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

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (1) : 75 -92.

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Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (1) : 75 -92. DOI: 10.1007/s40436-022-00409-x
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Study on the thermally induced spindle angular errors of a five-axis CNC machine tool

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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 R 2 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.

Keywords

Machine tool / Angular thermal errors / Thermal error modeling / Analytical model

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

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References

[1]

Mayr J, Jedrzejewski J, Uhlmann E, et al. Thermal issues in machine tools. CIRP Ann Manuf Technol, 2012, 61(2): 771-791.

[2]

Weck M, McKeown P, Bonse R, et al. Reduction and compensation of thermal errors in machine tools. CIRP Ann Manuf Technol, 1995, 44(2): 589-598.

[3]

Bryan J. International status of thermal error research (1990). CIRP Ann Manuf Technol, 1990, 39(2): 645-656.

[4]

Liu Y, Ma YX, Meng QY, et al. Improved thermal resistance network model of motorized spindle system considering temperature variation of cooling system. Adv Manuf, 2018, 6: 384-400.

[5]

Xiang S, Zhu X, Yang J. Modeling for spindle thermal error in machine tools based on mechanism analysis and thermal basic characteristics tests. Proc Inst Mech Eng Part C J Mech Eng Sci, 2014, 228(18): 3381-3394.

[6]

Liu K, Liu Y, Sun M, et al. Spindle axial thermal growth modeling and compensation on CNC turning machines. Int J Adv Manuf Technol, 2016, 87: 2285-2292.

[7]

Xiang S, Yao X, Du Z, et al. Dynamic linearization modeling approach for spindle thermal errors of machine tools. Mechatronics, 2018, 53: 215-228.

[8]

Liu P, Yao X, Ge G, et al. A dynamic linearization modeling of thermally induced error based on data-driven control for CNC machine tools. Int J Precis Eng Manuf, 2021, 22: 241-258.

[9]

Li Y, Zhao W, Lan S, et al. A review on spindle thermal error compensation in machine tools. Int J Mach Tools Manuf, 2015, 95: 20-38.

[10]

Wang L, Wang H, Li T, et al. A hybrid thermal error modeling method of heavy machine tools in z-axis. Int J Adv Manuf Technol, 2015, 80: 389-400.

[11]

Liu PL, Du ZC, Li HM, et al. Thermal error modeling based on BiLSTM deep learning for CNC machine tool. Adv Manuf, 2021, 9: 235-249.

[12]

Zou Z, Yan W, Ma W, et al. Development of thermal error mapping model for the dry gear hobbing machine based on CNN-DAE integrated structure and its application. Int J Adv Manuf Technol, 2021, 113: 2343-2354.

[13]

Liang YC, Li WD, Lou P, et al. Thermal error prediction for heavy-duty CNC machines enabled by long short-term memory networks and fog-cloud architecture. J Manuf Syst, 2022, 62: 950-963.

[14]

Ko TJ, Gim TW, Ha JY. Particular behavior of spindle thermal deformation by thermal bending. Int J Mach Tools Manuf, 2003, 43: 17-23.

[15]

Liu K, Li T, Liu H, et al. Analysis and prediction for spindle thermal bending deformations of a vertical milling machine. IEEE Trans Ind Inform, 2020, 16: 1549-1558.

[16]

Liu J, Ma C, Wang S. Data-driven thermally-induced error compensation method of high-speed and precision five-axis machine tools. Mech Syst Signal Process, 2020, 138.

[17]

Yang J, Shi H, Feng B, et al. Thermal error modeling and compensation for a high-speed motorized spindle. Int J Adv Manuf Technol, 2015, 77: 1005-1017.

[18]

Ma C, Zhao L, Mei X, et al. Thermal error compensation of high-speed spindle system based on a modified BP neural network. Int J Adv Manuf Technol, 2017, 89: 3071-3085.

[19]

Yang J, Mei X, Zhao L, et al. Thermal error compensation on a computer numerical control machine tool considering thermal tilt angles and cutting tool length. Proc Inst Mech Eng Part B J Eng Manuf, 2015, 229: 78-97.

[20]

Liu J, Ma C, Gui H, et al. Thermally-induced error compensation of spindle system based on long short term memory neural networks. Appl Soft Comput, 2021, 102.

[21]

Wang S, Yang Y, Li X, et al. Research on thermal deformation of large-scale computer numerical control gear hobbing machines. J Mech Sci Technol, 2013, 27: 1393-1405.

[22]

Xiang S, Deng M, Li H, et al. Cross-rail deformation modeling, measurement and compensation for a gantry slideway grinding machine considering thermal effects. Meas Sci Technol, 2019, 30(6): 065007.

[23]

Tan B, Mao X, Liu H, et al. A thermal error model for large machine tools that considers environmental thermal hysteresis effects. Int J Mach Tools Manuf, 2014, 82(83): 11-20.

[24]

Mian NS, Fletcher S, Longstaff AP, et al. Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations. Precis Eng, 2013, 37: 372-379.

[25]

Ye WH, Guo YX, Zhou HF, et al. Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine. Adv Manuf, 2020, 8: 119-132.

[26]

ISO 230-3:2020 (2020) Test code for machine tools—part 3: determination of thermal effects. Switzerland

[27]

Moré JJ, Sorensen DC. Computing a trust region step. SIAM J Sci Stat Comput, 1983, 4: 553-572.

Funding

Sichuan Province Science and Technology Support Program http://dx.doi.org/10.13039/100012542(2019ZDZX0021)

Fundamental Research Funds for the Central Universities http://dx.doi.org/10.13039/501100012226(20826041D4254)

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