• Chinese Journal of Lasers
  • Vol. 50, Issue 12, 1202108 (2023)
Yuhui Huang, Xi’an Fan, Yanxi Zhang, and Xiangdong Gao*
Author Affiliations
  • Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
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    DOI: 10.3788/CJL220922 Cite this Article Set citation alerts
    Yuhui Huang, Xi’an Fan, Yanxi Zhang, Xiangdong Gao. Online Detection of Root Hump in Laser‐MIG Hybrid Welding Based on Invariable Moment Characteristics of Molten Pool Image[J]. Chinese Journal of Lasers, 2023, 50(12): 1202108 Copy Citation Text show less
    Schematic of laser-MIG hybrid welding testing platform
    Fig. 1. Schematic of laser-MIG hybrid welding testing platform
    Schematics of laser-MIG hybrid welding molten pool. (a) Formation process of root hump pool; (b) formation process of full penetration defect pool
    Fig. 2. Schematics of laser-MIG hybrid welding molten pool. (a) Formation process of root hump pool; (b) formation process of full penetration defect pool
    Detection image in laser-MIG hybrid welding process
    Fig. 3. Detection image in laser-MIG hybrid welding process
    Image processing of laser-MIG hybrid welding molten pool. (a) Original image; (b) molten pool image after MSR processing; (c) image of solidified molten pool; (d) image of molten pool tail
    Fig. 4. Image processing of laser-MIG hybrid welding molten pool. (a) Original image; (b) molten pool image after MSR processing; (c) image of solidified molten pool; (d) image of molten pool tail
    Invariant moments and moving average values of molten pool tail images. (a) Invariant moment 1; (b) invariant moment 2; (c) invariant moment 3; (d) invariant moment 4
    Fig. 5. Invariant moments and moving average values of molten pool tail images. (a) Invariant moment 1; (b) invariant moment 2; (c) invariant moment 3; (d) invariant moment 4
    Boxplots of invariant moment of molten pool tail image. (a) Invariant moment 1; (b) invariant moment 2; (c) invariant moment 3; (d) invariant moment 4
    Fig. 6. Boxplots of invariant moment of molten pool tail image. (a) Invariant moment 1; (b) invariant moment 2; (c) invariant moment 3; (d) invariant moment 4
    One-dimensional convolutional neural network model for weld root hump detection
    Fig. 7. One-dimensional convolutional neural network model for weld root hump detection
    Training results of one-dimensional convolutional neural network model for weld root hump detection. (a) Accuracy curves of model; (b) loss curves of model; (c) learning rate curve of model
    Fig. 8. Training results of one-dimensional convolutional neural network model for weld root hump detection. (a) Accuracy curves of model; (b) loss curves of model; (c) learning rate curve of model
    Comparison of detection and real results of root hump
    Fig. 9. Comparison of detection and real results of root hump
    SampleNumber of testing samplesNumber of correct testing samplesAccuracy
    Sum9150866594.7%
    Root hump sample7209689095.6%
    Full penetration sample1941177591.4%
    Table 1. Detection accuracies of model under different test samples
    Yuhui Huang, Xi’an Fan, Yanxi Zhang, Xiangdong Gao. Online Detection of Root Hump in Laser‐MIG Hybrid Welding Based on Invariable Moment Characteristics of Molten Pool Image[J]. Chinese Journal of Lasers, 2023, 50(12): 1202108
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