Study on Fuzzy Data Fusion for Real-Time Intelligent Recognition of Tool Wear State

Abstract:

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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 et al., "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:

$35.00

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