- Additive Manufacturing Optimization of Directed Energy Deposition-Processed Ti-6Al-4V Alloy using Energy Density and Powder Deposition Density
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Yukyeong Lee, Eun Sung Kim, Se-Ho Chun, Jae Bok Seol, Hyokyung Sung, Jung Seok Oh, Hyoung Seop Kim, Taekyung Lee, Tae-Hyun Nam, Jung Gi Kim
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J Korean Powder Metall Inst. 2021;28(6):491-496. Published online December 1, 2021
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DOI: https://doi.org/10.4150/KPMI.2021.28.6.491
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The process optimization of directed energy deposition (DED) has become imperative in the manufacture of reliable products. However, an energy-density-based approach without a sufficient powder feed rate hinders the attainment of an appropriate processing window for DED-processed materials. Optimizing the processing of DEDprocessed Ti-6Al- 4V alloys using energy per unit area (Eeff) and powder deposition density (PDDeff) as parameters helps overcome this problem in the present work. The experimental results show a lack of fusion, complete melting, and overmelting regions, which can be differentiated using energy per unit mass as a measure. Moreover, the optimized processing window (Eeff = 44~47 J/mm2 and PDDeff = 0.002~0.0025 g/mm2) is located within the complete melting region. This result shows that the Eeff and PDDeff-based processing optimization methodology is effective for estimating the properties of DED-processed materials. -
Citations
Citations to this article as recorded by  - Cryogenic Tensile Behavior of Ferrous Medium-entropy Alloy Additively Manufactured by Laser Powder Bed Fusion
Seungyeon Lee, Kyung Tae Kim, Ji-Hun Yu, Hyoung Seop Kim, Jae Wung Bae, Jeong Min Park
journal of Korean Powder Metallurgy Institute.2024; 31(1): 8. CrossRef
- Modeling the Density and Hardness of AA2024-SiC Nanocomposites
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A-Hyun Jeon, Hong In Kim, Hyokyung Sung, N. S. Reddy
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J Korean Powder Metall Inst. 2019;26(4):275-281. Published online August 1, 2019
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DOI: https://doi.org/10.4150/KPMI.2019.26.4.275
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An artificial neural network (ANN) model is developed for the analysis and simulation of correlation between flake powder metallurgy parameters and properties of AA2024-SiC nanocomposites. The input parameters of the model are AA 2024 matrix size, ball milling time, and weight percentage of SiC nanoparticles and the output parameters are density and hardness. The model can predict the density and hardness of the unseen test data with a correlation of 0.986 beyond the experimental data. A user interface is designed to predict properties at new instances. We have used the model to simulate the individual as well as the combined influence of parameters on the properties. Moreover, we have analyzed the calculated results from the powder metallurgical point of view. The developed model can be used as a guide for further composite development.
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