactions to enhance the predictive accuracy of drilling outcomes.Through meticulous training on diverse datasets encompassing material properties, laser parameters, and resultant hole geometries, the PINN model exhibits a remarkable ability to generalize across varied conditions.Here, the Artificial Neural Network's (ANN) inputs are spatial and temporal coordinates inside the domain, on the boundaries and at initial moment.The output is the shape of laser drilled hole.During the training step, the output values, and their derivatives w.r.t.inputs are calculated and afterwards they are used to evaluate the loss value.Through the gradient-based optimization method, the weights of network are changed to reach minimum loss value.The trained model predicts the unknown coefficient in the physical governing equation, which here is ablation threshold intensity.Our study investigates the effectiveness of the PINN approach in optimizing laser drilling parameters and achieving superior accuracy.We demonstrate its robustness in predicting drilling outcomes for different laser configurations.This research contributes not only to the advancement of laser precision microfabrication but also to the broader field of computational physics.By seamlessly integrating data-driven approaches with fundamental physical insights, the PINN methodology offers a transformative pathway to optimize laser drilling processes, paving the way for enhanced efficiency and quality in microfabrication applications.The insights presented herein promise to shape the future of laser microfabrication, fostering innovation and pushing the boundaries of precision engineering.