Fuzzy controlled backpropagation neural network for tool condition monitoring in face milling

被引：25

作者：

Dutta, RK

Paul, S ^{[1
]}

Chattopadhyay, AB

机构：

[1] Indian Inst Technol, Dept Mech Engn, Kharagpur 721302, W Bengal, India

[2] Assam Engn Coll, Dept Mech Engn, Gauhati 781013, Assam, India

来源：

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | 2000年 / 38卷 / 13期

关键词：

D O I：

10.1080/00207540050117404

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The performance of a fuzzy controlled backpropagation neural network has been studied to predict the tool wear in a face milling process based on simple process parameters and sensor signal features. The results show the potentiality of the method in comparison to the standard backpropagation neural network and one of its variants. The speed of convergence, accuracy of prediction and total time of system development make fuzzy controlled backpropagation an attractive technique amenable for online tool condition monitoring.

引用

页码：2989 / 3010

页数：22

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