ONLINE CUTTING STATE RECOGNITION IN TURNING USING A NEURAL-NETWORK

被引：31

作者：

RAHMAN, M ^{[1
]}

ZHOU, Q ^{[1
]}

HONG, GS ^{[1
]}

机构：

[1] NATL UNIV SINGAPORE,DEPT MECH & PROD ENGN,SINGAPORE 0511,SINGAPORE

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 1995年 / 10卷 / 02期

关键词：

CHATTER VIBRATION; CHIP BREAKING; MONITORING; NEURAL NETWORK; TOOL WEAR;

D O I：

10.1007/BF01179276

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Tool wear, chatter vibration, chip breaking and built-up edge are the main phenomena to be monitored in modern manufacturing processes. Much work has been carried out in the analysis and detection of these phenomena. However, most work has been mainly concerned with single, isolated detection I of such phenomena. The relationships between each fault have so far received very little attention. This paper presents a neural-network-based on-line fault diagnosis scheme which monitors the level of tool wear, chatter vibration and chip breaking in a turning operation. The experimental results show that the neural network has a high prediction success rate.

引用

页码：87 / 92

页数：6

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