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Laser & Optoelectronics Progress, Volume. 57, Issue 15, 153002(2020)

Plastic Classification and Recognition by Laser-Induced Breakdown Spectroscopy and GA-BP Neural Network

Haisheng Song, Linzhao Ma*, Engong Zhu, Yifan Wang, Yuping Liu, Wenjian Sun, Peng Peng, and Chengfei Li
Author Affiliations
  • College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu 730070, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (14)
    Cited By (3)
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    Figures & Tables(13)
    Composition of BP neural network

    Fig. 1. Composition of BP neural network

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    Flow chart of GA-BP neural network

    Fig. 2. Flow chart of GA-BP neural network

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    Schematic diagram of the experimental device

    Fig. 3. Schematic diagram of the experimental device

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    Defocused state of laser burning sample surface. (a) Positive focus; (b) focus; (c) negative focus

    Fig. 4. Defocused state of laser burning sample surface. (a) Positive focus; (b) focus; (c) negative focus

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    Relationship between the characteristic spectral line and the amount of defocusing

    Fig. 5. Relationship between the characteristic spectral line and the amount of defocusing

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    Emission spectrum of ABS. (a) Original spectrum; (b) spectrum after treatment

    Fig. 6. Emission spectrum of ABS. (a) Original spectrum; (b) spectrum after treatment

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    First three main component dispersion points of different plastic samples

    Fig. 7. First three main component dispersion points of different plastic samples

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    Prediction results of PCA-GA-BP neural network

    Fig. 8. Prediction results of PCA-GA-BP neural network

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    Performance comparison of different algorithms. (a) GA; (b) PCA-BP neural network; (c) PCA-GA-BP neural network

    Fig. 9. Performance comparison of different algorithms. (a) GA; (b) PCA-BP neural network; (c) PCA-GA-BP neural network

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    Classification errors of three neural networks

    Fig. 10. Classification errors of three neural networks

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    • Table 1. Molecular formula and color of 9 plastic samples

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      Table 1. Molecular formula and color of 9 plastic samples

      SampleMolecular formulaColor
      ABS(C58H64N2)nyellow opaque
      PC(C16H14O3)nlight yellow-transparent
      PP[CH2CH(CH3)]nwhite opaque
      PE(C2H4)nwhite opaque
      POM(CH2O)nwhite opaque
      PU(CHNO2)nyellow black-transparent
      PS(C8H8)ncolorless and transparent
      PA-6(C6H11ON)nlight yellow-transparent
      PMMA(C5H8O2)ncolorless and transparent

    • Table 2. Characteristic spectral line and wavelength

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      Table 2. Characteristic spectral line and wavelength

      Characteristic spectral lineWavelength /nm
      C(I)247.86
      Mg (II)279.55
      Al (I)309.27
      Ti (II)334.90
      C-N388.30
      Ca (II)393.34
      F(II)429.91
      C2516.50
      Na (I)589.06
      H(I)656.30
      CL(I)725.70
      F(I)739.90
      N (I)746.90
      K(I)766.50
      O (I)777.30

    • Table 3. Training results of three neural networks

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      Table 3. Training results of three neural networks

      Neural networksTotal-error /pieceMean-error /pieceTotal training-time /sAverage-time /sAverage recognition-accuracy /%
      PCA-BP3046.0846.240.9398.31
      GA-BP1553.10113.602.2799.14
      PCA-GA-BP330.6682.101.6499.82
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    Haisheng Song, Linzhao Ma, Engong Zhu, Yifan Wang, Yuping Liu, Wenjian Sun, Peng Peng, Chengfei Li. Plastic Classification and Recognition by Laser-Induced Breakdown Spectroscopy and GA-BP Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(15): 153002

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    Paper Information

    Category: Spectroscopy

    Received: Nov. 19, 2019

    Accepted: Nov. 26, 2019

    Published Online: Aug. 4, 2020

    The Author Email: Ma Linzhao (1093704655@qq.com)

    DOI:10.3788/LOP57.153002

    Topics

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