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

页码：73 / 80

页数：8

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