Volume 37 Issue 3
Mar.  2011
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Nie Peng, Chen Xin. Prediction of tool VB value based on PCA and BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(3): 364-367,373. (in Chinese)
Citation: Nie Peng, Chen Xin. Prediction of tool VB value based on PCA and BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(3): 364-367,373. (in Chinese)

Prediction of tool VB value based on PCA and BP neural network

  • Received Date: 20 Apr 2010
  • Publish Date: 31 Mar 2011
  • Five layers of wavelet decomposition was applied on acoustic emission signals for extracting the acoustic emission(AE) signals energy value of six bands. Energy value and cutting speed, cutting depth, feed rate, cutting time were turned into state feature vectors of tool wear. The principal component analysis was used to reduce dimension and eliminate the correlation between the feature vectors. The principal components were seen as back propagation(BP) neural network input vector. Improved Levenberg-Marquart (LM) algorithm was used to BP neural network for learning, input vectors were trained for BP neural network. Then, the BP neural network would realize the forecast of tool flank wear VB value. The results indicate that the VB value forecast system based on principal component analysis (PCA) and the improved BP neural network with LM algorithm can accurately predict the tool flank wear VB value within the error range 0.03. The different states of tool wear can be judged according to the VB value.

     

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