Classification of Welding Defects in Radiographic Images Using an Adaptive-Network-Based Fuzzy System

  • Rafael Vilar
  • Juan Zapata
  • Ramón Ruiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)


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%.


Membership Function Fuzzy Inference System Radiographic Image Weld Region Welding Defect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Liao, T.: Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition. Journal of Intelligent Manufacturing 15(1), 69–85 (2004)CrossRefGoogle Scholar
  5. 5.
    Liao, T.: Improving the accuracy of computer-aided radiographic weld inspection by feature selection. NDT & E International 42(4), 229–239 (2009)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    Wang, G., Liao, T.: Automatic identification of different types of welding defects in radiographic images. NDT & E International 35, 519–528 (2002)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Lim, J.: Two-dimensional signal and image processing, pp. 536–540. Prentice-Hall, Englewood Cliffs (1990)Google Scholar
  15. 15.
    Otsu, N.: A threshold selection meted from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)CrossRefGoogle Scholar
  16. 16.
    Haralick, R., Shapiro, L.: Computer and robot vision, vol. 1, pp. 28–48. Addison Wesley, NY (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rafael Vilar
    • 1
  • Juan Zapata
    • 2
  • Ramón Ruiz
    • 2
  1. 1.Departamento de Estructuras y ConstrucciónUniversidad Politécnica de CartagenaCartagenaSpain
  2. 2.Departamento de Electrónica, Tecnología de Computadores y ProyectosUniversidad Politécnica de CartagenaCartagenaSpain

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