On-line Monitoring of Tool Wear Conditions in Machining Processes Based on Machine Tool Data

被引：4

作者：

Lu Z. ^{[1
]}

Ma P. ^{[1
]}

Xiao J. ^{[2
]}

Wang M. ^{[1
]}

Tang X. ^{[1
]}

机构：

[1] School of Mechanical Engineering and Automation, Beihang University, Beijing

[2] CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, 412005, Hunan

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2019年 / 30卷 / 02期

关键词：

Convolutional neural network; Object linking and embedding for process control unified architecture(OPC UA); On-line monitoring; Tool wear;

D O I：

10.3969/j.issn.1004-132X.2019.02.013

中图分类号：

学科分类号：

摘要：

To realize the on-line monitoring of tool wear conditions, and improve the feasibility of monitoring system in machining processes, an on-lime cutting tool condition monitoring method was proposed based on machine tool data in machining processes. OPC UA was used for NC machine tool data acquisition and storing, and the internal machine process informations related to tool wear conditions were collected. Based on the process informations and corresponding wear informations, convolutional neural network was used to establish a recognition model of tool wear conditions. The performance of proposed method was verified in machining cases, and compared with other tool wear condition monitoring methods. This method is more suitable for tool wear condition monitoring in practical machining processes. © 2019, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：220 / 225

页数：5

共 14 条

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