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Crack Detection in Welded Images: A Comprehensive Survey

  • L. Mohanasundari
  • P. Sivakumar
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

This chapter presents a review on the different crack detection techniques. Welding crack detection plays a vital role in engineering applications. Some of this application includes engineering machinery, ships, civil infrastructure, etc. The complexities of the weld structure, the disparity of the welded materials, the variation in surface thermal radiation and the angle between the crack and the weld are found to be the major factors in crack detection. The intention of this survey is to find the improved performance metric like accuracy, sensitivity and specificity in different image processing techniques.

Keywords

Radiographic image Welding defect Classification Segmentation Enhancement Neural network Fuzzy Genetic algorithm 

References

  1. 1.
    S. Legendre, D. Massicotte, J. Goyette, T.K. Bose, Neural classification of lamb wave ultrasonic weld testing signals using wavelet coefficients. IEEE Trans. Instrum. Meas. 50(3), 672–678 (2001)CrossRefGoogle Scholar
  2. 2.
    T. Warren Liao, E. Triantaphyllou, P.C. Chang, Detection of welding flaws with MLP neural network and case based reasoning. Intell. Autom. Soft Comput. 9(4), 259–267 (2003)CrossRefGoogle Scholar
  3. 3.
    Y. Cho, S. Rhee, Quality estimation of resistance spot welding by using pattern recognition with neural networks. IEEE Trans. Instrum. Meas. 53(2), 330–334 (2004)CrossRefGoogle Scholar
  4. 4.
    J. Neuenschwander, B. Blau, R. Christin, T. Lüthi, G. Rössler, Quality monitoring of the electron beam welding of the CMS conductor using ultrasonics. IEEE Trans. Appl. Supercond. 14(2), 526–529 (2004)CrossRefGoogle Scholar
  5. 5.
    A. Prateepasen, P. Kaewtrakulpong, C. Jiarrungsatean, Classification of DC microspot welding quality using fuzzy artmap on acoustic emission monitoring. IEEE Trans. 500, 649–652Google Scholar
  6. 6.
    Y. Li, Y.F. Li, Q.L. Wang, X. De, M. Tan, Measurement and defect detection of the weld bead based on online vision inspection. IEEE Trans. Instrum. Meas. 59(7), 1841–1849 (2010)CrossRefGoogle Scholar
  7. 7.
    D. Du, R. Hou, J. Shao, B. Chang, L. Wang, Registration of real-time X-ray image sequences for weld inspection. Nondest. Test. Evalu. 25(2), 153–159 (2010)CrossRefGoogle Scholar
  8. 8.
    W. Wang, H. Wei, Y. Zhen, Y. Dou, H. Heng, Nondestructive evaluation of welding crack defects in structural component of track crane using acoustic emission technique. Intell. Autom. Soft Comput. 18(5), 513–523 (2012)CrossRefGoogle Scholar
  9. 9.
    Wafaa Al-Hameed, Yahya Mayali and Phil Picton, Segmentation of radiographic images of weld defect, J. Global Res. Comput. Sci., 4, 7, July (2013)Google Scholar
  10. 10.
    G. Casalino, S.L. Campanelli, F.M.C. Minutolo, Neuro-fuzzy model for the prediction and classification of the fused zone levels of imperfections in Ti6Al4V alloy butt weld. Adv. Mater. Sci. Eng. 2013, 7 (2013)CrossRefGoogle Scholar
  11. 11.
    Jayendra Kumar, R.S. Anand, S.P. Srivastava, Flaws classification using ANN for radiographic weld images, 978-1-4799-2866-8/14/$31.00 ©2014 IEEEGoogle Scholar
  12. 12.
    Jayendra Kumar, R.S. Anand, S.P. Srivastava, Multi-class welding flaws classification using texture feature for radiographic images, in 2014 International Conference on Advances in Electrical Engineering (ICAEE) (IEEE, 2014), pp. 1–4Google Scholar
  13. 13.
    Faiza Mekhalfa, Nafaa Nacereddine, Multiclass classification of weld defects in radiographic images based on support vector machines, in Tenth International Conference on Signal-Image Technology & Internet-Based Systems (2014)Google Scholar
  14. 14.
    Amit Kumar, R.S. Jadoun, Ankur Singh Bist, Optimization of MIG welding parameters using Artificial Neural Network (ANN) and Genetic Algorithm (GA), Int. J. Eng. Sci. Res. Technol., 614–620 (2014). ISSN: 2277-9655Google Scholar
  15. 15.
    Kamran Ali, Majid Awan, Abdul Jalil, Fiaz Mustansar, Localization and classification of welding defects using genetic algorithm based optimal feature set, in 2015 International Conference on Information and Communication Technologies (ICICT) (IEEE, 2015), pp. 1–6Google Scholar
  16. 16.
    C. Dang, J. Gao, Z. Wang, F. Chen, Y. Xiao, Multi-step radiographic image enhancement conforming to weld defect segmentation. IET Image Process. 9(11), 943–950 (2015)CrossRefGoogle Scholar
  17. 17.
    R. Ranjan, A. Talati, M. Ho, H. Bharmal, V.A. Bavdekar, V. Prasad, P. Mendez, Multivariate data analysis of gas-metal arc welding process, in The International Federation of Automatic Control June 7-10 (Whistler, British Columbia, Canada, 2015)Google Scholar
  18. 18.
    J. Gunther, P.M. Pilarski, G. Helfrich, H. Shen, K. Diepold, Intelligent laser welding through representation, prediction, and control learning: an architecture with deep neural networks and reinforcement learning. Mechatronics 34, 1–11.  https://doi.org/10.1016/j.mechatronics.2015.09.004 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • L. Mohanasundari
    • 1
  • P. Sivakumar
    • 2
  1. 1.Department of Electronics and Communication EngineeringKingston Engineering CollegeVelloreIndia
  2. 2.Department of Electronics and Communication EngineeringDr. N.G.P Institute of TechnologyCoimbatoreIndia

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