吉林大学学报(工学版) ›› 2026, Vol. 56 ›› Issue (10): 2546-2553.doi: 10.13229/j.cnki.jdxbgxb.20250780

• 车辆工程·机械工程 • 上一篇    

抛光盘表面修整热误差的流体压力补偿方法

赵凌宇1(),高泽村1,赵惠英2   

  1. 1.北京石油化工学院 机械工程学院,北京 102627
    2.西安交通大学 机械工程学院,西安 710049
  • 收稿日期:2025-09-01 出版日期:2026-10-01 发布日期:2026-10-09
  • 作者简介:赵凌宇(1987-),男,讲师,博士. 研究方向:超精密加工技术和平面光学元件抛光加工.E-mail:1291437100@qq.com
  • 基金资助:
    国家科技重大专项项目(2024ZD0708101);北京市教育委员会科研计划项目(KM202410017010)

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

摘要:

针对大口径平面光学元件连续抛光加工过程中Φ1 200 mm口径金属抛光盘表面修整的热变形问题,提出了一种利用流体压力实现金属抛光盘表面修整热误差的补偿方法。设计Φ1 200 mm口径金属抛光盘表面的流体压力调控系统结构,针对抛光盘表面热变形、抛光盘表面流体压力调控规律以及流体压力调控的抛光盘表面热误差补偿开展实验研究。试验结果表明,通过调节流体压力可以实现抛光盘表面热位移误差补偿,当进液口压力为0.10 MPa时,Φ1 200 mm直径抛光盘表面热误差补偿效果最好,峰谷值从原始的24 μm降低到14 μm,热误差降低了42%。

关键词: 超精密加工, 热误差补偿, 平面光学元件, 流体压力调控, 平面抛光

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

中图分类号: 

  • TH122

图1

抛光盘表面流体压力调控物理模型"

图2

抛光盘表面的流体压力变形简化模型"

图3

抛光盘表面流体压力调控系统实物"

表1

抛光盘表面原始热位移试验"

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

表2

不同作用时间的抛光盘表面流体压力变形试验参数"

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

图4

抛光盘表面的流体压力调控试验方案"

图5

抛光盘表面的流体压力调控试验台"

表3

抛光盘表面轮廓热误差补偿的试验参数"

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

图6

转台工作热功率计算结果"

图7

转台不同转速下抛光盘表面的热误差"

图8

流体压力不同作用时间的抛光盘表面变形"

图9

不同流体压力作用下抛光盘表面变形"

图10

抛光盘表面流体压力变形的最大值和平均值"

图11

抛光盘表面热位移误差补偿结果"

图12

抛光盘表面热位移误差补偿结果"

[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.
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