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

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...

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Veröffentlicht in:Journal of manufacturing science and engineering 2023-08, Vol.145 (8)
Hauptverfasser: Duan, Chenghong, Cao, Xiankun, Luo, Xiangpeng, Shang, Dazhi, Hao, Xiaojie
Format: Artikel
Sprache:eng
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Zusammenfassung: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 multi-phase 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 µm, 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.
ISSN:1087-1357
1528-8935
DOI:10.1115/1.4062259