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

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

Abstract

Abstract: 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.

Key words: tool wear, multi-source data, digital twin, on-line monitoring

摘要： 针对数控铣床不断老化导致刀具磨损预测模型误差较大，加工过程中动态数据难以在线采集等问题，提出一种数字孪生驱动的刀具磨损在线监测方法。采用神经网络对加工过程中的多源数据进行特征提取，建立考虑机床老化的刀具磨损时变偏差量化模型，并在此基础上提出数控铣削刀具磨损的在线预测方法；开发了面向刀具磨损的数控铣削数字孪生系统，在线感知加工过程中的动态数据并实时仿真刀具磨损过程；最后，将该方法应用于实际加工中并与其他的预测方法进行了对比，结果表明该方法有效降低了机床老化带来的误差，实现了刀具磨损的精确预测。

关键词: 刀具磨损, 多源数据, 数字孪生, 在线监测

CLC Number:

TH17

SUN Xin, HOU Xiaobo, ZHAO Xikun, WU Shaoqing. Online Monitoring Method for NC Milling Tool Wear by Digital Twin-drivenLI Congbo[J]. China Mechanical Engineering, 2022, 33(01): 78-87.

李聪波, 孙鑫, 侯晓博, 赵希坤, 吴少卿. 数字孪生驱动的数控铣削刀具磨损在线监测方法[J]. 中国机械工程, 2022, 33(01): 78-87.

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