Laser Material Interaction

Laser material interaction research focuses on understanding and controlling the complex physical processes occurring when lasers interact with various materials, aiming to optimize manufacturing processes and improve material properties. Current research emphasizes the use of advanced modeling techniques, including multi-physics simulations and machine learning algorithms like convolutional neural networks and decision trees, to analyze melt pool dynamics, predict defects (e.g., spatter, cracks, pores), and optimize process parameters in applications such as laser powder bed fusion and laser directed energy deposition. These advancements enable real-time process monitoring and control, leading to improved quality and efficiency in additive manufacturing and other laser-based technologies, such as robotic laser surgery where precise temperature control is crucial. The development of in-situ monitoring techniques using acoustic signals and high-speed imaging further enhances the ability to understand and control these interactions.