重型数控机床热误差建模及预测方法的研究

doi:10.3901/JME.2016.11.154

机械工程学报 ›› 2016, Vol. 52 ›› Issue (11): 154-160.doi: 10.3901/JME.2016.11.154

扫码分享

重型数控机床热误差建模及预测方法的研究

李逢春, 王海同, 李铁民

清华大学精密超精密制造装备及控制北京市重点实验室北京 100084

出版日期:2016-06-05 发布日期:2016-06-05
作者简介:李逢春,男,1991年出生。主要研究方向为数控机床热误差分析。E-mail：fengchunli91@gmail.com
基金资助:
国家科技重大专项(2013ZX04013011, 2015ZX04011002)、国家重点基础研究发展计划(973计划,2011CB302400)、国家自然科学基金(51275260)和清华大学自主科研计划(2014z22068)资助项目

Research on Thermal Error Modeling and Prediction of Heavy CNC Machine Tools

LI Fengchun, WANG Haitong, LI Tiemin

Beijing Key Lab of Precision/Uitra-precision Manufacturing Equipment and Control, Tsinghua University, Beijing 100084

Online:2016-06-05 Published:2016-06-05

摘要/Abstract

摘要： 重型数控机床的热误差已经成为影响其加工精度的一个关键问题。针对一台典型的重型落地铣镗床,以机床热误差测量试验为依据,分析该类机床温度场的特点;据此提出一种旨在完成高效温度测点优化的改进系统聚类方法,该方法使用一种兼顾欧氏距离和相关系数的系统聚类准则,可以有效地降低优化后温度测点之间的共线性。基于优化后的温度测点,利用多元线性回归分析,构建了机床的热误差预测模型。现场试验数据表明,该方法可以将热误差预测的均方根误差降低到10 μm以下,相较于其他方法有着更高的热误差预测精度,有望在其他重型数控机床的热误差建模和预测研究中得到更大的推广应用。

关键词: 热误差建模, 温度测点优化, 系统聚类方法, 重型数控机床

Abstract: Thermal error has been a significant factor influencing the accuracy of heavy CNC machine tools. A thermal experiment is performed on a typical heavy CNC machine tool. According to the temperature field of the machine tool, a new hierarchical clustering method is proposed to optimize temperature variables efficiently. Euler distance and correlation coefficient are both considered in the method, by which the collinearity of temperature variables is reduced. Thermal error prediction model is built based on optimized temperature variables utilizing MRA. Results show that the RMSE of thermal error prediction can be reduced to lower than 10 μm, which has higher accuracy compared to other methods. This method is expected to be used in thermal error modeling and prediction of other heavy CNC machine tools.

Key words: heavy CNC machine tool, hierarchical clustering method, temperature variable optimization, thermal error modeling

中图分类号:

TG502

李逢春, 王海同, 李铁民. 重型数控机床热误差建模及预测方法的研究[J]. 机械工程学报, 2016, 52(11): 154-160.

LI Fengchun, WANG Haitong, LI Tiemin. Research on Thermal Error Modeling and Prediction of Heavy CNC Machine Tools[J]. Journal of Mechanical Engineering, 2016, 52(11): 154-160.

