精密加工实验台热误差建模与补偿方法的对比分析

张恩忠; 赵继; 冀世军; 程亚平

doi:10.3788/OPE.20162413.0520

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精密加工实验台热误差建模与补偿方法的对比分析

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- 精密加工实验台热误差建模与补偿方法的对比分析
- Contrastive analysis of modeling and compensation method of thermal error of precision machining experiment table
- 光学精密工程 2016年24卷第10s期页码：520-526
- 作者机构：
  
  1. 吉林大学机械科学与工程学院, 吉林长春 130022
  2. 长春工业大学机电学院,吉林长春,130012
- 作者简介：
- 基金信息：
  
  “973”国家重点基础研究发展计划课题资助项目（No.2011CB706702）()
- DOI：10.3788/OPE.20162413.0520
  中图分类号：
- 收稿日期：2016-05-07，
  
  修回日期：2016-06-17，
  
  纸质出版日期：2016-11-14
- 稿件说明：
移动端阅览
张恩忠, 赵继, 冀世军等. 精密加工实验台热误差建模与补偿方法的对比分析[J]. 光学精密工程, 2016,24(10s): 520-526

ZHANG En-zhong, ZHAO Ji, JI Shi-jun etc. Contrastive analysis of modeling and compensation method of thermal error of precision machining experiment table[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 520-526
张恩忠, 赵继, 冀世军等. 精密加工实验台热误差建模与补偿方法的对比分析[J]. 光学精密工程, 2016,24(10s): 520-526 DOI： 10.3788/OPE.20162413.0520.

ZHANG En-zhong, ZHAO Ji, JI Shi-jun etc. Contrastive analysis of modeling and compensation method of thermal error of precision machining experiment table[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 520-526 DOI： 10.3788/OPE.20162413.0520.

摘要

为了提高精密加工实验台的运行精度，采用激光干涉仪、PT100等仪器测量了四轴数控平台

X、Z

轴的定位误差以及温度，并分析得到了

X、Z

轴定位误差与温度之间的变化规律。分别运用支持向量回归机（SVR）、RBF、BP神经网络方法建立了

X、Z

轴的热误差模型，并进行了对比分析，结果表明SVR模型的拟合性能和泛化能力等明显高于RBF、BP模型。依据SVR、RBF、BP热误差模型进行了误差补偿实验，实验结果表明，数控平台X轴定位误差最大降低了92.3%，Z轴定位误差最大降低了90.3%，SVR的预测能力、补偿精度和鲁棒性等均明显优于RBF、BP神经网络方法。

Abstract

To improve operation accuracy of precision machining experiment table

instrument of laser interferometer and PT100 instrument etc. is adopted to measure position error of X and Z axis of four-axis numerical control platform and temperature. Varying pattern between position error of X and Z axis and temperature is analyzed and gained. Respectively employ support vector regression (SVR)

RBF and BP neural network method to establish thermal error model of X and Z axis and contrastive analysis is performed. Result shows that matching performance and generalization ability of SVR model are obviously greater than those of RBF and BP model. Error compensation experiment is performed according to thermal error model of SVR

RBF and BP and experimental result shows that position error of X axis of numerical control platform decreases by 92.3% to the largest degree

position error of Z axis decreases by 90.3% to the largest degree

and predictive capacity

compensation precision and robustness of SVR are obviously greater than those of RBF and BP neural network method.

关键词

Keywords

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