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

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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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61727802, 61901220) and the China Postdoctoral Science Foundation(2021M691591).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xingguo Wang, Tianyun Chen, Yiming Wang, Dongliang Zheng, Xiaoyu Chen & Zhuang Zhao

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  1. Xingguo Wang
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  2. Tianyun Chen
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  3. Yiming Wang
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  4. Dongliang Zheng
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  5. Xiaoyu Chen
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  6. Zhuang Zhao
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Correspondence to Xiaoyu Chen or Zhuang Zhao.

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Wang, X., Chen, T., Wang, Y. et al. The 3D narrow butt weld seam detection system based on the binocular consistency correction. J Intell Manuf 34, 2321–2332 (2023). https://doi.org/10.1007/s10845-022-01927-y

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