Paper Title:
Study on Fuzzy Data Fusion for Real-Time Intelligent Recognition of Tool Wear State
  Abstract

For detecting gradual tool wear state on line, the methods of Wavelet Fuzzy Neural Network, Regression Neural Network and Sample Classification Fuzzy Neural Network by detecting cutting force, motor power of machine tool and AE signal respectively are presented. Although these methods are not difficult to come true and processed accurately and rapidly, it is difficult to obtain comprehensive information of machining and exact value of tool wear when using single method of intelligent modeling and single signal detecting. For this purpose, fuzzy inference technique is adopted to fuse the recognized data. Emulation experiment is carried out by using Matlab software platform and this method is verified to be feasible. Experimental result indicates that by applying fuzzy data fusion, we can get an exact tool wear forecast rapidly.

  Info
Periodical
Key Engineering Materials (Volumes 375-376)
Edited by
Yingxue Yao, Xipeng Xu and Dunwen Zuo
Pages
626-630
DOI
10.4028/www.scientific.net/KEM.375-376.626
Citation
B. Y. Ye, J. P. Liu, R. T. Peng, Y. Tang, X. Z. Zhao, "Study on Fuzzy Data Fusion for Real-Time Intelligent Recognition of Tool Wear State", Key Engineering Materials, Vols. 375-376, pp. 626-630, 2008
Online since
March 2008
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Price
$32.00
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