参考文献

[1] BRYAN J B. International status of thermal error research[C]. Annals of CIRP,1990：39(2)：645-656.
[2] RAMESH R,MANNAN M A,POO A N. Error compensation in machine tools-a review：Part II：Thermal errors[J]. International Journal of Machine Tools & Manufacture,2000,40(9)：1257-1284.
[3] MORIWAKI T. Thermal deformation and its on-line compensation of hydrostatically supported precision spindle[J]. Manufacturing Technology,1988,37(1)：393-396.
[4] DONALDSON R R,THOMPSON D C,LOEWEN E G,Design and performance of a small precision CNC turning machine[J]. Manufacturing Technology,1986,35(1)：373-376.
[5] LO C H,YUAN J X,NI J. Optimal temperature variable selection by grouping approach for thermal error modeling and compensation[J]. International Journal of Machine Tools & Manufacture,1999,39(9)：1383-1396.
[6] HAN J,WANG L P,WANG H T,et al. A new thermal error modeling method for CNC machine tools[J]. International Journal of Advanced Manufacturing Technology,2012,62(1-4)：205-212.
[7] 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)：22-26.
ZHU Xiaolong,YANG Jianguo,DAI Guisong. AVQ clustering algorithm and OIF-Elman neural network for machine tool thermal error[J]. Journal of Shanghai Jiao Tong University,2014,,4(1)：22-26.
[8] 凡志磊,李中华,杨建国. 基于偏相关分析的数控机床温度布点优化及其热误差建模[J]. 中国机械工程,2010(17)：2025-2028.
FAN Zhilei,LI Zhonghua,YANG Jianguo. NC machine tool temperature measuring point optimization and thermal error modeling based on partial correlation analysis[J]. China Mechanical Engineering,2010,21(17)：2025-2028.
[9] 沈岳熙,杨建国. 基于岭回归的数控机床温度布点优化及其热误差建模[J]. 机床与液压,2012,40(5)：13-17.
SHEN Yuexi,YANG Jianguo. Temperature measuring point optimization and thermal error modeling for NC machine tool based on ridge regression[J]. Machine Tool & Hydraulics,2012(5)：13-17.
[10] 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 Wei,YE Wenhua. 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.
[11] YANG H,NI J. Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error[J]. International Journal of Machine Tools & Manufacture,2005,45(4-5)：455-465.
[12] 林伟青,傅建中,陈子辰,等. 数控机床热误差的动态自适应加权最小二乘支持矢量机建模方法[J]. 机械工程学报,2009,45(3)：178-182.
LIN Weiqing,FU Jianzhong,CHEN Zichen,et al. Modeling of NC machine tool thermal error based on adaptive best-fitting WLS-SVM[J]. Journal of Mechanical Engineering,2009,45(3)：178-182.
[13] 姜辉,杨建国,姚晓栋,等. 数控机床主轴热漂移误差基于贝叶斯推断的最小二乘支持向量机建模[J]. 机械工程学报,2013,49(15)：115-121.
JIANG Hui,YANG Jianguo,YAO Xiaodong,et al. Modeling of CNC machine tool spindle thermal distortion with LS-SVM Based on Bayesian inference[J]. Journal of Mechanical Engineering,2013,49(15)：115-121.
[14] YANG H,NI J. Dynamic modeling for machine tool thermal error compensation[J]. Journal of Manufacturing Science and Engineering,2003,125(2)：245-254.
[15] 张毅,杨建国. 基于灰色理论预处理的神经网络机床热误差建模[J]. 机械工程学报,2011,47(7)：134-139.
ZHANG Yi, YANG Jianguo. Modeling for machine tool thermal error based on grey model preprocessing neural network[J]. Journal of Mechanical Engineering,2011,47(7)：134-139.
[16] 项四通,杨建国,张毅. 基于机理分析和热特性基本单元试验的机床主轴热误差建模[J]. 机械工程学报,2014,50(11)：144-152.
XIANG Sitong,YANG Jianguo,ZHANG Yi. Modeling method for spindle thermal error based on mechanism analysis and thermal basic characteristics tests[J]. Journal of Mechanical Engineering,2014,50(11)：144-152.
[17] 高新波. 模糊聚类分析及其应用[M]. 西安：西安电子科技大学出版社,2004.
GAO Xinbo. Fuzzy cluster analysis and its applications[M]. Xi’an：Xidian University Press,2004.

重型数控机床热误差建模及预测方法的研究

Research on Thermal Error Modeling and Prediction of Heavy CNC Machine Tools

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

[1]	张建涛, 刘志峰, 李彦生, 姜凯, 杨聪彬, 张彩霞. 基于相似理论的重型数控机床-基础系统位移变形研究[J]. 机械工程学报, 2022, 58(7): 309-316.
[2]	夏军勇;胡友民;吴波;史铁林. 热弹性效应分析与机床进给系统热动态特性建模[J]. , 2010, 46(15): 191-198.