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Automatic Recognition of Welding Seam Defects in TOFD Images Based on TensorFlow

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Abstract

This study is aimed to analyze the characteristics of different kinds of time-of-flight diffraction (TOFD) images for welding seam defects. Combined with the image recognition technology of artificial intelligence, a deep learning neural network program based on TensorFlow was developed and applied to the training and recognition of welding seam defects in ultrasonic TOFD images. The results showed that, after training, the program could identify typical welding seam defects such as stoma, crackle, slag inclusion, lack of fusion, and incomplete penetration in TOFD images of the welding seam, and the recognition confidence was more than 0.8. This proved that the program developed in this study provided an effective reference for determining typical welding seam defects in TOFD images of the welding seam.

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Authors and Affiliations

  1. Beijing no. 3 Construction Engineering Co., Ltd, Beijing, China

    Shuohui Chen & Xuan Teng

  2. Beijing Construction Engineering Group Co., Ltd, Beijing, China

    Xiuxing Sang

  3. China Agricultural University, Department of Civil Engineering, Beijing, China

    Huidi Zhang & Jinzhao Zhuang

Authors
  1. Shuohui Chen
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  2. Xuan Teng
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  3. Xiuxing Sang
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  4. Huidi Zhang
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  5. Jinzhao Zhuang
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Correspondence to Jinzhao Zhuang.

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Shuohui Chen, Teng, X., Sang, X. et al. Automatic Recognition of Welding Seam Defects in TOFD Images Based on TensorFlow. Aut. Control Comp. Sci. 56, 58–66 (2022). https://doi.org/10.3103/S0146411622010035

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