Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (10): 2546-2553.doi: 10.13229/j.cnki.jdxbgxb.20250780

Previous Articles    

Fluid pressure compensation method for thermal error in polishing pad surface dressing

Ling-yu ZHAO1(),Ze-cun GAO1,Hui-ying ZHAO2   

  1. 1.School of Mechanical Engineering,Beijing Institute of Petrochemical Technology,Beijing 102627,China
    2.School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049,China
  • Received:2025-09-01 Online:2026-10-01 Published:2026-10-09

Abstract:

To solve the problem of thermal deformation in the surface dressing of Φ1 200 mm metal polishing disc during the continuous polishing process of large aperture planar optical elements, a compensation method of thermal error in the surface dressing of metal polishing pad using fluid pressure is proposed. The structure of fluid pressure control system on the surface of Φ1 200 mm metal polishing pad was designed. Three aspects of the experiment were studied, including the thermal deformation of the polishing pad surface, the fluid pressure control deformation of the polishing pad surface, and the thermal error compensation of the polishing pad surface. The experimental results showed that the surface thermal error compensation of polishing pad can be realized by adjusting the fluid pressure. When liquid inlet pressure was 0.10 MPa, the best effect of surface thermal error compensation of Φ1 200 mm polishing pad was obtained, in which the error peak and valley value was reduced from the original 24 μm to 14 μm, and the thermal error was reduced by 42%.

Key words: ultra precision machining, thermal error compensation, planar optical components, fluid pressure regulation, flat polishing

CLC Number: 

  • TH122

Fig.1

Principle model of fluid pressure regulation for surface of polishing pad"

Fig.2

Fluid pressure deformation model of polishing pad surface"

Fig.3

Physical object of fluid pressure control system on surface of polishing pad"

Table 1

Original thermal displacement experiment on surface of polishing pad"

序号转台转速n/(r·min-1)抛光盘表面热位移/μm
120

根据式(6)

测量并计算

260
3100

Table 2

Experimental parameters for stress deformation on surface of polishing pad"

序号t/min

p1/MPa

(主动控制)

p2/MPa

(实时监测)

抛光盘表面变形/μm
100.10.02根据式(7)测量并计算
2100.10.02
3200.10.02

Fig.4

Experimental scheme for fluid pressure regulation of polishing pad surface"

Fig.5

Experimental platform of fluid pressure control on surface of polishing pad"

Table 3

Experimental parameters for thermal error compensation of polishing pad surface"

序

号

t/min

p1/MPa

(主动控制)

p2/MPa

(实时监测)

n/

(r·min-1)

抛光盘表面热热位移/μm
1100.100.02100

根据式(6)

测量并计算

2100.150.03100
3100.200.05100
4100.250.06100
5100.300.08100

Fig.6

Calculation results of turntable thermal power"

Fig.7

Thermal error on the surface of polishing pad at different rotational speeds of turntable"

Fig.8

Deformation of polishing pad surface under different fluid pressure application durations"

Fig.9

Surface deformation of polishing pad under different fluid pressure"

Fig.10

Maximum and average values of surface deformation of the polishing pad"

Fig.11

Compensation result of thermal displacement error on surface of polishing pad"

Fig.12

Compensation result of thermal displacement error on surface of polishing pad"

