• DocumentCode
    2534610
  • Title

    A cellular neural networks approach for non-destructive control of mechanical parts

  • Author

    Bertucco, L. ; Fargione, G. ; Nunnari, G. ; Risitano, A.

  • Author_Institution
    Dept. of Electr. Electron. & Syst., Catania Univ., Italy
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    159
  • Lastpage
    164
  • Abstract
    An approach is proposed using cellular neural networks applied image processing, for the detection and characterisation of superficial faults in mechanical parts. There are above all two advantages deriving from an application of the proposed methodologies: the automization of a procedure, that of non-destructive tests (NDT), which is today carried out manually, and the possibility to reduce to a negligible amount the time spent on checking operations at present estimated to be in the order of a number of hours for each separate mechanical part
  • Keywords
    cellular neural nets; image processing; nondestructive testing; mechanical parts; nondestructive control; superficial faults; Automatic control; Cellular neural networks; Electrical equipment industry; Electronic mail; Electronics industry; Image processing; Industrial electronics; Mechanical engineering; Nondestructive testing; Stress;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cellular Neural Networks and Their Applications, 2000. (CNNA 2000). Proceedings of the 2000 6th IEEE International Workshop on
  • Conference_Location
    Catania
  • Print_ISBN
    0-7803-6344-2
  • Type

    conf

  • DOI
    10.1109/CNNA.2000.876838
  • Filename
    876838