Transferring melt pool knowledge between multiple materials in laser-directed energy deposition via Gaussian process regression
Author:
Funder
Engineer Research and Development Center
U.S. Department of Energy
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00366-024-02029-4.pdf
Reference52 articles.
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2. Caiazzo F, Caggiano A (2018) Laser direct metal deposition of 2024 Al alloy: trace geometry prediction via machine learning. Materials 11:444. https://doi.org/10.3390/ma11030444
3. DebRoy T, Wei HL, Zuback JS, Mukherjee T, Elmer JW, Milewski JO, Beese AM, Wilson-Heid AD, De A, Zhang W (2018) Additive manufacturing of metallic components–process, structure and properties. Prog Mater Sci 92:112–224. https://doi.org/10.1016/j.pmatsci.2017.10.001
4. Wei C, Zhang Z, Cheng D, Sun Z, Zhu M, Li L (2020) An overview of laser-based multiple metallic material additive manufacturing: from macro-to micro-scales. Int J Extreme Manuf 3:012003. https://doi.org/10.1088/2631-7990/abce04
5. Svetlizky D, Das M, Zheng B, Vyatskikh AL, Bose S, Bandyopadhyay A, Schoenung JM, Lavernia EJ, Eliaz N (2021) Directed energy deposition (DED) additive manufacturing: physical characteristics, defects, challenges and applications. Mater Today 49:271–295. https://doi.org/10.1016/j.mattod.2021.03.020
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