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Weld defect classification in radiographic images using unified deep neural network with multi-level features

Author

Listed:
  • Lu Yang

    (Xi’an Jiaotong University)

  • Hongquan Jiang

    (Xi’an Jiaotong University
    Massachusetts Institute of Technology)

Abstract

Deep neural network (DNN) exhibits state-of-the-art performance in many fields including weld defect classification. However, there is still a large room for improving the classification performance over the generic DNN models. In this paper, a unified deep neural network with multi-level features is proposed for weld defect classification. Firstly, we define 11 weld defect features as inputs of our proposed classification model. Not limited to geometric and intensity features, 4 features based on the intensity contrast between weld defect and its background are proposed in this paper. Secondly, we construct a novel deep learning framework: a unified deep neural network, where multi-level features of each hidden layer are fused by the last hidden layer to predict the type of weld defect comprehensively. In addition, we investigate pre-training and fine-turning strategies to get better generalization performance with small dataset. Comparing with other classification methods like SVM and generic DNN model, our framework takes full advantage of multi-level features extracted from each hidden layer, an outstanding performance is shown where the classification accuracy is improved by 3.18% and 4.33% on the test dataset, to reach 91.36%.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01581-2
    DOI: 10.1007/s10845-020-01581-2
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Deyuan Ma & Ping Jiang & Leshi Shu & Zhaoliang Gong & Yilin Wang & Shaoning Geng, 2024. "Online porosity prediction in laser welding of aluminum alloys based on a multi-fidelity deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 55-73, January.
    2. Feng Huang & Ben-wu Wang & Qi-peng Li & Jun Zou, 2023. "Texture surface defect detection of plastic relays with an enhanced feature pyramid network," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1409-1425, March.
    3. 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.

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