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

doi:10.3788/OPE.20152306.1705

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

更新时间：2020-08-13

- Application of generalized radial basis function neural network to thermal error modeling
- Optics and Precision Engineering Vol. 23, Issue 6, Pages: 1705-1713(2015)
- 作者机构：
  
  湖南大学汽车车身先进设计制造国家重点实验室,湖南长沙,410082
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152306.1705
  CLC： TG596;TP183
- Received：15 October 2014，
  
  Revised：09 December 2014，
  
  Published：25 June 2015
- 稿件说明：
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吕程, 刘子云, 刘子建等. 广义径向基函数神经网络在热误差建模中的应用[J]. 光学精密工程, 2015,23(6): 1705-1713

L&#220, Cheng, LIU Zi-yun etc. Application of generalized radial basis function neural network to thermal error modeling[J]. Editorial Office of Optics and Precision Engineering, 2015,23(6): 1705-1713
吕程, 刘子云, 刘子建等. 广义径向基函数神经网络在热误差建模中的应用[J]. 光学精密工程, 2015,23(6): 1705-1713 DOI： 10.3788/OPE.20152306.1705.

L&#220, Cheng, LIU Zi-yun etc. Application of generalized radial basis function neural network to thermal error modeling[J]. Editorial Office of Optics and Precision Engineering, 2015,23(6): 1705-1713 DOI： 10.3788/OPE.20152306.1705.

摘要

针对现有的热误差建模方法建模效率低

模型预测精度不理想等问题

提出了广义径向基函数神经网络(RBF)建模方法并将其应用于数控机床热误差建模中.讨论了采用广义RBF神经网络进行热误差建模的原理及步骤.以数控导轨磨床主轴箱系统为例

布置了12个主轴热误差的关键温度测点

测得了2组独立的主轴箱系统热误差数据.将测得的数据分别用于建立主轴箱系统热误差广义RBF神经网络预报模型和验证模型的准确性.研究结果表明

热误差广义RBF神经网络模型具有预测精度高及泛化能力强的优点;与传统的RBF神经网络建模方法相比

提出的广义RBF神经网络建模方法建模效率更高

模型鲁棒性及预测性能更好

是一种可以用于数控机床热误差实时补偿的有效建模方法.

Abstract

In considering of the lower efficiency and worse prediction accuracy of the existing thermal error modeling methods

a generalized Radial Basis Function (RBF) neural network modeling approach was proposed to establish the thermal error model of Numerical Control(NC) machine tools. The model theory and the corresponding steps based on this method were discussed. An experiment on the spindle box of a NC guide rail grinder was performed

and two groups of independent thermal error data were obtained by setting twelve critical temperature measuring points of spindle thermal error. One group of the data was used for building the thermal error model of the spindle box system based on generalized RBF neural network method and the other was used for verifying the correction of the model. The study results show that the thermal error model based on generalized RBF neural network method has high prediction precision and good generalization ability. By comparing the generalized RBF neural network method with traditional RBF neural network modeling method

the former shows better efficiency

robustness and prediction capacity

and it is an effective modeling method for the real-time thermal error compensation of NC machine tools.

关键词

Keywords

references

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