Online Monitoring Method for NC Milling Tool Wear by Digital Twin-driven

被引：0

作者：

Li C. ^{[1
]}

Sun X. ^{[1
]}

Hou X. ^{[1
]}

Zhao X. ^{[1
]}

Wu S. ^{[1
]}

机构：

[1] State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2022年 / 33卷 / 01期

关键词：

Digital twin; Multi-source data; On-line monitoring; Tool wear;

D O I：

10.3969/j.issn.1004-132X.2022.01.009

中图分类号：

学科分类号：

摘要：

In order to solve the problems of large errors of tool wear prediction model caused by continuous aging of CNC milling machines and difficulties of on-line acquisition of dynamic data during machining, a digital twin-driven online tool wear monitoring method was proposed. Firstly, a neural network was used to extract features from multi-source data in the machining processes, and a quantitative model of tool wear time varying deviation was established considering machine aging. Based on this, an on-line prediction method of tool wear in CNC milling was proposed. Then, a numerical control milling digital twin system for tool wear was developed to online sense the dynamic data and simulate the tool wear processes in real time. Finally, this method was applied to actual machining and compared with other prediction methods. The results show that this method may reduce the prediction errors and realize the accurate prediction of tool wear value. © 2022, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：78 / 87

页数：9

共 24 条

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