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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
A. Prateepasen, P. Kaewtrakulpong, C. Jiarrungsatean, Classification of DC microspot welding quality using fuzzy artmap on acoustic emission monitoring. IEEE Trans. 500, 649–652
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)
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)
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)
Wafaa Al-Hameed, Yahya Mayali and Phil Picton, Segmentation of radiographic images of weld defect, J. Global Res. Comput. Sci., 4, 7, July (2013)
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)
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 IEEE
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. 1–4
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)
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: 2277-9655
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. 1–6
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)
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)
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
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mohanasundari, L., Sivakumar, P. (2020). Crack Detection in Welded Images: A Comprehensive Survey. In: Haldorai, A., Ramu, A., Mohanram, S., Onn, C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-19562-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19561-8
Online ISBN: 978-3-030-19562-5
eBook Packages: EngineeringEngineering (R0)