Dynamics of the laser–powder interaction in the Ti6Al4V powder feeding process of laser-directed energy deposition additive manufacturing

材料科学 表面张力 激光器 钛合金 金属粉末 复合材料 合金 冶金 光学 热力学 物理 金属
作者
Chunchi Lv,Jiayue Wang,Hui Li,Qianxing Yin,Wenjie Liu,Shengnan Shen
出处
期刊:Journal of materials research and technology [Elsevier BV]
卷期号:27: 6376-6385 被引量:16
标识
DOI:10.1016/j.jmrt.2023.11.099
摘要

Laser-directed energy deposition (L-DED) utilizes a high-power laser and coaxial powder feed to rapidly fabricate high-quality components with intricate structures. However, the complex interaction between the powder stream and laser beam is not yet fully understood. Particularly, the morphological evolution of the droplets after powder melting is closely related to the quality of component formation. In this study, the dynamical mechanism of the aggregation and evolution of molten powder after laser–powder interaction was investigated. The Ti6Al4V powder flow was observed at various laser powers using a high-speed camera. Imaging of the molten droplets at approximately 50,000 frames·s−1 was conducted, depicting the aggregation of molten powder into droplets of different sizes and shapes, mainly consisting of spherical and ribbon droplets, after laser–powder interaction. A transient numerical model was established with consideration of the mutual influence of gravity, recoil pressure, and surface tension, depicting the temporal evolution of the temperature field and droplet morphology. The simulation results indicated that the balance between the surface tension, gravity, and recoil pressure determined the morphological evolution of the molten droplets. When spherical and ribbon droplets fell into the molten pool, the maximum velocities within the pool increased by 24 % and 69 %, respectively. This phenomenon reduced the stability of the molten pool to a certain extent and affected the print quality of the part. This study deepens our comprehension of the dynamic mechanisms of Ti6Al4V alloy powder melting evolution, providing invaluable insights for optimizing L-DED processes.
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