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A normal weld recognition method for time-of-flight diffraction detection based on generative adversarial network

Author

Listed:
  • Hongquan Jiang

    (Xi’an Jiaotong University)

  • Deyan Yang

    (Xi’an Jiaotong University)

  • Zelin Zhi

    (Xi’an Jiaotong University)

  • Qiangzheng Jing

    (Xi’an Special Equipment Inspection Institute)

  • Jianmin Gao

    (Xi’an Jiaotong University)

  • Chenyue Tao

    (Xi’an Jiaotong University)

  • Zhixiang Cheng

    (Xi’an Jiaotong University)

Abstract

Time-of-flight diffraction (TOFD) has become a widely used nondestructive testing (NDT) technique, owing to its wide coverage, fast detection speeds, and high defect detection rates. However, compared with nondestructive radiographic testing images, TOFD image analysis requires more technicians and more difficult defect analysis. Owing to the improvements in weld manufacturing quality, there are fewer welds with defects; consequently, a large number of TOFD images have no defect information. The TOFD image analysis of normal welds occupies a lot of time in the weld evaluation process that easily leads to problems of missed and false detections and reduces the efficiency of overall weld evaluation. To solve these problems, a TOFD image reconstruction model based on the generative adversarial network (GAN) and a normal weld recognition method are proposed. First, combined with the TOFD image characteristics, an image-wave feature fusion (IWFF) module based on depth-separable convolution is designed, which integrates and analyzes the TOFD image and wave features, and an IWFF–GAN model is developed. Second, to improve the accuracy of normal weld recognition, a method for denoising the reconstructed error-feature map based on the total variation model is proposed. Finally, the proposed method is verified using the TOFD images of large-scale spherical pressure-tank welds. The results show that the method accurately distinguishes between the normal and abnormal welds, exhibiting a higher normal weld recognition accuracy. The area under the receiver operating characteristic curve is 0.9903.

Suggested Citation

  • Hongquan Jiang & Deyan Yang & Zelin Zhi & Qiangzheng Jing & Jianmin Gao & Chenyue Tao & Zhixiang Cheng, 2024. "A normal weld recognition method for time-of-flight diffraction detection based on generative adversarial network," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 217-233, January.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:1:d:10.1007_s10845-022-02041-9
    DOI: 10.1007/s10845-022-02041-9
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    References listed on IDEAS

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    1. Ahmad Barari & Marcos Sales Guerra Tsuzuki & Yuval Cohen & Marco Macchi, 2021. "Editorial: intelligent manufacturing systems towards industry 4.0 era," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1793-1796, October.
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