金刚石车削
伺服
金刚石工具
机床
伺服机构
机械加工
机械工程
模型预测控制
数字控制
控制工程
工程类
材料科学
控制理论(社会学)
计算机科学
控制(管理)
电子工程
人工智能
作者
Xichun Luo,Qi Liu,P. M. Abhilash,Wenkun Xie
出处
期刊:CIRP Annals
[Elsevier BV]
日期:2024-01-01
卷期号:73 (1): 377-380
被引量:13
标识
DOI:10.1016/j.cirp.2024.04.080
摘要
A predictive digital twin (DT)-driven dynamic error control approach is presented for accuracy control in high-frequency slow-tool-servo ultraprecision diamond turning processes. An explainable artificial intelligence-enabled real-time DT of the total dynamic error (inside and outside the servo loop) was established using in-line acceleration input data near the tool. A feedforward controller was used to mitigate the total dynamic errors before they came into effect. The machining trials using this approach showed that significant improvement in machining accuracy (87%, surface form accuracy; 95%, phase accuracy with precisions of 0.06 µm and 0.05°), and efficiency (8 times the state-of-the-art) were successfully achieved.
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