Abstract
In this paper, we describe an automatic system of radiographic inspection of welding. An important stage in the construction of this system is the classification of defects. In this stage, an adaptive-network-based fuzzy inference system (ANFIS) for weld defect classification was used. The results was compared with the aim to know the features that allow the best classification. The correlation coefficients were determined obtaining a minimum value of 0.84. The accuracy or the proportion of the total number of predictions that were correct was determined obtaining a value of 82.6%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Silva, R.R., Mery, D.: State-of-the-art of weld seam inspection using X-ray testing: partI-image processing. Materials Evaluation 9(65), 643–647 (2007)
Silva, R.R., Mery, D.: State-of-the-art of weld seam inspection using X-ray testing: part II-pattern recognition. Materials Evaluation 9(65), 833–838 (2007)
Da Silva, R.R., Caloba, L.P., Siqueira, M.H., Rebello, J.M.: Pattern recognition of weld defects detected by radiographic test. NDT& E International 37(6), 461–470 (2004)
Liao, T.: Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition. Journal of Intelligent Manufacturing 15(1), 69–85 (2004)
Liao, T.: Improving the accuracy of computer-aided radiographic weld inspection by feature selection. NDT & E International 42(4), 229–239 (2009)
Shafeek, H., Gadelmawla, E., Abdel-Shafy, A., Elewa, I.: Automatic inspection of gas pipeline welding defects using an expert vision system. NDT & E International 37(4), 301–317 (2004)
Lim, T., Ratnam, M., Khalid, M.: Automatic classification of weld defects using simulated data and an mlp neural network. Insight: Non-Destructive Testing and Condition Monitoring 49(3), 154–159 (2007)
Mery, D., Berti, M.: Automatic detection of welding defects using texture features. In: International Symposium on Computed Tomography and Image Processing for Industrial Radiology, Berlin (2003)
Mirapeix, J., García-Allende, P.B., Cobo, A., Conde, O.M., Loópez, J.M.: Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks. NDT & E International 40, 315–323 (2007)
Wang, G., Liao, T.: Automatic identification of different types of welding defects in radiographic images. NDT & E International 35, 519–528 (2002)
Vilar, R., Zapata, J., Ruiz, R.: Classification of welding defects in radiographic images using an ANN with modified performance function. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2009. LNCS, vol. 5602, pp. 284–293. Springer, Heidelberg (2009)
Vilar, R., Zapata, J., Ruiz, R.: An automatic system of classification of weld defects in radiographic images. NDT & E International 42(5), 467–476 (2009)
Lim, J.: Two-dimensional signal and image processing, pp. 536–540. Prentice-Hall, Englewood Cliffs (1990)
Otsu, N.: A threshold selection meted from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)
Haralick, R., Shapiro, L.: Computer and robot vision, vol. 1, pp. 28–48. Addison Wesley, NY (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vilar, R., Zapata, J., Ruiz, R. (2011). Classification of Welding Defects in Radiographic Images Using an Adaptive-Network-Based Fuzzy System. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-21326-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21325-0
Online ISBN: 978-3-642-21326-7
eBook Packages: Computer ScienceComputer Science (R0)