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The 3D narrow butt weld seam detection system based on the binocular consistency correction

Author

Listed:
  • Xingguo Wang

    (Nanjing University of Science and Technology)

  • Tianyun Chen

    (Nanjing University of Science and Technology)

  • Yiming Wang

    (Nanjing University of Science and Technology)

  • Dongliang Zheng

    (Nanjing University of Science and Technology)

  • Xiaoyu Chen

    (Nanjing University of Science and Technology)

  • Zhuang Zhao

    (Nanjing University of Science and Technology)

Abstract

Detecting narrow butt weld seam with high precision has become an urgent problem with the wide application of laser welding technology. Many previous methods use line laser to locate the welds. However, these methods can only get a single position of the weld seam in each shooting and the detection scope is limited to the laser projection area, leading to low detection efficiency. To extract the narrow butt welds more efficiently, this paper combines the passive methods with the active methods, and proposes a 3D narrow butt weld seam detection system based on the binocular consistency analysis. Specifically, the active light method of fringe projection profilometry is adopted to capture the 3D information of the weldment. The weld seam extraction network based on binocular spatial information mining (BSMNet) is designed to analyze the corresponding passive light data and locate the weld seam position. Besides, a data annotation method based on binocular consistency correction is proposed to achieve more accurate data annotation for the BSMNet training. The experimental results show the max error of the detection is about 0.081mm, and the mean error is about 0.021mm.

Suggested Citation

  • Xingguo Wang & Tianyun Chen & Yiming Wang & Dongliang Zheng & Xiaoyu Chen & Zhuang Zhao, 2023. "The 3D narrow butt weld seam detection system based on the binocular consistency correction," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2321-2332, June.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01927-y
    DOI: 10.1007/s10845-022-01927-y
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    References listed on IDEAS

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    1. Yanxi Zhang & Deyong You & Xiangdong Gao & Congyi Wang & Yangjin Li & Perry P. Gao, 2020. "Real-time monitoring of high-power disk laser welding statuses based on deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 799-814, April.
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