• Laser & Optoelectronics Progress
  • Vol. 57, Issue 15, 153002 (2020)
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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    DOI: 10.3788/LOP57.153002 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Composition of BP neural network
    Fig. 1. Composition of BP neural network
    Flow chart of GA-BP neural network
    Fig. 2. Flow chart of GA-BP neural network
    Schematic diagram of the experimental device
    Fig. 3. Schematic diagram of the experimental device
    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
    Relationship between the characteristic spectral line and the amount of defocusing
    Fig. 5. Relationship between the characteristic spectral line and the amount of defocusing
    Emission spectrum of ABS. (a) Original spectrum; (b) spectrum after treatment
    Fig. 6. Emission spectrum of ABS. (a) Original spectrum; (b) spectrum after treatment
    First three main component dispersion points of different plastic samples
    Fig. 7. First three main component dispersion points of different plastic samples
    Prediction results of PCA-GA-BP neural network
    Fig. 8. Prediction results of PCA-GA-BP neural network
    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
    Classification errors of three neural networks
    Fig. 10. Classification errors of three neural networks
    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 1. Molecular formula and color of 9 plastic samples
    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 2. Characteristic spectral line and wavelength
    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
    Table 3. Training results of three neural networks
    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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