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A deep convolutional network combining layerwise images and defect parameter vectors for laser powder bed fusion process anomalies classification

Author

Listed:
  • Zimeng Jiang

    (South China University of Technology)

  • Aoming Zhang

    (South China University of Technology)

  • Zhangdong Chen

    (South China University of Technology)

  • Chenguang Ma

    (South China University of Technology)

  • Zhenghui Yuan

    (South China University of Technology)

  • Yifan Deng

    (South China University of Technology)

  • Yingjie Zhang

    (South China University of Technology)

Abstract

Defect detection is an essential way to ensure the quality of parts made by laser powder bed fusion (LPBF) and industrial cameras are one of the commonly used tools for defect monitoring. Different lighting environments affect the visibility of defects in the images, and the illumination condition becomes one of the most important factors affecting the defect detection effect of industrial cameras, but the modification of the equipment lighting environment will increase the complexity and cost of monitoring. In this study, only an off-axis CMOS camera monitoring system is used and the lighting facilities are not changed to improve the effect of defect detection under uneven lighting conditions. A dual-input convolutional neural network fusing defect parameter vectors and layerwise images is proposed for real-time online monitoring of defects in the LPBF process using a paraxial CMOS camera monitoring system. The model integrates the image and the parameter information related to defect generation, and can distinguish some defects that are not easily discerned by images alone. To a certain extent, it avoids the problem that the same defects are visually indistinguishable in images caused by uneven light distribution and reflections on metal surfaces. The results indicate that the method has better performance than the method with a single image input, with recognition accuracies above 80.00% for all defect categories. In addition, the method is more suitable for real-time online monitoring scenarios due to its low parameter number, short training time and fast prediction speed compared to classical deep learning algorithms.

Suggested Citation

  • Zimeng Jiang & Aoming Zhang & Zhangdong Chen & Chenguang Ma & Zhenghui Yuan & Yifan Deng & Yingjie Zhang, 2024. "A deep convolutional network combining layerwise images and defect parameter vectors for laser powder bed fusion process anomalies classification," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2929-2959, August.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:6:d:10.1007_s10845-023-02183-4
    DOI: 10.1007/s10845-023-02183-4
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    References listed on IDEAS

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    1. Jingchang Li & Qi Zhou & Xufeng Huang & Menglei Li & Longchao Cao, 2023. "In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 853-867, February.
    2. Masoumeh Aminzadeh & Thomas R. Kurfess, 2019. "Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2505-2523, August.
    3. Hanxin Hu & Ting Sun, 2022. "The Applications of Machine Learning in Accounting and Auditing Research," Springer Books, in: Cheng-Few Lee & Alice C. Lee (ed.), Encyclopedia of Finance, edition 0, chapter 89, pages 2095-2115, Springer.
    4. Yingjie Zhang & Wentao Yan, 2023. "Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2557-2580, August.
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