Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF

被引:18
|
作者
Zhang, Haolin [1 ]
Vallabh, Chaitanya Krishna Prasad [1 ]
Zhao, Xiayun [1 ]
机构
[1] Univ Pittsburgh, Dept Mech Engn & Mat Sci, Pittsburgh, PA 15261 USA
关键词
Laser powder bed fusion; In-situ melt pool monitoring; Data registration; Machine Learning; Surface topography;
D O I
10.1016/j.addma.2022.103075
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In-situ monitoring technologies for laser powder bed fusion (LPBF) additive manufacturing often face one key challenge, extracting the ultrafast melt pool (MP) signatures for understanding the localized part properties. Further, the spatial information of each monitored MP signature is essential for correlating the MP - part property. This spatial information is often unavailable especially from commercial LPBF printers. Many MP monitoring methods have been reported and utilized. However, very few of these have the MP's spatial information. To overcome this challenge, in this work we report a method for spatially registering the key MP signatures (MP intensity, temperature, and area) to the monitored print parts. The MP signatures are obtained from our coaxial high-speed single-camera based two-wavelength imaging pyrometry (STWIP) system and the MP spatial information is obtained from an off-axis camera system. A machine learning aided image analysis method is employed to retrieve the spatial distribution of MPs within the corresponding part's coordinates system. Then, the MP signature maps (MPSMs) are reconstructed by mapping the STWIP measured MP signatures to the registered MP coordinates. Further, a long short-term memory (LSTM) neural network is developed for estimating the layer surface topography from the registered MPSMs. The obtained results indicate that the layer surface topography can be more accurately estimated by using MP temperature signature rather than MP intensity and/or area signatures as in common practice. Our developed methods for MP monitoring, registration, and MP-surface topography prediction offer advanced capabilities for the online detection of process anomalies and part defects.
引用
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页数:16
相关论文
共 22 条
  • [1] Prediction of lack of fusion porosity in selective laser melting based on melt pool monitoring data
    Coeck, Sam
    Bisht, Manisha
    Plas, Jan
    Verbist, Frederik
    [J]. ADDITIVE MANUFACTURING, 2019, 25 : 347 - 356
  • [2] Data fusion and bias registration based on sensor selection for large-scale sensor networks
    Guo, Junjun
    Han, Chongzhao
    Li, Longfei
    [J]. 2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 7286 - 7291
  • [3] CAMERA-BASED COAXIAL MELT POOL MONITORING DATA REGISTRATION FOR LASER POWDER BED FUSION ADDITIVE MANUFACTURING
    Lu, Yan
    Yang, Zhuo
    Kim, Jaehyuk
    Cho, Hyunbo
    Yeung, Ho
    [J]. PROCEEDINGS OF THE ASME 2020 INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, IMECE2020, VOL 2B, 2020,
  • [4] Effects of large-scale surface topography on ground motions, as demonstrated by a study of the San Gabriel Mountains, Los Angeles, California
    Ma, Shuo
    Archuleta, Ralph J.
    Page, Morgan T.
    [J]. BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, 2007, 97 (06) : 2066 - 2079
  • [5] Deep learning-based data registration of melt-pool-monitoring images for laser powder bed fusion additive manufacturing
    Kim, Jaehyuk
    Yang, Zhuo
    Ko, Hyunwoong
    Cho, Hyunbo
    Lu, Yan
    [J]. JOURNAL OF MANUFACTURING SYSTEMS, 2023, 68 : 117 - 129
  • [6] PRELIMINARY EXPERIMENTS IN NUMERICAL PREDICTION OF LARGE-SCALE SEA-SURFACE TEMPERATURE ANOMALIES
    HANEY, RL
    HUNT, KH
    [J]. TRANSACTIONS-AMERICAN GEOPHYSICAL UNION, 1975, 56 (12): : 1007 - 1007
  • [7] Ladle Furnace Temperature Prediction Model Based on Large-scale Data With Random Forest
    Wang, Xiaojun
    [J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2017, 4 (04) : 770 - 774
  • [9] UNSTEADY BLADE-SURFACE PRESSURES ON A LARGE-SCALE ADVANCED PROPELLER - PREDICTION AND DATA
    NALLASAMY, M
    GROENEWEG, JF
    [J]. JOURNAL OF PROPULSION AND POWER, 1991, 7 (06) : 866 - 872
  • [10] A Multimodal Data Fusion and Deep Learning Framework for Large-Scale Wildfire Surface Fuel Mapping
    Alipour, Mohamad
    La Puma, Inga
    Picotte, Joshua
    Shamsaei, Kasra
    Rowell, Eric
    Watts, Adam
    Kosovic, Branko
    Ebrahimian, Hamed
    Taciroglu, Ertugrul
    [J]. FIRE-SWITZERLAND, 2023, 6 (02):