Paper Title:
Tool Wear State Diagnosis Based on Wavelet Analysis-BP Neural Network
  Abstract

Cutting force collected by experiment is transformed by continue wavelet in order to overcome the disadvantage that signal processing analyzes single variable. The eigenvector which can reflect tool wear state is extracted from scale-energy matrix based on analysis, and BP neural network is established to predict tool wear. Trained network is used for prediction by unknown sample. Results show that this method can identify and diagnose accurately tool wear state.

  Info
Periodical
Key Engineering Materials (Volumes 431-432)
Edited by
Yingxue Yao, Dunwen Zuo and Xipeng Xu
Pages
253-256
DOI
10.4028/www.scientific.net/KEM.431-432.253
Citation
N. Fan, H. Liang, P. Q. Guo, "Tool Wear State Diagnosis Based on Wavelet Analysis-BP Neural Network", Key Engineering Materials, Vols. 431-432, pp. 253-256, 2010
Online since
March 2010
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Price
$32.00
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