Tool condition monitoring using artificial intelligence methods

被引：117
|
作者：
Balazinski, M
Czogala, E
Jemielniak, K
Leski, J
机构：
[1] Ecole Polytech, Dept Mech Engn, Montreal, PQ H3C 3A7, Canada
[2] Silesian Tech Univ, Inst Elect, PL-44101 Gliwice, Poland
[3] Warsaw Univ Technol, Fac Prod Engn, PL-02524 Warsaw, Poland
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2002年 / 15卷 / 01期
关键词：
tool monitoring; cutting force; artificial intelligence;
D O I：
10.1016/S0952-1976(02)00004-0
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper describes an application of three artificial intelligence (AI) methods to estimate tool wear in lathe turning. The first two are "conventional" AI methods- the feed forward back propagation neural network and the fuzzy decision support system. The third is a new artificial neural network based-fuzzy inference system with moving consequents in if-then rules. Tool wear estimation is based on the measurement of cutting force components. This paper discusses a comparison of usability of these methods in practice. (C) 2002 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：73 / 80
页数：8
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