• DocumentCode
    3024150
  • Title

    Application of ANN in the Thickness Measuring of Conductive Materials

  • Author

    Zhang Wei ; Qu Surong ; Li, Li ; Miao Qinglin ; Song Changyuan

  • Author_Institution
    Sch. of Mechinery & Electron., Henan Inst. of Sci. & Technol., Xinxiang, China
  • Volume
    4
  • fYear
    2009
  • fDate
    7-8 Nov. 2009
  • Firstpage
    94
  • Lastpage
    97
  • Abstract
    Eddy current testing (ECT) is becoming a widely used inspection technique, particularly in the aircraft, power and nuclear industries. Many factors may affect the eddy current response. Inverse problems to determine the thickness from ECT signals of multilayer conductors have been a challenge for a certain degree. The objectives of this study are to introduce a method based on improved back propagation neural network (BPNN) to identify the multilayer thickness from their ECT signals. The simulation study and an experimental validation carried out on a number of specimens with different known thickness confirmed the suitability of the proposed approach for multilayer thickness measuring.
  • Keywords
    backpropagation; eddy current testing; inspection; neural nets; production engineering computing; ANN; back propagation neural network; conductive materials; eddy current response; eddy current testing; inspection technique; inverse problems; multilayer conductors; multilayer thickness measuring; Aerospace materials; Aircraft; Artificial neural networks; Conducting materials; Conductivity measurement; Eddy current testing; Electrical capacitance tomography; Inspection; Multi-layer neural network; Thickness measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3835-8
  • Electronic_ISBN
    978-0-7695-3816-7
  • Type

    conf

  • DOI
    10.1109/AICI.2009.468
  • Filename
    5376421