Real-time surrogate compensation architecture for machine-tool thermal error compensation with high-performance model

被引:0
|
作者
Huang, Yubin [1 ]
Hong, Hongyou [1 ]
Zhou, Huichen [1 ]
Xiang, Hua [1 ]
Yang, Jianzhong [1 ]
机构
[1] National Numerical Control System Engineering Research Center, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan,430074, China
关键词
D O I
10.1007/s42452-024-06389-w
中图分类号
学科分类号
摘要
Thermal error compensation based on high-performance (high-accuracy prediction of thermal errors under complex machining conditions and environments) models is of great significance to high-precision CNC machining. Currently, the high-performance thermal error models can only run at the edge computer due to the computing power limitation of CNC system under real-time constraints, Therefore, the timeliness of the thermal error compensation instruction cannot be guaranteed; delayed and mutated compensation instructions cause defects in the machining surface, which is the main obstacle that restricts the application of thermal error compensation based on high-performance models. In order to solve this challenge, this research proposes a novel real-time surrogate compensation architecture for machine tool thermal error compensation with high-performance model. Based on the look-ahead machine tool control command, the thermal error of next stage could be predicted according to the high-performance thermal error prediction model running on the edge, and the parameters of thermal error surrogate compensation model running on the CNC system could be fitted for high accuracy real-time compensation of the thermal error during the processing process. The verification experimental results on the CK40S CNC lathe show that the proposed surrogate method can retain more than 95% of the accuracy of the original high performance thermal error prediction model. With the surrogate thermal error compensation, the machining surface quality of the machine tool is flawless, and the batch machining error has been reduced from 18.3 μm to 4.5 μm. Compared with the traditional Multi Liner Regression (MLR) model compensation method, the machining accuracy under complex working conditions is further improved by 50%. © The Author(s) 2024.
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