A self-organising neural network for chatter identification in milling

被引：0

作者：

Li, TC ^{[1
]}

Tarng, YS ^{[1
]}

Chen, MC ^{[1
]}

机构：

[1] NATL TAIWAN INST TECHNOL,DEPT MECH ENGN,TAIPEI 10772,TAIWAN

来源：

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY | 1996年 / 9卷 / 5-6期

关键词：

chatter; milling; monitoring; neural networks;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

An in-process milling chatter identification system based on a self-organising neural network using the adaptive resonance theory (ART2-A) is presented in the paper. The difference of the resultant cutting force signal in a revolution is utilised as the input pattern for the neural network to recognise the milling process with or without chatter. Experiments show that the new approach can correctly monitor chatter in milling operations.

引用

页码：239 / 248

页数：10

共 14 条

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[10] Skapura D.M., 1991, NEURAL NETWORKS ALGO

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