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In-situ monitoring laser based directed energy deposition process with deep convolutional neural network

Author

Listed:
  • Jiqian Mi

    (Wuhan University)

  • Yikai Zhang

    (Wuhan University)

  • Hui Li

    (Wuhan University
    Wuhan University)

  • Shengnan Shen

    (Wuhan University
    Wuhan University)

  • Yongqiang Yang

    (South China University of Technology)

  • Changhui Song

    (South China University of Technology)

  • Xin Zhou

    (Air Force Engineering University)

  • Yucong Duan

    (Air Force Engineering University)

  • Junwen Lu

    (Civil Aviation Flight University of China)

  • Haibo Mai

    (Civil Aviation Flight University of China)

Abstract

Laser based directed energy deposition (L-DED) is a promising type of additive manufacturing technology. The non-destructive testing technology for the quality monitoring of L-DED processed parts is becoming more and more demanding in terms of accuracy, real-time, and ease of operation. This paper introduces a new image recognition system based on a deep convolutional neural network, which uses multiple lightweight architectures to reduce detection time. In order to eliminate the interference better, it improves the penalty function, which effectively improves the accuracy. Judging from the detection results of the data set, the accuracy of the model training reaches 94.71%, which achieves a very good image segmentation effect and solves the technical problem of in-situ monitoring of the L-DED process. This system realizes the positioning of the spatters for the first time, and at the same time, the number of spatters and area of molten pool are correlated to the laser scanning speed and the laser power.

Suggested Citation

  • Jiqian Mi & Yikai Zhang & Hui Li & Shengnan Shen & Yongqiang Yang & Changhui Song & Xin Zhou & Yucong Duan & Junwen Lu & Haibo Mai, 2023. "In-situ monitoring laser based directed energy deposition process with deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 683-693, February.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01820-0
    DOI: 10.1007/s10845-021-01820-0
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    References listed on IDEAS

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    1. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
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