Multi-Physics Investigations on the Gas-Powder Flow and the Molten Pool Dynamics During Directed Energy Deposition Process

被引:4
作者
Duan, Chenghong [1 ]
Cao, Xiankun [1 ]
Luo, Xiangpeng [1 ]
Shang, Dazhi [1 ]
Hao, Xiaojie [1 ]
机构
[1] Beijing Univ Chem Technol, Coll Mech & Elect Engn, Beijing 100029, Peoples R China
来源
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME | 2023年 / 145卷 / 08期
基金
中国国家自然科学基金;
关键词
gas-powder flow; molten pool dynamics; heat and mass transfer; pore defect; directed energy deposition; additive manufacturing; advanced materials and processing; CAD/CAM/CAE; modeling and simulation; HEAT-TRANSFER; LASER; SIMULATION; POROSITY; PREDICTION; TRANSPORT; BEHAVIOR; ALLOY;
D O I
10.1115/1.4062259
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In order to establish a high-fidelity mechanism model for investigating the molten pool behaviors during directed energy deposition (DED) process, a molten pool dynamics model combined with the discrete element method is developed in the present study. The proposed model contains several newly added particle sources to further intuitively reproduce the interaction between the discrete powder particles and the molten pool. Meanwhile, the effects of the nozzle structure, carrier gas, and shielding gas on the feedstock feeding process are simulated in detail using the gas-powder flow model based on the multiphase flow theory. The gas-powder flow model is used to provide the reasonable outlet velocities, focal distance, and radius of the focal point for the particle sources in the molten pool dynamics model, which solves the difficulty that the motion state of the powder streams obtained by the molten pool dynamics simulation is hard to reproduce the actual situation. Besides, relevant experiments are conducted to verify the developed models. The predicted parameters of the powder streams are consistent with the experiment, and the deviations of the predicted molten pool dimensions are less than 10%. The heat and mass transfer phenomena inside the molten pool are also revealed. Furthermore, the maximum size of the spherical pore defects is predicted to be 18.6 mu m, which is underestimated by 7% compared to the microscopic observation. Altogether, the numerical methods developed in this study could further augment and improve the samples for the machine learning modeling of DED process.
引用
收藏
页数:13
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