[1] Li Yu, Tian Hong-chuan, Liu Di-fei, et al. Thermal error analysis of five-axis machine tools based on five-point test method[J]. Lubricants, 2022, 10(6): 122.
[2] Shi Hu, Xiao Yao, Mei Xue-song, et al. Thermal error modeling of machine tool based on dimensional error of machined parts in automatic production line[J]. ISA Transactions, 2023, 135: 575-584.
[3] Nico Zimmermann, Lang Sebastian, Josef Mayr, et al. Validating real time compensation: a thermal test piece for 5-axis machine tools to separate thermal errors in Z-direction[J]. Precision Engineering, 2024, 91: 263-277.
[4] Mu Sen, Yu Chun-ping, Lin Kun-long, et al. A review of machine learning—based thermal error modeling methods for CNC machine tools[J]. Machines, 2025, 13(2): 153.
[5] Gu Geon-woo, Min-suk Park, Jun-ho Suh,et al. A technique for integrated compensation of geometric errors and thermal errors to improve positional accuracy of hole machining in large-size parts[J]. International Journal of Precision Engineering and Manufacturing, 2024, 25(8): 1541-1555.
[6] Wu Shi, Wang Yu-peng, Liu Xian-li, et al. Separating machining errors of S-shaped samples based on the comprehensive error field of five-axis machine tools[J]. Journal of Mechanical Science and Technology, 2023, 37(1): 305-316.
[7] Feng Zhi-ming, Min Xing-long, Jiang Wei, et al. Study on thermal error modeling for cnc machine tools based on the improved radial basis function neural network[J]. Applied Sciences-Basel, 2023, 13(9): 5299.
[8] Song Lei, Liu Kuo, Zhao Di, et al. The spindle axial time-varying thermal error compensation method for horizontal boring and milling machine tool based on edge computing[J]. International Journal of Advanced Manufacturing Technology, 2023, 128(5,6): 2631-2638.
[9] Mohsen Soori, Behrooz Arezoo. Dimensional, geometrical, thermal and tool deflection errors compensation in 5-axis CNC milling operations[J]. Australian Journal of Mechanical Engineering, 2023, 22(5): 935-949.
[10] Li Zong-ze, Vogl Gregory W, Kinzel Edward C, et al. Machine tool thermal error measurement and prediction via wireless microscope[C]∥The 52nd SME North American Manufacturing Research Conference, Knoxville, USA,2024: 1440-1451.
[11] Sun Jia-cheng, Liu Zhen-yu, Qiu Chan, et al. An axial attention-BiLSTM-based method for predicting the migration of CNC machine tool spindle thermal error under varying working conditions[J]. International Journal of Advanced Manufacturing Technology, 2024, 130(3,4): 1439-1449.
[12] Cui Chang, Zan Tao, Ma Sheng-kai, et al. Thermal image-driven thermal error modeling and compensation in CNC machine tools based on deep attentional residual network[J]. International Journal of Advanced Manufacturing Technology, 2024, 134(7,8): 3153-3169.
[13] Li Hua-dong, Liu Jing-jun, Li Yang. Research on optimization of temperature sensitive points of machine tool thermal error based on independent variable selection criteria[J]. Scientific Reports, 2025, 15(1): 14082.
[14] Benat Inigo, Natalia Colinas-Armijo, Luis-norberto-lopez Lacalle, et al. Digital twin for volumetric thermal error compensation of large machine tools[J]. Sensors, 2024, 24(19): 6196.
[15] 郭世杰, 张学炜, 张楠, 等. 机床主轴热关键点选择与典型转速热误差预测[J]. 吉林大学学报: 工学版,2023, 53(1): 72-81.
Guo Shi-jie, Zhang Xue-wei, Zhang Nan, et al. Thermal key point select and error prediction under typical speed of machine tool spindle[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 72-81.
[16] Wang Yu-peng, Wu Shi, Liu Xian-li, et al. Modeling and analysis of axial thermal error in machining space of double-swing five-axis machine tool[J]. International Journal of Advanced Manufacturing Technology, 2023, 128(11,12): 5179-5194.
[17] Peng Liang, Chen Zhen-lei, Cheng Lei-lei, et al. Research on optimal multivariate thermal error modeling based on finite-element analysis[J]. Proceedings of the Institution of Mechanical Engineers Part E: Journal of Process Mechanical Engineering, 2024, 238(4): 1792-1799.
[18] Weng Ling-tao, Gao Wei-guo, Zhang Da-wei, et al. Thermal analytical modeling of machine tool structural components via dual-layer equivalence[J]. International Journal of Heat and Mass Transfer, 2024, 221: 125083.
[19] Fu Guo-qiang, Zhou Lin-feng, Zheng Yue, et al. Improved unscented Kalman filter algorithm-based rapid identification of thermal errors of machine tool spindle for shortening thermal equilibrium time[J]. Measurement, 2022, 195: 111121.
[20] Wei Xin-yuan, Ye Hong-han, Miao En-ming, et al. Thermal error modeling and compensation based on Gaussian process regression for CNC machine tools[J]. Precision Engineering, 2022, 77: 65-76.
