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.

• Periodical: Applied Mechanics and Materials (Volumes 37-38)
• Pages: 1581-1584
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.37-38.1581
• Online since: November 2010
• Authors: Xin Yin, Yuan Peng Liu
• Keywords: GH4169 Superalloy, Superalloy GH4169, Ultrasonic Signal, Wavelet Neural Network (WNN)

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