Laser welding has been widely used in aerospace automotive and other fields due to its advantages of high weld seam quality

high processing efficiency

and low heat-affected zone. During the welding process

fluctuations on the surface of the molten pool directly affect the quality of the weld formation

making it significant to investigate the flow behavior of the shallow melt. In this study

a small amount of aluminum oxide (Al

2

O

3

) particles was introduced as tracers during welding. By combining image processing and machine learning methods

the motion trajectories of surface oxides in the molten pool were extracted and analyzed

quantifying the changes in surface melt flow velocity at different penetration stages. By correlating f

eatures such as temperature

spatter

and molten pool spot frequency

the periodic variation patterns of vortices on both sides of the molten pool surface during complete penetration stage were revealed

and their respective correlations with spatter behavior and keyhole breakthrough phenomena were illustrated. This study provides valuable references for analyzing the surface flow of shallow molten pools and optimizing weld surface quality.