Machine learning for molten pool dynamics prediction in laser manufacturing

This study investigates the influence of line energy density, power and scanning speed on the molten pool geometry and maximum solute concentration obtained under steady-state conditions in hybrid laser–MIG welding of aluminum alloys. A comprehensive numerical model incorporating multiple reflections, Fresnel absorption, and laser–arc coupling was established to simulate the thermo-fluid behavior during the welding process. Key characteristics, including molten pool width, height, depth, and magnesium concentration, were quantitatively analyzed. To enhance predictive capability and improve process optimization efficiency, the Support Vector Regression (SVR) method was introduced to develop a machine learning model. The results demonstrate that SVR effectively captures the nonlinear relationship between line energy density, power, scanning speed and molten pool responses, achieving high predictive accuracy across multiple target variables and providing a reliable basis for process parameter optimization and weld quality improvement.