Application of Artificial Neural Network to Discrimination of Defect Type in Automatic Radiographic Testing of Welds

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The X-ray radiographic testing method is often used for detecting weld defects as a non-destructive testing method (NDT). Due to the difficulties in identifying small defects from the X-ray film, skilled laborers should be trained. However, recently it has been difficult to employ skilled laborers. Moreover, for the identification process, not only the laborers skill influence the testing result, but also it is difficult for skilled laborers to assess small flaws within a short time. In comparison, computer visual image processing system have some good characteristics, allowing objective assessment, high speed judgment, non-human's error etc. Therefore, an image processing system allows weld defects to detect using X-ray radiography in the presence of background noise. This paper deals with an image processing method for displaying defects by computer graphics. Furthermore an application of neural network to discriminate the type of defect was tried. As the result of the investigation, it was seen that the system constructed is effective to the detection and discrimination of small weld defects clearly.


  • Transactions of the Iron and Steel Institute of Japan  

    Transactions of the Iron and Steel Institute of Japan 39(10), 1081-1087, 1999-10 

    The Iron and Steel Institute of Japan

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