Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine

doi:10.1007/s40436-020-00293-3

Advances in Manufacturing ›› 2020, Vol. 8 ›› Issue (1): 119-132.doi: 10.1007/s40436-020-00293-3

• ARTICLES • 上一篇

Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine

Wen-Hua Ye¹, Yun-Xia Guo^1,2, Heng-Fei Zhou¹, Rui-Jun Liang¹, Wei-Fang Chen¹

1 College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China;
2 Department of Mechanical Engineering, Henan Institute of Technology, Xinxiang 453003, Henan, People's Republic of China

收稿日期:2019-03-22 修回日期:2019-11-20 出版日期:2020-03-25 发布日期:2020-03-07
通讯作者: Wen-Hua Ye E-mail:whye@nuaa.edu.cn
基金资助:
This work is financially supported by the National Natural Science Foundation of China (Grant Nos. 51775277 and 51575272).

Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine

Wen-Hua Ye¹, Yun-Xia Guo^1,2, Heng-Fei Zhou¹, Rui-Jun Liang¹, Wei-Fang Chen¹

1 College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China;
2 Department of Mechanical Engineering, Henan Institute of Technology, Xinxiang 453003, Henan, People's Republic of China

Received:2019-03-22 Revised:2019-11-20 Online:2020-03-25 Published:2020-03-07
Contact: Wen-Hua Ye E-mail:whye@nuaa.edu.cn
Supported by:
This work is financially supported by the National Natural Science Foundation of China (Grant Nos. 51775277 and 51575272).

摘要/Abstract

摘要： This paper describes a novel modeling method for determining the thermal deformation coefficient of the moving shaft of a machine tool. Firstly, the relation between the thermal deformation coefficient and the thermal expansion coefficient is expounded, revealing that the coefficient of thermal deformation is an important factor affecting the precision of moving shaft feed systems. Then, thermal errors and current boundary and machining conditions are measured using sensors to obtain the first set of parameters for a thermal prediction model. The dynamic characteristics of the positioning and straightness thermal errors of the moving axis of a machine tool are analyzed under different feed speeds and mounting modes of the moving shaft and bearing. Finally, the theoretical model is derived from experimental data, and the axial and radial thermal deformation coefficients at different time and positions are obtained. The expressions for the axial and radial thermal deformation of the moving shaft are modified according to theoretical considerations, and the thermal positioning and straightness error models are established and experimentally verified. This modeling method can be easily extended to other machine tools to determine thermal deformation coefficients that are robust and self-correcting.

The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-020-00293-3.pdf

关键词: Moving shaft, Ball screw, Thermal expansion, Thermal deformation coefficient, Error modeling

Abstract: This paper describes a novel modeling method for determining the thermal deformation coefficient of the moving shaft of a machine tool. Firstly, the relation between the thermal deformation coefficient and the thermal expansion coefficient is expounded, revealing that the coefficient of thermal deformation is an important factor affecting the precision of moving shaft feed systems. Then, thermal errors and current boundary and machining conditions are measured using sensors to obtain the first set of parameters for a thermal prediction model. The dynamic characteristics of the positioning and straightness thermal errors of the moving axis of a machine tool are analyzed under different feed speeds and mounting modes of the moving shaft and bearing. Finally, the theoretical model is derived from experimental data, and the axial and radial thermal deformation coefficients at different time and positions are obtained. The expressions for the axial and radial thermal deformation of the moving shaft are modified according to theoretical considerations, and the thermal positioning and straightness error models are established and experimentally verified. This modeling method can be easily extended to other machine tools to determine thermal deformation coefficients that are robust and self-correcting.

The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-020-00293-3.pdf

Key words: Moving shaft, Ball screw, Thermal expansion, Thermal deformation coefficient, Error modeling

Wen-Hua Ye, Yun-Xia Guo, Heng-Fei Zhou, Rui-Jun Liang, Wei-Fang Chen. Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine[J]. Advances in Manufacturing, 2020, 8(1): 119-132.

