A neural-fuzzy pattern recognition algorithm based cutting tool condition monitoring procedure

被引：0

作者：

Fu, Pan ^{[1
]}

Hope, A. D. ^{[2
]}

机构：

[1] SW Jiao Tong Univ, Fac Mech Engn, Chengdu 610031, Peoples R China

[2] Syst Engn Fac, Southampton Inst, Southampton SO14 OYN, Hants, England

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 2, PROCEEDINGS | 2007年 / 4492卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An intelligent tool wear monitoring system for metal cutting process will be introduced in this paper. The system is equipped with four kinds of sensors, signal transforming and collecting apparatus and a micro computer. A knowledge based intelligent pattern recognition algorithm has been developed. The fuzzy driven neural network can carry out the integration and fusion of multi-sensor information. The weighted approaching degree can measure the difference of signal features accurately and ANNs successfully recognize the tool wear states. The algorithm has strong learning and noise suppression ability. This leads to successful tool wear classification under a range of machining conditions.

引用

页码：293 / +

页数：2

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