Thermal error modeling for spindle system of precision CNC lathe

Chen-guang GUO; Xue HAN; Yuan LI; Hua-long XIE

doi:10.3788/OPE.20162407.1731

您当前的位置：

首页 >

文章列表页 >

Thermal error modeling for spindle system of precision CNC lathe

Micro/Nano Technology and Fine Mechanics | 更新时间：2020-08-13

- Thermal error modeling for spindle system of precision CNC lathe
- Optics and Precision Engineering Vol. 24, Issue 7, Pages: 1731-1742(2016)
- 作者机构：
  
  1.辽宁工程技术大学机械工程学院, 辽宁阜新 123000
  2.东北大学先进制造与自动化技术研究所, 辽宁沈阳 110819
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162407.1731
  CLC： TG502.15;TG659
- Received：26 January 2016，
  
  Accepted：31 March 2016，
  
  Published：2016-07
- 稿件说明：
移动端阅览
Chen-guang GUO, Xue HAN, Yuan LI, et al. Thermal error modeling for spindle system of precision CNC lathe[J]. Optics and precision engineering, 2016, 24(7): 1731-1742.
DOI：

Chen-guang GUO, Xue HAN, Yuan LI, et al. Thermal error modeling for spindle system of precision CNC lathe[J]. Optics and precision engineering, 2016, 24(7): 1731-1742. DOI： 10.3788/OPE.20162407.1731.

摘要

开展了精密数控车床主轴系统热误差补偿的实验与建模方法的研究。建立了精密数控车床主轴系统轴向与径向偏转热误差补偿模型以增强其误差补偿能力，并提高机床加工精度。构建了主轴系统热误差测试平台，应用五点法测试主轴系统热误差，使用热电偶与红外热像仪测量主轴系统温升关键点温度变化数据，应用灰色综合关联分析法实现温度敏感测点辨识。构建了基于粒子滤波重采样粒子群算法的热误差预测模型，对模型预测效果进行评价。结果表明：基于粒子滤波重采样粒子群热误差补偿模型得到的轴向热误差预测残差为-1.29

m～1.55

m，建模精度为95.04%；

向热偏转误差预测残差为-4.68×10

-6

°～9.66×10

-6

建模精度为91.26%；

向热偏转误差预测残差为-5.83×10

-6

°～8.59×10

-6

建模精度为93.24%。实验结果证明该热误差补偿模型具有较高的预测精度，具有较强的工程应用价值。

Abstract

The experiments and modeling of thermal error compensation for the spindle system of a Computer Numerical Control (CNC) lathe were researched. A thermal error compensation model for the Spindle system of CNC lathe at axial and radial directions was established to enhance its error compensation ability and to improve the machining precision. A test platform for the thermal error of the spindle system was built. The five point method was used to test the thermal error of the spindle system

and a thermocouple and a infrared thermal imager were taken to measure the temperature changes of the spindle system. Then the gray comprehensive correlation analysis method was used to identify the temperature-sensitive measurement points and to construct thermal error prediction model based on re-sampling step particle swam optimization to evaluate the model effect. The prediction results on the thermal error compensation model based on re-sampling step particle swam optimization show that the axial residual thermal error is-1.29

m-1.55

and the modeling accuracy is 95.04%. The thermal residual error along

direction is-4.68×10

-6

°-9.66×10

-6

and the modeling accuracy is 91.26%. The thermal residual error along

direction is-5.83×10

-6

°-8.59×10

-6

and the modeling accuracy is 93.24%. The research shows that the thermal error compensation model has high precision and a strong engineering application value.

关键词

Keywords

references

RAMESH R, MANNAN M A, POO A N. Error compensation in machine tools-a review partⅡ:thermal error[J]. International Journal of Machine Tools and Manufacture, 2000, 40(9):1257-1284.

JOSEF M, JERZY J, ECKART U, et al. Thermal issues in machine tools[J]. CIRP Annals Manufacturing Technology, 2012, 61(2):771-791.

王海同, 李铁民, 王立平, 等.机床热误差建模研究综述[J].机械工程学报, 2015, 51(9):119-128.

WANG H T, LI T M, WANG L P, et al. Review on thermal error modeling of machine tools[J]. Journal of Mechanical Engineering, 2015, 51(9):119-128.(in Chinese)

张成新, 高峰, 李艳, 等.基于分段拟合的机床大尺寸工作台热误差补偿模型[J].机械工程学报, 2015, 51(3):146-152.

ZHANG CH X, GAO F, LI Y, et al. Model of thermal error compensation of large size worktable for machine tools based on piecewise fitting[J]. Journal of Mechanical Engineering, 2015, 51(3):146-152.(in Chinese)

要小鹏, 殷国富, 李光明.基于OE-CM算法的机床主轴热误差建模与补偿分析[J].中国机械工程, 2015, 26(20):2757-2762.

YAO X P, YIN G F, LI G M. Thermal error modeling and compensation analysis based on OE-CM algorithm for machine tool spindles[J]. China Mechanical Engineering, 2015, 26(20):2757-2762.(in Chinese)

王乾俸, 张松, 陈舟, 等.基于指数函数的机床主轴热误差补偿模型[J].计算机集成制造系统, 2015, 21(6):1553-1558.

