Tool Wear State Diagnosis Based on Wavelet Analysis-BP Neural Network

Abstract:

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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 et al., "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:

$35.00

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