• Laser & Optoelectronics Progress
  • Vol. 57, Issue 10, 101013 (2020)
Xianglin Zhan** and Wanting Zhao*
Author Affiliations
  • College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
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    DOI: 10.3788/LOP57.101013 Cite this Article Set citation alerts
    Xianglin Zhan, Wanting Zhao. Classification of Carbon Fiber Reinforced Polymer Defects Based on One-Dimensional CNN[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101013 Copy Citation Text show less
    Overall process of automatic identification of CFRP defect types
    Fig. 1. Overall process of automatic identification of CFRP defect types
    Structure diagram of U-1DCNN
    Fig. 2. Structure diagram of U-1DCNN
    Structure diagram of parallel structure of multi-convolution blocks
    Fig. 3. Structure diagram of parallel structure of multi-convolution blocks
    Structure diagrams of the residual units. (a) No matching dimensions; (b) matching dimensions
    Fig. 4. Structure diagrams of the residual units. (a) No matching dimensions; (b) matching dimensions
    Schematic of one-dimensional convolution calculation
    Fig. 5. Schematic of one-dimensional convolution calculation
    Bayesian optimization algorithm
    Fig. 6. Bayesian optimization algorithm
    Visualization of the activation from U-1DCNN convolutional layers
    Fig. 7. Visualization of the activation from U-1DCNN convolutional layers
    Dimensionality reduction visualization of U-1DCNN features
    Fig. 8. Dimensionality reduction visualization of U-1DCNN features
    Comparison of classification accuracy of different methods
    Fig. 9. Comparison of classification accuracy of different methods
    CFRP test blocksTest block 1Test block 2Test block 3
    Number of layers323232
    Thickness of test blocks /mm444
    Distribution of defectsUsing polytetrafluoroetylene(PTFE) films (thickness is0.25mm) to simulate delamination;a total of 30 defects, locatedat 2nd to 31st layersUsing PTFE films(thickness is 0.25mm)to simulate delamination;defect shapes:square and circleAdding hollow glassmicrospheres tosimulate gas cavity
    Defect type in thedatasetDelaminationNon-defectDelaminationNon-defectGas cavity
    Number of defectsin the dataset13001300390013002600
    Table 1. Specifications of CFRP test blocks and dataset composition
    Defect typeEvaluationindicatorBP+WPTBP+SFCNN+STFTU-1DCNN
    DelaminationPrec /%80.8588.3096.70100.00
    R /%91.8399.33100.00100.00
    Gas cavityPrec /%97.7299.01100.0098.04
    R /%100.0099.6793.33100.00
    Non-defectPrec /%80.1499.55100.00100.00
    R /%56.5074.0099.8398.00
    Average valuePrec /%86.2495.6298.9099.36
    R /%82.7891.0097.7299.33
    F1 /%84.4793.2598.3199.34
    Error rate /%14.966.921.710.50
    Table 2. Comparison of evaluation indicators of different methods
    Xianglin Zhan, Wanting Zhao. Classification of Carbon Fiber Reinforced Polymer Defects Based on One-Dimensional CNN[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101013
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