WANG Q F, ZHANG S, CHEN ZH, et al. Thermal error compensation model of machine spindle based on exponential function[J]. Computer Integrated Manufacturing Systems, 2015, 21(6):1553-1558.(in Chinese)

朱小龙, 杨建国, 代贵松.基于AVQ聚类和OIF-Elman神经网络的机床热误差建模[J].上海交通大学学报, 2014, 48(1):16-21.

ZHU X L, YANG J G, DAI G S. AVQ clustering algorithm and OIF-Elman neural network for machine tool thermal error[J]. Journal of Shanghai Jiao Tong University, 2014, 48(1):16-21.(in Chinese)

陈诚, 杨传民, 张晨阳, 等.考虑运行条件的Elman网络丝杠驱动系统热误差建模[J].光学精密工程, 2014, 22(3):704-711.

CHEN CH, YANG CH M, ZHANF CH Y, et al. Modeling on thermal errors of ball screw driving system on Elman network considering operating conditions[J]. Opt. Precision Eng., 2014, 22(3):704-711.(in Chinese)

林献坤, 王益涵, 朱琳.应用潜变量回归在线补偿双直接进给轴热误差[J].光学精密工程, 2015, 23(2):430-437.

LIN X K, WANG Y H, ZHU L. Online compensation of thermal error for dual direct feed drive system with latent variable model[J]. Opt. Precision Eng., 2015, 23(2):430-437.(in Chinese)

张伟, 叶文华.基于灰色关联和模糊聚类的机床温度测点优化[J].中国机械工程, 2014, 25(4):456-460.

ZHANG W, YE W H. Optimization of temperature measuring points for machine tools based on grey correlation and fuzzy clustering analysis[J]. China Mechanical Engineering, 2014, 25(4):456-460.(in Chinese)

马驰, 杨军, 梅雪松, 等.基于遗传算法及BP网络的主轴热误差建模[J].计算机集成制造系统, 2015, 21(10):2627-2636.

MA CH, YANG J, MEI X S, et al. High-speed spindle thermal error modeling based on genetic algorithm and BP neural network[J]. Computer Integrated Manufacturing Systems, 2015, 21(10):2627-2636.(in Chinese)

杨军, 梅雪松, 赵亮, 等.基于模糊聚类测点优化与向量机的坐标镗床热误差建模[J].上海交通大学学报, 2014, 48(8):20-28.

YANG J, MEI X S, ZHAO L, et al. Thermal error modeling of a coordinate boring machine based on fuzzy clustering and SVM[J]. Journal of Shanghai Jiao Tong University, 2014, 48(8):20-28.(in Chinese)

苗恩铭, 龚亚运, 成天驹, 等.支持向量回归机在数控加工中心热误差建模中的应用[J].光学精密工程, 2013, 21(4):980-986.

MIAO E M, GONG Y Y, CHENG T J, et al. Application of support vector regression machine to thermal error modelling of machine tools[J]. Opt. Precision Eng., 2013, 21(4):980-986.(in Chinese)

仇健, 刘春时, 刘启伟, 等.龙门数控机床主轴热误差及其改善措施[J].机械工程学报, 2012, 48(21):149-157.

QIU J, LIU CH SH, LIU Q W, et al. Thermal errors of planer type NC machine tools and its improvement measures[J]. Journal of Mechanical Engineering, 2012, 48(21):149-157.(in Chinese)

刘思峰, 党耀国, 方志耕, 等.灰色系统理论及其应用[M].第5版, 北京:科学出版社, 2010.

LIU S F, DANG Y G, FANG ZH G, et al. Gray System Theory and Its Application[M]. 5 th ed. Beijing:Science Press, 2010.(in Chinese)

董菲菲, 罗贤运, 吕保和.天然气管道内腐蚀影响因素的灰色综合关联度分析[J].安全与环境学报, 2014, 14(5):15-18.

DONG F F, LUO X Y, LV B H.Analysis of impact factors of natural gas pipeline internal corrosion based on the grey comprehensive correlation[J].Journal of Safety and Environment, 2014, 14(5):15-18.(in Chinese)

CLERE M, KENNEDY J. The particle swarm-explosion, stability and convergence in multidimesional complex space[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(1):58-73.

WAI R, LEE J, CHUANG K. Real-time PID control strategy for maglev transportation system via particle swarm optimization[J].IEEE Transactions on Industrial Electronics, 2010, 58(2):629-646.

SHARMA K D, CHATTERJEE A, RAKSHIT A. A hybrid approach for design of stable adaptive fuzzy controllers employing lyapunov theory and particle swarm optimization[J].IEEE Transactions on Fuzzy Systems, 2009, 17(2):329-342.

KARANKI S B, MISHRA M K, KUMAR B K. Particle swarm optimization based feedback controller for unified power quality conditioner[J].IEEE Transactions on Power Delivery, 2010, 25(4):2814-2824.

Views

407

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Application of generalized radial basis function neural network to thermal error modeling

Related Author

吕程

刘子云

刘子建

余治民

Related Institution

State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