[21] Huang Yu-bin, Hong Hong-you, Zhou Hui-chen, et al. Real-time surrogate compensation architecture for machine-tool thermal error compensation with high-performance model[J]. Discover Applied Sciences, 2025, 7(1): 1-14.
[22] Weng Ling-tao, Gao Wei-guo, Zhang Da-wei, et al. Analytical modelling of transient thermal characteristics of precision machine tools and real-time active thermal control method[J]. International Journal of Machine Tools and Manufacture, 2023, 186: 104003.
[23] Chen Gang, Wang Kun-chieh. Real-time thermal error compensation of machine tools based on machine learning model and actual cutting measurement via temperature sensors[J]. Sensors and Materials, 2024, 36(10): 4221-4238.
[24] Guo Xiao-guang, Wei Yan-jun, Jin Zhu-ji, et al. A numerical model for optical glass cutting based on SPH method[J]. International Journal of Advanced Manufacturing Technology, 2013, 68(5-8): 1277-1283.
[25] Liu Ming, Wu Jia-nan, and Gao Cheng-hui. Sliding of a diamond sphere on K9 glass under progressive load[J]. Journal of Non-Crystalline Solids, 2019, 526: 119711.
[26] Zheng Lian-qing, Schmid Ansger W, Lambropoulos John C. Surface effects on Young's modulus and hardness of fused silica by nanoindentation study[J]. Journal of Materials Science, 2007, 42(1): 191-198.
[27] 张兆顺. 流体力学[M]. 北京: 清华大学出版社, 2002.
[28] 徐芝纶. 弹性力学[M]. 5版. 北京: 高等教育出版社, 2016.
[29] Liu Jia-lan, Ma Chi, Gui Hong-quan, et al. Thermally-induced error compensation of spindle system based on long short term memory neural networks[J]. Applied Soft Computing, 2021, 102: 107094.
[1] Xian-zhen HUANG,Bin GUO,Zhi-yuan JIANG,Ji-wu TANG. Vibration characteristics and precision reliability analysis of high-speed motorized spindle system [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2432-2440.
[2] Chen WANG,Te LUO,Qian-qian HUI,Zhong-hao WANG,Fang-fang WANG. Design and verification of electromechanical system for docking and locking of modular flying vehicle [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2130-2140.
[3] Yan-qin ZHANG,Yi LUO,Shi-qian NI,Ru-kang WU,Zhi-quan ZHANG. High⁃speed characteristics of micro⁃beveled heavy duty hydrostatic bearing lubricant oil film [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 450-457.
[4] Yan-qin ZHANG,Ya-nan FENG,Peng-rui KONG,Xiao-dong YU,Xiang-bin KONG. Temperature field and experiment of hydrostatic bearing oil film based on hot oil carrying [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1203-1211.
[5] Dong⁃liang CHEN,Rui ZANG,Peng DUAN,Wei⁃peng ZHAO,Xu⁃tao WENG,Yang SUN,Yi⁃peng TANG. Biomimetic design of multi⁃link fishbone based on crescent′s fishtail propulsion theory [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1246-1257.
[6] LIU Kun, LIU Yong, YAN Jian-chao, JI Shuo, SUN Zhen-yuan, XU Hong-wei. Dynamic analysis of sit-to-stand human motion based on in vitro-sensor detection [J]. 吉林大学学报(工学版), 2018, 48(4): 1140-1146.
[7] YOSHINO Tatsuo, FAN Lu-lu, YAN Lei, XU Tao, LIN Ye, GUO Gui-kai. Multiobjective optimization design for dummy chest structure based on MBNWS algorithm [J]. 吉林大学学报(工学版), 2018, 48(4): 1133-1139.
[8] ZHENG Yu-bin, YANG Bin, WANG Xiao-feng, SHEN Gui-xiang, ZHAO Xian-zhuo, QIN Meng-meng. Test time design of motorized spindle accelerated life test based on Weibull distribution [J]. 吉林大学学报(工学版), 2018, 48(3): 767-772.
[9] ZHAO De-ming, JIANG Sheng-yuan, TANG De-wei, HOU Xu-yan, DENG Zong-quan. Structure design of lunar subsurface sampling drill [J]. 吉林大学学报(工学版), 2017, 47(4): 1149-1158.
[10] CHEN Dong-hui, LIU Wei, LYU Jian-hua, CHANG Zhi-yong, WU Ting, MU Hai-feng. Bionic design of corn stubble collector based on surface structure of Patinopecten yessoensis [J]. 吉林大学学报(工学版), 2017, 47(4): 1185-1193.
[11] WANG Ying, LI Jian-qiao, ZHANG Guang-quan, HUANG Han, ZOU Meng. Mechanical characteristics of bionic walking foot in different media [J]. 吉林大学学报(工学版), 2017, 47(2): 546-551.
[12] LIU Kun, YE Ming, LI Chao, YAN Peng-fei, LIU Jin-qiao. Design and analysis of an arm mechanical structure or sit-to-stand rehabilitation training [J]. 吉林大学学报(工学版), 2016, 46(5): 1532-1539.
[13] GONG Jun, HE Qing-hua, ZHANG Da-qing, LIU Chang-sheng, ZHAO Yu-ming, HU Peng. Evaluation and testing of electric-hydraulic energy regeneration system of excavator [J]. 吉林大学学报(工学版), 2016, 46(2): 479-486.
[14] TIAN Ye, DENG Zong-quan, TANG De-wei, JIANG Sheng-yuan, QUAN Qi-quan, HOU Xu-yan. Power consumption of lunar subsurface coring driller and earth environment stimulant experiments [J]. 吉林大学学报(工学版), 2016, 46(1): 166-171.
[15] LYU Feng, YANG Yin-sheng, TANG Jin. Integration method for customer requirement analysis in product planning [J]. 吉林大学学报(工学版), 2015, 45(4): 1213-1218.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!