Paper Title:
The Ultrasonic Signal Identification of the Nickel-Based Superalloy Based on the Wavelet Neural Network
  Abstract

By using the good time-frequency localized nature of the wavelet transformation and self-learning function of the traditional artificial neural network, this paper constructed a wavelet neural network model for the blemish signals in ultrasonic testing of the nickel-based superalloy GH4169, and it could recognize types of the blemish signals. The results show that the method is effective in fault diagnosis. Finally the article has confirmed its feasibility and superiority.

  Info
Periodical
Edited by
Yi-Min Deng, Aibing Yu, Weihua Li and Di Zheng
Pages
1581-1584
DOI
10.4028/www.scientific.net/AMM.37-38.1581
Citation
X. Yin, Y. P. Liu, "The Ultrasonic Signal Identification of the Nickel-Based Superalloy Based on the Wavelet Neural Network", Applied Mechanics and Materials, Vols. 37-38, pp. 1581-1584, 2010
Online since
November 2010
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