Neural network solutions to the tool condition monitoring problem in metal cutting - A critical review of methods

被引：126

作者：

Dimla, DE

Lister, PM

Leighton, NJ

机构：

[1] Engineering Research Group, SEBE, University of Wolverhampton, Wolverhampton WV1 1SB, Wulfruna Street

来源：

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE | 1997年 / 37卷 / 09期

关键词：

D O I：

10.1016/S0890-6955(97)00020-5

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A wide range of cutting tool monitoring techniques have been proposed and developed in the last decade, but only a few have found industrial applications, and a truly universally applicable system has still to be developed. In this paper a review of tool condition monitoring (TCM) systems, developed or implemented through application of neural networks, is provided. The review seeks to illustrate the extent of application of neural networks and the need for multiple source sensor signals in TCM systems. A critical analysis of methods is included and the trend in obtained results outlined. (C) 1997 Published by Elsevier Science Ltd. All rights reserved.

引用

页码：1219 / 1241

页数：23

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