参考文献

1. Mayr J, Jedrzejewski J, Uhlmann E et al (2012) Thermal issues in machine tools. CIRP Ann Manuf Technol 61:771-791
2. Putz M, Richter C, Regel J et al (2018) Industrial relevance and causes of thermal issues in machine tools. In:Proceedings of the conference on thermal issues in machine tools, 21-23 March, Dresden, pp 723-736
3. Liu K, Liu H, Li T et al (2018) Prediction of comprehensive thermal error of a preloaded ball screw on a gantry milling machine. J Manuf Sci Eng 140(2):021004. https://doi.org/10.1115/1.4037236
4. Wang HT, Li TM, Wang LP et al (2015) Review on thermal error modeling of machine tools. J Mech Eng 51(9):119-128
5. Chen JS (1996) A study of thermally induced machine tool errors in real cutting conditions. Int J Mach Tools Manuf 36(12):1401-1411
6. Mize CD, Ziegert JC (2000) Neural network thermal error compensation of a machining center. Precis Eng 24(4):338-346
7. Zhang Y, Yang JG (2011) Modeling for machine tool thermal error based on grey model preprocessing neural network. J Mech Eng 47(7):134-139
8. Zhang Y, Yang JG, Xiang ST et al (2013) Volumetric error modeling and compensation considering thermal effect on fiveaxis machine tools. Proc Inst Mech Eng Part C J Mech Eng Sci 227(5):1102-1115
9. Blaser P, Pavliček F, Mori K et al (2017) Adaptive learning control for thermal error compensation of 5-axis machine tools. J Manuf Syst 44(2):302-309
10. Wei X, Gao F, Li Y et al (2018) Study on optimal independent variables for the thermal error model of CNC machine tools. Int J Adv Manuf Technol 98(1/4):657-669
11. Yongjin K, Jeong MK, Omitaomub OA (2006) Adaptive support vector regression analysis of closed-loop inspection accuracy. Int J Mach Tools Manuf 46(6):603-610
12. Liu H, Miao EM, Wei XY et al (2017) Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm. Int J Mach Tools Manuf 113:35-48
13. Bai FY (2008) Research on thermal error modeling of machine tools based on Bayesian network. Dissertation, Zhejiang University, Hangzhou
14. Kim SK, Cho DW (1997) Real-time estimation of temperature distribution in a ball-screw system. Int J Mach Tools Manuf 37(4):451-464
15. Xu ZZ, Liu XJ, Choi CH et al (2012) A study on improvement of ball screw system positioning error with liquid-cooling. Int J Precis Eng Manuf 13(12):2173-2181
16. Horejs O (2007) Thermo-mechanical model of ball screw with non-steady heat sources. In:International conference on thermal issues in emerging technologies-theory and applications, 3-6 January 2007, Cairo, Egypt, pp 126-130
17. Mori M, Mizuguchi H, Fujishima M et al (2009) Design optimization and development of CNC lathe headstock to minimize thermal deformation. CIRP Ann Manuf Technol 58(1):331-334
18. Sun L, Ren M, Hong H et al (2017) Thermal error reduction based on thermodynamics structure optimization method for an ultra-precision machine tool. Int J Adv Manuf Technol 88(5/8):1267-1277
19. Shi H, Ma C, Yang J et al (2015) Investigation into effect of thermal expansion on thermally induced error of ball screw feed drive system of precision machine tools. Int J Mach Tools Manuf 97:60-71
20. Xu YY, Zu L, Wang YY et al (2018) Theoretical analysis and experimental study of thermal deformation model of ball screw and its influence on positioning accuracy. Modul Mach Tool Autom Manuf Tech 1:1-3, 7
21. Chen C, Qiu ZR, Li XF et al (2011) Temperature field model of ball screws used in servo systems. Optics Precis Eng 19(5):1151-1158
22. Mayr J, Blaser P, Ryser A et al (2018) An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates. CIRP Ann 67(1):551-554
23. Yang JG, Fan KG, Du ZC (2013) Technique of real-time error compensation on NC machine tools. China Machine Press, Beijing

Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine

Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine

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