Study on fuzzy data fusion for real-time intelligent recognition of tool wear state

被引：2

作者：

Ye, Bangyan ^{[1
]}

Liu, Jianping ^{[1
]}

Peng, Ruitao ^{[1
]}

Xu, Lanying ^{[1
]}

Zhao, Xuezhi ^{[1
]}

机构：

[1] S China Univ Tech, Sch Mech Engn, Guangzhou 510640, Peoples R China

来源：

ADVANCES IN MACHINING AND MANUFACTURING TECHNOLOGY IX | 2008年 / 375-376卷

关键词：

recognition of tool wear state; fuzzy data fusion; neural network; wavelet analysis;

D O I：

10.4028/www.scientific.net/KEM.375-376.626

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：626 / 630

页数：5