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

Jiahe Gao; Haibo Wen; Shenao Zhu; Shihui Dong; Shijie Su; Jian Zhang

doi:doi:10.11648/j.ijmea.20231101.11

| Peer-Reviewed

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

Jiahe Gao, Haibo Wen, Shenao Zhu, Shihui Dong, Shijie Su, Jian Zhang

Published in International Journal of Mechanical Engineering and Applications (Volume 11, Issue 1)

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	D. Kim, W. So, and M. J. A. M. S. E. Kang, "Effect of flash butt welding parameters on weld quality of mooring chain," vol. 38, no. 2, pp. 112-117, 2009.
[2]	S. Idapalapati, A. R. Akisanya, K. M. Loh, and S. J. E. F. A. Yeo, "Failure analysis of a failed anchor chain link," p. S1350630717310506, 2018.
[3]	D. Li, D. Chen, J. Goh, and S. K. Ng, "Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series," 2018.
[4]	A. B. Nassif, I. Shahin, I. Attili, M. Azzeh, and K. Shaalan, "Speech Recognition Using Deep Neural Networks: A Systematic Review," IEEE Access, vol. 7, pp. 19143-19165, 2019.
[5]	G. Pang, C. Shen, and A. Hengel, "Deep Anomaly Detection with Deviation Networks," in Knowledge Discovery and Data Mining, 2019.
[6]	J. Zhang, Y. Xie, Q. Wu, and Y. Xia, "Medical image classification using synergic deep learning," Med Image Anal, vol. 54, pp. 10-19, May 2019.
[7]	Y. Zhang, Y. Chen, J. Wang, and Z. Pan, "Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals," 2021.
[8]	J.-K. Park, W.-H. An, and D.-J. Kang, "Convolutional Neural Network Based Surface Inspection System for Non-patterned Welding Defects," International Journal of Precision Engineering and Manufacturing, vol. 20, no. 3, pp. 363-374, 2019.
[9]	R. Du, Y. Xu, Z. Hou, J. Shu, and S. Chen, "Strong noise image processing for vision-based seam tracking in robotic gas metal arc welding," The International Journal of Advanced Manufacturing Technology, vol. 101, no. 5-8, pp. 2135-2149, 2018.
[10]	M. Lngkvist, L. Karlsson, and A. J. P. R. L. Loutfi, "A review of unsupervised feature learning and deep learning for time-series modeling," vol. 42, pp. 11-24, 2014.
[11]	C. A. Ronao and S. B. J. E. S. w. A. Cho, "Human activity recognition with smartphone sensors using deep learning neural networks," vol. 59, 2016.
[12]	J. Zhao, X. Mao, and L. J. I. S. P. Chen, "Learning deep features to recognize speech emotion using merged deep CNN," vol. 12, no. 6, pp. 713-721, 2018.
[13]	U. R. Acharya, H. Fujita, O. S. Lih, M. Adam, J. H. Tan, and C. K. J. K.-B. S. Chua, "Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network," Knowledge-Based Systems, vol. 132, no. sep. 15, pp. 62-71, 2017.
[14]	X. Liu, Q. Zhou, J. Zhao, H. Shen, and X. Xiong, "Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network," Sensors (Basel), vol. 19, no. 4, Feb 25 2019.
[15]	H. Yang, C. Meng, and C. Wang, "Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network," IEEE Access, vol. 8, pp. 18305-18315, 2020.
[16]	S. Salman and X. Liu, "Overfitting Mechanism and Avoidance in Deep Neural Networks," ed, 2019.
[17]	C.-F. Tsai, W.-C. Lin, Y.-H. Hu, and G.-T. Yao, "Under-sampling class imbalanced datasets by combining clustering analysis and instance selection," Information Sciences, vol. 477, pp. 47-54, 2019.
[18]	X. Xu, W. Chen, and Y. Sun, "Over-sampling algorithm for imbalanced data classification," Jsee, vol. 30, no. 6, pp. 1182-1191, 2019.
[19]	N. Dvornik, I. Hadji, K. G. Derpanis, A. Garg, and A. D. Jepson, "Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers," in Neural Information Processing Systems, 2021.
[20]	L. Wang and P. Koniusz, "Uncertainty-DTW for time series and sequences," in European Conference on Computer Vision, 2022, pp. 176-195: Springer.
[21]	H. Wang, R. Pasupathy, B. W. J. A. T. o. M. Schmeiser, and C. Simulation, "Integer-ordered simulation optimization using RSPLINE: Retrospective search with piecewise-linear interpolation and neighborhood enumeration," Acm Transactions on Modeling and Computer Simulation ，vol. 23, no. 3, pp. 1-24, 2013.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download

Author Information

Jiahe Gao

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

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

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

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

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

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download