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Quality Evaluation Method of Anchor Chain Flash Butt Welding Based on Deep Learning

Received: 24 December 2022     Accepted: 10 January 2023     Published: 17 January 2023
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Abstract

Flash butt welding, a mainstream welding method employed in producing anchor chains, is a critical manufacturing process affecting the quality of anchor chains. Ultrasonic and load testing are used to evaluate the welding quality of anchor chains, but the cost of checking and replacing unqualified chain links is high. A deep learning-based quality evaluation method for flash butt welding is proposed to reduce the cost of detecting and replacing substandard chain links. First, displacement and current sensors collect electrode position and current signals during welding. Second, since the number of qualified anchor links is much larger than that of unqualified ones, a new data synthesis method is proposed: nearest-neighbor splicing sampling, which achieves the enhancement of minority samples by segmenting and combining existing data samples according to the features of anchor chain welding. Then, a piecewise linear interpolation method is used to handle the varying data length problem, thus satisfying the input requirements of the convolutional neural network (CNN). Finally, a CNN model is established, and dropout is used to reduce the over-fitting phenomenon. The experimental results show that the accuracy of the under-sampling method, over-sampling method, and nearest-neighbor splicing sampling method are 93.8%, 95.9%, and 96.3%, respectively, and the sensitivity, specificity, and accuracy of the CNN model are 95.7%, 93%, and 94.3%, respectively, which are better than those of the support vector machine (SVM).

Published in International Journal of Mechanical Engineering and Applications (Volume 11, Issue 1)
DOI 10.11648/j.ijmea.20231101.11
Page(s) 1-8
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Deep Learning, Quality Evaluation, Anchor Chain, Flash Butt Welding, Nearest-Neighbor Splicing Sampling

References
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Cite This Article
  • APA Style

    Jiahe Gao, Haibo Wen, Shenao Zhu, Shihui Dong, Shijie Su, et al. (2023). Quality Evaluation Method of Anchor Chain Flash Butt Welding Based on Deep Learning. International Journal of Mechanical Engineering and Applications, 11(1), 1-8. https://doi.org/10.11648/j.ijmea.20231101.11

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    ACS Style

    Jiahe Gao; Haibo Wen; Shenao Zhu; Shihui Dong; Shijie Su, et al. Quality Evaluation Method of Anchor Chain Flash Butt Welding Based on Deep Learning. Int. J. Mech. Eng. Appl. 2023, 11(1), 1-8. doi: 10.11648/j.ijmea.20231101.11

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    AMA Style

    Jiahe Gao, Haibo Wen, Shenao Zhu, Shihui Dong, Shijie Su, et al. Quality Evaluation Method of Anchor Chain Flash Butt Welding Based on Deep Learning. Int J Mech Eng Appl. 2023;11(1):1-8. doi: 10.11648/j.ijmea.20231101.11

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  • @article{10.11648/j.ijmea.20231101.11,
      author = {Jiahe Gao and Haibo Wen and Shenao Zhu and Shihui Dong and Shijie Su and Jian Zhang},
      title = {Quality Evaluation Method of Anchor Chain Flash Butt Welding Based on Deep Learning},
      journal = {International Journal of Mechanical Engineering and Applications},
      volume = {11},
      number = {1},
      pages = {1-8},
      doi = {10.11648/j.ijmea.20231101.11},
      url = {https://doi.org/10.11648/j.ijmea.20231101.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20231101.11},
      abstract = {Flash butt welding, a mainstream welding method employed in producing anchor chains, is a critical manufacturing process affecting the quality of anchor chains. Ultrasonic and load testing are used to evaluate the welding quality of anchor chains, but the cost of checking and replacing unqualified chain links is high. A deep learning-based quality evaluation method for flash butt welding is proposed to reduce the cost of detecting and replacing substandard chain links. First, displacement and current sensors collect electrode position and current signals during welding. Second, since the number of qualified anchor links is much larger than that of unqualified ones, a new data synthesis method is proposed: nearest-neighbor splicing sampling, which achieves the enhancement of minority samples by segmenting and combining existing data samples according to the features of anchor chain welding. Then, a piecewise linear interpolation method is used to handle the varying data length problem, thus satisfying the input requirements of the convolutional neural network (CNN). Finally, a CNN model is established, and dropout is used to reduce the over-fitting phenomenon. The experimental results show that the accuracy of the under-sampling method, over-sampling method, and nearest-neighbor splicing sampling method are 93.8%, 95.9%, and 96.3%, respectively, and the sensitivity, specificity, and accuracy of the CNN model are 95.7%, 93%, and 94.3%, respectively, which are better than those of the support vector machine (SVM).},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Quality Evaluation Method of Anchor Chain Flash Butt Welding Based on Deep Learning
    AU  - Jiahe Gao
    AU  - Haibo Wen
    AU  - Shenao Zhu
    AU  - Shihui Dong
    AU  - Shijie Su
    AU  - Jian Zhang
    Y1  - 2023/01/17
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ijmea.20231101.11
    DO  - 10.11648/j.ijmea.20231101.11
    T2  - International Journal of Mechanical Engineering and Applications
    JF  - International Journal of Mechanical Engineering and Applications
    JO  - International Journal of Mechanical Engineering and Applications
    SP  - 1
    EP  - 8
    PB  - Science Publishing Group
    SN  - 2330-0248
    UR  - https://doi.org/10.11648/j.ijmea.20231101.11
    AB  - Flash butt welding, a mainstream welding method employed in producing anchor chains, is a critical manufacturing process affecting the quality of anchor chains. Ultrasonic and load testing are used to evaluate the welding quality of anchor chains, but the cost of checking and replacing unqualified chain links is high. A deep learning-based quality evaluation method for flash butt welding is proposed to reduce the cost of detecting and replacing substandard chain links. First, displacement and current sensors collect electrode position and current signals during welding. Second, since the number of qualified anchor links is much larger than that of unqualified ones, a new data synthesis method is proposed: nearest-neighbor splicing sampling, which achieves the enhancement of minority samples by segmenting and combining existing data samples according to the features of anchor chain welding. Then, a piecewise linear interpolation method is used to handle the varying data length problem, thus satisfying the input requirements of the convolutional neural network (CNN). Finally, a CNN model is established, and dropout is used to reduce the over-fitting phenomenon. The experimental results show that the accuracy of the under-sampling method, over-sampling method, and nearest-neighbor splicing sampling method are 93.8%, 95.9%, and 96.3%, respectively, and the sensitivity, specificity, and accuracy of the CNN model are 95.7%, 93%, and 94.3%, respectively, which are better than those of the support vector machine (SVM).
    VL  - 11
    IS  - 1
    ER  - 

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Author Information
  • School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China

  • School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China

  • School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China

  • School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China

  • School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China

  • School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China

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