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Acta Photonica Sinica, Volume. 49, Issue 10, 1015002(2020)

Classification of Edge Distortion of Tooth Profile Image Based on Improved Twin Support Vector Machine

He SUN1...2, Wen-zhen ZHAO1, Wen-hui ZHAO1 and Zhen-yun DUAN1 |Show fewer author(s)
Author Affiliations
  • 1School of Mechanical Engineering，Shenyang University of Technology，Shenyang 110870，China
  • 2School of Electrical and Information Engineering，Liaoning Institute of Science and Technology，Benxi，Liaoning 117004，China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
    Equations (16)
    References (20)
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    Figures & Tables(13)
    Geometric relationship of points on the involute

    Fig. 1. Geometric relationship of points on the involute

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    Segmentation of the tooth profile measurement area

    Fig. 2. Segmentation of the tooth profile measurement area

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    Discrimination of edge distortion area & type

    Fig. 3. Discrimination of edge distortion area & type

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    Schematic diagram of edge distortion type discrimination

    Fig. 4. Schematic diagram of edge distortion type discrimination

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    Structure of gear vision measuring device

    Fig. 5. Structure of gear vision measuring device

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    The working process of gear vision image acquisition

    Fig. 6. The working process of gear vision image acquisition

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    Eigenvector analysis

    Fig. 7. Eigenvector analysis

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    Test results on OCF-PBT-TWSVM algorithm

    Fig. 8. Test results on OCF-PBT-TWSVM algorithm

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    Test results on PBT-SVM algorithm

    Fig. 9. Test results on PBT-SVM algorithm

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    • Table 1. Comparison of different normalization methods

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      Table 1. Comparison of different normalization methods

      Normalization methodTest accuracyOptimal parameter selection of OCF-PBT-TWSVM
      No normalization78% (39/50)

      Population size N=20；Maximum number of iterations K=200;

      Best c1=46.52; c2=48.97; g=0.084

      [-1,1]Normalization96% (48/50)

      Population size N=20；Maximum number of iterations K=200;

      Best c1=6.544; c2=6.998; g=4.634

      [0,1]Normalization96% (48/50)

      Population size N=20；Maximum number of iterations K=200;

      Best c1=6.875; c2=6.529; g=16.004

    • Table 2. Comparison of different kernel functions

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      Table 2. Comparison of different kernel functions

      Choice of kernel functionTest accuracyOptimal parameter selection of OCF-PBT-TWSVM
      Linear54% (27/50)

      Population size N=20；Maximum number of iterations K=200;

      Best c1=9.93; c2=10.46; g=11.795

      Polynomial92% (46/50)

      Population size N=20；Maximum number of iterations K=200;

      Best c1=7.59; c2=8.92; g=14.234

      Radial basis function96% (48/50)

      Population size N=20；Maximum number of iterations K=200;

      Best c1=6.89; c2=7.42; g=13.931

      Sigmoid42% (21/50)

      Population size N=20；Maximum number of iterations K=200;

      Best c1=6.58; c2=7.39; g=17.242

    • Table 3. Comparison of test results of different algorithms

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      Table 3. Comparison of test results of different algorithms

      AlgorithmTest accuracyOptimal parameter selectionAverage test accuracy
      OCF-PBT-TWSVM97.87% (46/47)N=20; K=200; Best c1=15.38; c2=16.92; g=4.1896.96%
      97.22% (35/36)N=20; K=200; Best c1=20.85; c2=22.02; g=20.69
      96% (48/50)N=20; K=200; Best c1=6.89; c2=7.42; g=13.931
      PBT-SVM95.75% (45/47)Best c=5.66; g=494.06%
      94.44% (34/36)Best c=11.6; g=8
      92% (46/50)Best c=8; g=16

    • Table 4. Real-time statistics of visual measurement results of tooth profile edges

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      Table 4. Real-time statistics of visual measurement results of tooth profile edges

      Normal edge signal

      Ignore type of distorted

      signal

      		Eliminate type of distorted signalCompensation type of distorted signal
      1606410
      1587312
      162648
      1568412
      164448
      1539513
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    He SUN, Wen-zhen ZHAO, Wen-hui ZHAO, Zhen-yun DUAN. Classification of Edge Distortion of Tooth Profile Image Based on Improved Twin Support Vector Machine[J]. Acta Photonica Sinica, 2020, 49(10): 1015002

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    Paper Information

    Category: Machine Vision

    Received: Aug. 10, 2020

    Accepted: Sep. 17, 2020

    Published Online: Mar. 10, 2021

    The Author Email:

    DOI:10.3788/gzxb20204910.1015002

    Topics

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