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

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%.

[1]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[2]  Domingo Mery,et al.  The state of the art of weld seam radiographic testing: Part II, pattern recognition , 2007 .

[3]  T. Warren Liao,et al.  Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition , 2004, J. Intell. Manuf..

[4]  J. López-Higuera,et al.  Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks , 2007 .

[5]  Gang Wang,et al.  Automatic identification of different types of welding defects in radiographic images , 2002 .

[6]  I. M. Elewa,et al.  Automatic inspection of gas pipeline welding defects using an expert vision system , 2004 .

[7]  Domingo Mery,et al.  Automatic detection of welding defects using texture features , 2003 .

[8]  Ramón Ruiz,et al.  An automatic system of classification of weld defects in radiographic images , 2009 .

[9]  T. Warren Liao,et al.  Improving the accuracy of computer-aided radiographic weld inspection by feature selection , 2009 .

[10]  Juan Zapata-Pérez,et al.  Classification of Welding Defects in Radiographic Images Using an ANN with Modified Performance Function , 2009, IWINAC.

[11]  Mani Maran Ratnam,et al.  Automatic classification of weld defects using simulated data and an MLP neural network , 2007 .

[12]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[13]  José Manuel Ferrández,et al.  Bioinspired Applications in Artificial and Natural Computation, Third International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2009, Santiago de Compostela, Spain, June 22-26, 2009, Proceedings, Part II , 2009, IWINAC.

[14]  Jae S. Lim,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[15]  Romeu Ricardo da Silva,et al.  Pattern recognition of weld defects detected by radiographic test , 2004 .