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An end-to-end welding defect detection approach based on titanium alloy time-of-flight diffraction images

Author

Listed:
  • Zelin Zhi

    (Xi’an Jiaotong University
    Shaanxi Special Equipment Inspection and Testing Institute)

  • Hongquan Jiang

    (Xi’an Jiaotong University)

  • Deyan Yang

    (Xi’an Jiaotong University)

  • Jianmin Gao

    (Xi’an Jiaotong University)

  • Quansheng Wang

    (Shaanxi Special Equipment Inspection and Testing Institute)

  • Xiaoqiao Wang

    (Shaanxi Special Equipment Inspection and Testing Institute)

  • Jingren Wang

    (Shaanxi Special Equipment Inspection and Testing Institute)

  • Yongxiang Wu

    (Shaanxi Special Equipment Inspection and Testing Institute)

Abstract

The weld defect recognition of titanium alloy is of great significance for ensuring the safety and reliability of equipment. This study proposes a method based on the enlighten faster region-based convolutional neural network (EFRCNN) to recognize titanium alloy weld defects. First, by designing defect test blocks and using probes with different frequencies, a dataset of time-of-flight diffraction (TOFD) weld defect detections is constructed. Next, to overcome the problems of high data noise and low recognition accuracy, a parallel series multi-scale feature information fusion mechanism and a channel domain attention strategy are designed, and a deep learning network model based on the faster region-based convolution neural network (Faster R-CNN) is constructed. Finally, the proposed method is verified by the TOFD test data of titanium alloy welds. The results show that the proposed method can achieve a defect type recognition accuracy of more than 92%, especially in detecting cracks or a lack of fusion.

Suggested Citation

  • Zelin Zhi & Hongquan Jiang & Deyan Yang & Jianmin Gao & Quansheng Wang & Xiaoqiao Wang & Jingren Wang & Yongxiang Wu, 2023. "An end-to-end welding defect detection approach based on titanium alloy time-of-flight diffraction images," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1895-1909, April.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01905-w
    DOI: 10.1007/s10845-021-01905-w
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    References listed on IDEAS

    as
    1. Hongquan Jiang & Rongxi Wang & Zhiyong Gao & Jianmin Gao & Hongye Wang, 2019. "Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 2013-2024, April.
    2. Lu Yang & Hongquan Jiang, 2021. "Weld defect classification in radiographic images using unified deep neural network with multi-level features," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 459-469, February.
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