Study on the thermally induced spindle angular errors of a five-axis CNC machine tool

doi:10.1007/s40436-022-00409-x

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (1): 75-92.doi: 10.1007/s40436-022-00409-x

• • 上一篇

Study on the thermally induced spindle angular errors of a five-axis CNC machine tool

Ji Peng, Ming Yin, Li Cao, Luo-Feng Xie, Xian-Jun Wang, Guo-Fu Yin

School of Mechanical Engineering, Sichuan University, Chengdu, 610065, People's Republic of China

收稿日期:2022-01-25 修回日期:2022-02-27 发布日期:2023-02-16
通讯作者: Ming Yin,E-mail:mingyin@scu.edu.cn E-mail:mingyin@scu.edu.cn
作者简介:Ji Peng is a Ph.D. candidate at the School of Mechanical Engineering, Sichuan University, China. His research interests include machine tool accuracy design, thermal error modeling, geometric error modeling, and intelligent compensation;
Ming Yin received the B.Sc. and Ph.D. degrees from the School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China, in 2010 and 2014, respectively. He is currently an Associate Professor with the School of Mechanical Engineering, Sichuan University, Chengdu, China. His current research interests include machine tool accuracy design,optical measurement, and additive manufacturing;
Li Cao is a Master's degree candidate at the School of Mechanical Engineer ing, Sichuan University, China. His research interests include machine tool thermal error modeling, the design of thermal error compensation systems;
Luo-Feng Xie received his Ph.D. degree in 2019 and B.Sc. degree in 2014 from the School of Manufacturing Science and Engineering, Sichuan University, Chengdu, China. He is currently an Associate Professor with the School of Mechanical Engineering, Sichuan University. His current research interests include machine tool accuracy modeling, signal processing and data mining;
Xian-Jun Wang is a Master's degree candidate at the School of Mechanical Engineer ing, Sichuan University, China. His research interests include machine tool accuracy modeling, machine tool vibration and compensation;
Guo-Fu Yin received a Ph.D. degree from the School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China, in 1989. He is currently a Professor at the School of Mechanical Engineering, Sichuan University, Chengdu, China. He has published more than 200 scientific papers. His current research interests include mechanical design and manufacturing, robotics and mechatronics, image processing and pattern recognition, computer vision, machine learning, and CAD/CAE/CAM.

Study on the thermally induced spindle angular errors of a five-axis CNC machine tool

Ji Peng, Ming Yin, Li Cao, Luo-Feng Xie, Xian-Jun Wang, Guo-Fu Yin

School of Mechanical Engineering, Sichuan University, Chengdu, 610065, People's Republic of China

Received:2022-01-25 Revised:2022-02-27 Published:2023-02-16
Supported by:
This work is supported by the Science and Technology Program of Sichuan Province (Grant Nos. 2019ZDZX0021 and 2020ZDZX0003), and the Fundamental Research Funds for the Central Universities (Grant No. 20826041D4254).

摘要/Abstract

摘要： Thermally induced spindle angular errors of a machine tool are important factors that affect the machining accuracy of parts. It is critical to develop models with good generalization abilities to control these angular thermal errors. However, the current studies mainly focus on the modeling of linear thermal errors, and an angular thermal error model applicable to different working conditions has rarely been investigated. Furthermore, the formation mechanism of the angular thermal error remains to be studied. In this study, an analytical modeling method was proposed by analyzing the formation and propagation chain of the spindle angular thermal errors of a five-axis computer numerical control (CNC) machine tool. The effects of the machine tool structure and position were considered in the modeling process. The angular thermal error equations were obtained by analyzing the spatial thermoelastic deformation states. An analytical model of the spindle angular thermal error was established based on the geometric relation between thermal deformations. The model parameters were identified using the trust region least squares method. The results showed that the proposed analytical model exhibited good generalization ability in predicting spindle pitch angular thermal errors under different working conditions with variable spindle rotational speeds, spindle positions, and environmental temperatures in different seasons. The average mean absolute error (MAE), root mean square error (RMSE) and R² in twelve different experiments were 4.7 μrad, 5.6 μrad and 0.95, respectively. This study provides an effective method for revealing the formation mechanism and controlling the spindle angular thermal errors of a CNC machine tool.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00409-x

关键词: Machine tool, Angular thermal errors, Thermal error modeling, Analytical model

Abstract: Thermally induced spindle angular errors of a machine tool are important factors that affect the machining accuracy of parts. It is critical to develop models with good generalization abilities to control these angular thermal errors. However, the current studies mainly focus on the modeling of linear thermal errors, and an angular thermal error model applicable to different working conditions has rarely been investigated. Furthermore, the formation mechanism of the angular thermal error remains to be studied. In this study, an analytical modeling method was proposed by analyzing the formation and propagation chain of the spindle angular thermal errors of a five-axis computer numerical control (CNC) machine tool. The effects of the machine tool structure and position were considered in the modeling process. The angular thermal error equations were obtained by analyzing the spatial thermoelastic deformation states. An analytical model of the spindle angular thermal error was established based on the geometric relation between thermal deformations. The model parameters were identified using the trust region least squares method. The results showed that the proposed analytical model exhibited good generalization ability in predicting spindle pitch angular thermal errors under different working conditions with variable spindle rotational speeds, spindle positions, and environmental temperatures in different seasons. The average mean absolute error (MAE), root mean square error (RMSE) and R² in twelve different experiments were 4.7 μrad, 5.6 μrad and 0.95, respectively. This study provides an effective method for revealing the formation mechanism and controlling the spindle angular thermal errors of a CNC machine tool.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00409-x

Key words: Machine tool, Angular thermal errors, Thermal error modeling, Analytical model

Ji Peng, Ming Yin, Li Cao, Luo-Feng Xie, Xian-Jun Wang, Guo-Fu Yin. Study on the thermally induced spindle angular errors of a five-axis CNC machine tool[J]. Advances in Manufacturing, 2023, 11(1): 75-92.

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[1]	Pu-Ling Liu, Zheng-Chun Du, Hui-Min Li, Ming Deng, Xiao-Bing Feng, Jian-Guo Yang. Thermal error modeling based on BiLSTM deep learning for CNC machine tool[J]. Advances in Manufacturing, 2021, 9(2): 235-249.
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Study on the thermally induced spindle angular errors of a five-axis CNC machine tool

Study on the thermally induced spindle angular errors of a five-axis CNC machine tool

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