• DocumentCode
    1561551
  • Title

    The research of defect recognition for radiographic weld image based on fuzzy neural network

  • Author

    Xiao-Guang, Zhang ; Xu Jian-Jian ; Yu, Li

  • Author_Institution
    Inst. of Appl. Phys., Nanjing Univ., China
  • Volume
    3
  • fYear
    2004
  • Firstpage
    2661
  • Abstract
    This paper presents a method of automatic recognition of weld defects based on fuzzy neural network (FNN). The weld image is preprocessed to extract defect features, according to which 8 characteristic parameters are selected, and the FNN model used for defects recognition is set up. Here inputted samples of the FNN model are fuzzified using π function. And weld defects of different types are processed using a three-layer neural network and BP learning algorithm. Using forty-two training samples and seven testing samples for examination, the results show that this model can recognize weld defects with better effects. This research indicates that FNN has excellent performance for the defect recognition in weld image.
  • Keywords
    backpropagation; feature extraction; fuzzy neural nets; fuzzy set theory; image recognition; multilayer perceptrons; nondestructive testing; radiography; welding; BP learning algorithm; Pi function; automatic recognition; feature extraction; fuzzy neural network model; fuzzy set theory; radiographic weld image; three layer neural network; weld defect recognition; Biological neural networks; Character recognition; Feature extraction; Fuzzy neural networks; Image recognition; Image segmentation; Inspection; Noise reduction; Radiography; Welding;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
  • Print_ISBN
    0-7803-8273-0
  • Type

    conf

  • DOI
    10.1109/WCICA.2004.1342080
  • Filename
    1342080