Effect of spreading of the melt pool on the deposition characteristics in laser directed energy deposition

被引:4
|
作者
Vundru, Chaitanya [1 ,2 ]
Singh, Ramesh [1 ]
Yan, Wenyi [2 ]
Karagadde, Shyamprasad [1 ]
机构
[1] Indian Inst Technol, Dept Mech Engn, Mumbai 400076, Maharashtra, India
[2] Monash Univ, Dept Mech & Aerosp Engn, Clayton Melbourne, Vic 3800, Australia
来源
49TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 49, 2021) | 2021年 / 53卷
关键词
Laser directed energy deposition; spreading; deposition geometry; computational fluid dynamics; ANALYTICAL-MODEL; PROCESS MAPS; PHASE-CHANGE; ATTENUATION; EFFICIENCY; CATCHMENT;
D O I
10.1016/j.promfg.2021.06.043
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Laser direct energy deposition (DED) is an innovative additive manufacturing technology with tremendous potential for remanufacturing and restoration of dies, molds, and components for aerospace. Accurate prediction of the deposition geometry is important to control the repair quality. In laser DED, the powder particles are fused onto the melt pool formed on the substrate surface. To estimate the deposition geometry estimating the spreading and solidification of molten pool is of utmost importance. Therefore, a computation fluid dynamics (CFD) model has been developed to estimate the melt pool size on the substrate and the spreading of the melt pool over the substrate before solidification. The model can be also used to estimate the catchment efficiency and deposition geometry. The experimental observations of solidified deposition geometry on a substrate corroborate the proposed model. The CFD model is utilized to develop data-driven models correlating the process parameters with the deposition geometry. This study, observes that larger the width of the deposition, important is the inclusion of the spreading of melt pool in the estimation of the deposition geometry. (C) 2021 The Authors. Published by Elsevier B.V.
引用
收藏
页码:407 / 416
页数:10
相关论文
共 50 条
  • [1] Melt pool morphology in directed energy deposition additive manufacturing process
    Chen, Y.
    Clark, S.
    Leung, A. C. L.
    Sinclair, L.
    Marussi, S.
    Atwood, R.
    Connoley, T.
    Jones, M.
    Baxter, G.
    Lee, P. D.
    INTERNATIONAL CONFERENCE ON MODELLING OF CASTING, WELDING AND ADVANCED SOLIDIFICATION PROCESSES (MCWASP XV), 2020, 861
  • [2] Thermoelectric magnetohydrodynamic control of melt pool flow during laser directed energy deposition additive manufacturing
    Fan, Xianqiang
    Fleming, Tristan G.
    Rees, David T.
    Huang, Yuze
    Marussi, Sebastian
    Leung, Chu Lun Alex
    Atwood, Robert C.
    Kao, Andrew
    Lee, Peter D.
    ADDITIVE MANUFACTURING, 2023, 71
  • [3] Melt Pool Size Extraction Method in Laser Directed Energy Deposition Based on Edge Gradient Search
    Miao L.
    Xing F.
    Shi J.
    Chai Y.
    Yan C.
    Bian H.
    Sun H.
    Cailiao Daobao/Materials Reports, 2024, 38 (02):
  • [4] Effects of Laser-Powder Alignment on Clad Dimension and Melt Pool Temperature in Directed Energy Deposition
    Jeong, Jihoon
    Webster, Samantha
    Zha, Rujing
    Mogonye, Jon-Erik
    Ehmann, Kornel
    Cao, Jian
    JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2024, 146 (01):
  • [5] Closed loop control of melt pool width in robotized laser powder–directed energy deposition process
    Meysam Akbari
    Radovan Kovacevic
    The International Journal of Advanced Manufacturing Technology, 2019, 104 : 2887 - 2898
  • [6] Classification of melt pool states for defect detection in laser directed energy deposition using FixConvNeXt model
    Zeng, Xinxin
    Peng, Shitong
    Guo, Jianan
    Chen, Guiying
    Tang, Jian
    Wang, Fengtao
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2025, 36 (01)
  • [7] Modelling particle impact on the melt pool and wettability effects in laser directed energy deposition additive manufacturing
    Haley, James C.
    Schoenung, Julie M.
    Lavernia, Enrique J.
    MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2019, 761
  • [8] Prediction of melt pool temperature in directed energy deposition using machine learning
    Zhang, Ziyang
    Liu, Zhichao
    Wu, Dazhong
    ADDITIVE MANUFACTURING, 2021, 37
  • [9] Observations of particle-melt pool impact events in directed energy deposition
    Haley, James C.
    Schoenung, Julie M.
    Lavernia, Enrique J.
    ADDITIVE MANUFACTURING, 2018, 22 : 368 - 374
  • [10] High fidelity model of directed energy deposition: Laser-powder-melt pool interaction and effect of laser beam profile on solidification microstructure
    Khairallah, Saad A.
    Chin, Eric B.
    Juhasz, Michael J.
    Dayton, Alan L.
    Capps, Arlie
    Tsuji, Paul H.
    Bertsch, Kaila M.
    Perron, Aurelien
    Mccall, Scott K.
    Mckeown, Joseph T.
    ADDITIVE MANUFACTURING, 2023, 73