AI-enabled modeling and simulation for optimized laser material processing

In the domain of material processing, lasers offer a unique blend of precision and throughput. However, optimizing the machining recipes remains challenging due to complex laser-matter interactions. This optimization currently relies on expensive and time-consuming trial-and-error approaches. We propose an advanced modeling and simulation framework for laser-enabled material processing, leveraging Artificial Intelligence (AI) to predict laser-matter interactions. Our approach involves training a Machine Learning (ML) algorithm on a dataset of pre- and post-laser pulse surface characteristics obtained through multimodality microscopy. The ML algorithm predicts the effects of laser pulses on surfaces, considering the cumulative impact of sequential pulses and temporal spacing. This AI-enabled approach reduces the need for trial and error, offering an efficient pathway to optimized machining recipes. Our framework has the potential to revolutionize laser material processing, enhancing precision, reducing costs, and accelerating the development of machining protocols. This advancement opens new avenues for innovative applications in various industries.