Abstract
Welding defects detection and classification is very important to guarantee the welding quality. Over the last 30 years, there has been a large amount of research attempting to develop an automatic (or semiautomatic) system for the detection and classification of weld defects in continuous welds using radiography. In this paper, we describe an automatic system for classification of welding defects from radiographic images and compare with KNN and SVM classifiers. We classify and recognize the linear defects such as lack of penetrations, incomplete fusion and external undercut. Experimental results have shown the classification method is useful for the lengthy defects and obtained through our method is better than the two classifiers methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
R. R. da Silva and D. Mery, “State-of-the-art of weld seam inspection by radiographic testing, Ch. I: Image processing,” Mater. Evaluation 6 (65), 643–647 (2007).
R. R. da Silva and D. Mery, “State-of-the-art of weld seam radiographic testing, Ch. II: Pattern recogtion.” wwwndtnet/search/docsphp3?id=4833
T. W. Liao and J. Ni, “An automated radiographic NDT system for weld inspection, Ch. I: Weld extraction,” NDT & E Int. 29 (3), 157–162 (1996).
T. W. Liao and K. Tang, “Automated extraction of welds from digitized radiographic images based on MLP neural networks,” Appl. Art. Intelligence 11 (3), 197–218 (1997).
T. W. Liao and Y. Li, “An automated radiographic NDT system for weld inspection, Ch. II: Flaw detection,” NDT & E Int. 31 (3), 183–192 (1998).
G. A. Wang and T. W. Liao, “Automatic identification of different types of welding defects in radiographic images,” NDT & E Int. 35 (8), 519–528 (2002).
R. Vilar, J. Zapata, and R. Ruiz, “Classification of welding defects in radiographic images using an ANN with modified performance function,” Lecture Notice Comput. Sci. 5620, 284–293 (Springer, Heidelberg, 2000).
R. Vilar, J. Zapata, and R. Ruiz, “Classification of welding defects in radiographic images using an adaptive-network-based fuzzy system,” NDT & E Int. 43, 191–199 (2010).
J. Zapata, R. Vilar, and R. Ruiz, “Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers,” Expert Syst. Appl. 38 (7), 8812–8824 (2011).
J. Zapata, R. Vilar, and R. Ruiz, “Automatic inspection system of welding radiographic images based on ANN under a regularisation process,” J. Nondestruct. Evaluat. 31 (1), 34–45 (2012).
D. Li and T. W. Liao, “Applications of fuzzy K-NN in weld recognition and tool failure monitoring,” in Proc. 28th Southeastern Symp. on System Theory (Baton Rouge, LA, 1996), pp. 222–226.
T. Liao, D. Li, and Y. Li, “Detection of welding flaws from radiographic images with fuzzy clustering methods,” Fuzzy Sets Syst. 108 (2), 145–158 (1999).
R. R. Da Silva, M. H. S. Siqueira, L. P. Calôba, I. C. Da Silva, A. De Carvalho, and J. Rebello, “Contribution to the development of a radiographic inspection automated system,” J. Nondestruct. Testing 7 (12), 1–8 (2002).
E. De Moura, R. Da Silva, A. De Carvalho, M. Siqueira, and J. Rebello, “Welding defecets pattern recognition in TOFD signals using linear classifier implemented by neural networks,” in Proc. 3th PanAmerican Conf. for Nondestructive Testing (Rio de Janeiro, 2003).
G. Weixin, T. Nan, and M. Xiangyang, “A novel algorithm for detecting air holes in steel pipe welding based on hopfield neural network,” in Proc. 8th ACIS IEEE Int. Conf. on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (Quingdao, 2007), pp. 79–83.
Z. Sun, D. Ruan, Y. Ma, X. Hu, and X. Zhang, “Crack defects detection in radiographic weldment images using FSVM and beamlet transform,” in Proc. 6th IEEE Int. Conf. on Fuzzy System and Knowledge Discovery (Tianjin, 2009), pp. 402–406.
J. Zapata, R. Vilar, and R. Ruiz, “An adaptive-networkbased fuzzy inference system for classification of welding defects,” NDT & E Int. 43 (3), 191–199 (2010).
H. Yazid and H. Arof, “Automated thresholding in radiographic image for welded joints,” Nondestructive Testing Evaluation 27 (1), 69–80 (2012).
A. Azari Moghaddam and L. Rangarajan, “A novel algorithm for de-noising radiographic images,” Int. J. Image, Graph. Signal Processing 4 (6), 22–28 (2012).
A. Azari Moghaddam and L. Rangarajan, “A method for segmentation radiographic images with case study on welding defects,” in Proc. 4th Int. Conf. on Signal and Image Processing 2012 (ICSIP 2012) (Springer, 2013), pp. 277–283.
http://wwwndt-edorg
Author information
Authors and Affiliations
Corresponding author
Additional information
The article is published in the original.
Alireza Azari Moghaddam, born in 1963, has master degrees in M. Tech, in Computer Science and technology from department of studies in computer science, University of Mysore, India in 2009. He completed his doctorate in Computer Science in 2014 in this university. He is Assistant Professor in department of studies in computer science, University of Maziar, Iran. His research interests include computer vision, pattern recognition, and digital image processing. His research is approaches for processing radiographic images with case study on welding defects detection. He is the author of 6 publications.
Lalitha Rangarajan, born in 1957, has master degrees in two related fields Mathematics (form University of Madras, India) and Industrial Engineering with a specialization in optimization (from Purdue University, USA). Since her return to India from USA in 1988, she is Associate Professor in department of studies in computer science, University of Mysore, India. She completed her doctorate in Computer Science in 2004 while teaching post graduate students of the department. Her research interests include Pattern Recognition, Image Retrieval and Bio Informatics. She has published more than 80 articles in renowned journals and peer reviewed conferences globally.
Rights and permissions
About this article
Cite this article
Moghaddam, A.A., Rangarajan, L. Classification of welding defects in radiographic images. Pattern Recognit. Image Anal. 26, 54–60 (2016). https://doi.org/10.1134/S1054661815040021
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1054661815040021