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Laser & Optoelectronics Progress, Volume. 60, Issue 23, 2314009(2023)

MLP Neural Network-Based Detection System for Rust Removal by Laser

Rui Yi1, Chunming Wang1, Wei Zhang2, and Jun Wang3、*
Author Affiliations
  • 1School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
  • 2School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
  • 3School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan 430200, Hubei, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
    Equations (7)
    References (23)
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    Figures & Tables(11)
    Schematic diagram of laser derusting computer vision detection system

    Fig. 1. Schematic diagram of laser derusting computer vision detection system

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    Structure of neural network

    Fig. 2. Structure of neural network

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    Training process and results of MLP network with different number of hidden layers. (a) Error curves; (b) training results

    Fig. 3. Training process and results of MLP network with different number of hidden layers. (a) Error curves; (b) training results

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    Single galvanometer beam scanning system

    Fig. 4. Single galvanometer beam scanning system

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    Image processing results. (a) ROI before opening-operation; (b) ROI after opening-operation

    Fig. 5. Image processing results. (a) ROI before opening-operation; (b) ROI after opening-operation

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    Rust samples in different states (left) and base plate samples (right)

    Fig. 6. Rust samples in different states (left) and base plate samples (right)

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    Different rust images. (a) Sample A taken under professional LED light source; (b) sample B taken under professional LED light source; (c) sample B taken under natural light; (d) sample B taken under uneven light

    Fig. 7. Different rust images. (a) Sample A taken under professional LED light source; (b) sample B taken under professional LED light source; (c) sample B taken under natural light; (d) sample B taken under uneven light

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    Image processing results of different rusts. (a1)‒(d1) are MLP rust identification results of Fig. 7(a)‒(d), respectively; (a2)‒(d2) are ROI of (a1)‒(d1), respectively

    Fig. 8. Image processing results of different rusts. (a1)‒(d1) are MLP rust identification results of Fig. 7(a)‒(d), respectively; (a2)‒(d2) are ROI of (a1)‒(d1), respectively

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    Results of laser cleaning. (a) Sample A; (b) sample B

    Fig. 9. Results of laser cleaning. (a) Sample A; (b) sample B

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    • Table 1. Chemical composition of Q345 steel

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      Table 1. Chemical composition of Q345 steel

      ElementCMnSiPSV
      Mass fraction /%<0.2<1.7<0.55<0.045<0.045<0.15

    • Table 2. Coverage and misrecognition rate of rust identification results

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      Table 2. Coverage and misrecognition rate of rust identification results

      GroupCoverage rate /%Misrecognition rate /%
      a99.391.03
      b96.870.13
      c95.052.03
      d98.025.96
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    Rui Yi, Chunming Wang, Wei Zhang, Jun Wang. MLP Neural Network-Based Detection System for Rust Removal by Laser[J]. Laser & Optoelectronics Progress, 2023, 60(23): 2314009

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    Paper Information

    Category: Lasers and Laser Optics

    Received: Oct. 26, 2022

    Accepted: Dec. 12, 2022

    Published Online: Dec. 11, 2023

    The Author Email: Jun Wang (wangjunwuhan@163.com)

    DOI:10.3788/LOP222896

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology