Paper Title:
Study on Self-Configuration Method of Neural Network Model for Grinding Troubles On-Line Monitoring
  Abstract

A grinding trouble on-line monitoring mode is presented based on the nonlinear building mode principle of neural network. The input units were the peak of the FFT, the peak of RMS, and the standard deviation of AE signals. The outputs were the troubles of the grinding burning, grinding chatter, and grinding wheel dull. The structure of neural network is established by self-configuration method. The network mode is trained and tested by using the experiment data, and the results indicate that the neural network mode obtained by self-configuration method has high recognize rate for grinding troubles, and can be used to monitor grinding troubles on-line.

  Info
Periodical
Key Engineering Materials (Volumes 359-360)
Edited by
Jiuhua Xu, Xipeng Xu, Guangqi Cai and Renke Kang
Pages
199-203
DOI
10.4028/www.scientific.net/KEM.359-360.199
Citation
G. J. Liu, Q. Wang, X. L. Shi, R. K. Kang, "Study on Self-Configuration Method of Neural Network Model for Grinding Troubles On-Line Monitoring ", Key Engineering Materials, Vols. 359-360, pp. 199-203, 2008
Online since
November 2007
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$32.00
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