精密坐标镗床进给系统热误差分析与预测

王新孟; 杨军; 梅雪松; 雷默涵; 赵亮; 施虎

doi:10.7652/xjtuxb201510004

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精密坐标镗床进给系统热误差分析与预测

更新时间：2025-07-09

- 精密坐标镗床进给系统热误差分析与预测
- Analysis and Prediction for Thermal Error of Precision Coordinate Boring Machine
- 西安交通大学学报 2015年49卷第10期页码：22-28
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2015-04-22。作者简介: 王新孟(1990—),男,硕士生;杨军(通信作者),男,讲师。基金项目: 国家重点基础研究发展计划资助项目(2011CB706805);国家重大科技成果转化资助项目(2012HZ01)
- DOI：10.7652/xjtuxb201510004
  中图分类号： TH161;TG532
- 网络首发：2015-10-10，
  
  纸质出版：2015
- 稿件说明：
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王新孟, 杨军, 梅雪松, 等. 精密坐标镗床进给系统热误差分析与预测[J]. 西安交通大学学报, 2015,49(10):22-28.

Analysis and Prediction for Thermal Error of Precision Coordinate Boring Machine[J]. 2015, 49(10): 22-28.
王新孟, 杨军, 梅雪松, 等. 精密坐标镗床进给系统热误差分析与预测[J]. 西安交通大学学报, 2015,49(10):22-28. DOI： 10.7652/xjtuxb201510004.

Analysis and Prediction for Thermal Error of Precision Coordinate Boring Machine[J]. 2015, 49(10): 22-28. DOI： 10.7652/xjtuxb201510004.

摘要

为了预测数控机床运行时热误差对进给系统定位精度的影响

以精密坐标镗床为研究对象

采用红外热像仪和激光干涉仪分别测量进给系统在每个测点的丝杠温度和定位精度

提出进给系统热误差的最小二乘支持向量机(LS-SVM)预测方法

建立了关于温度与位置的预测模型。模型引入最小二乘支持向量机方法对机床进给系统热误差进行预测分析

较好地描述了进给轴热误差与温度、位置之间的非线性关系

且对样本的依赖度小

有很好的泛化能力

解决了目前线性拟合模型用特征平均温度替代当前测点温度进行计算而存在较大误差的问题。实验结果表明

与目前已经在数控机床上实际应用的线性预测模型相比

LS-SVM模型对进给系统热误差的预测精度可达90%

预测精度提高30%以上

取得了非常好的预测效果

具有较高的现实应用价值。

Abstract

To predict the effect of heat deviation on positioning accuracy of feed system in a running CNC machine

for a precision coordinate boring machine

the temperature accuracy in every measuring point of the system is measured by infrared thermal imager and the positioning accuracy is measured by laser interferometer simultaneously

then the least squares support vector machine(LS-SVM)prediction method of thermal error in feed system is adopted to establish a prediction model of temperature and location. LS-SVM prediction method is introduced into the prediction model to predict the thermal error in the feed system. The model well describes the nonlinear relationship between the thermal errors of position and temperature with slight dependence on samples and good generalization ability. The difficulty that the prediction error gets greater because the average characteristic temperature is considered instead of the current measuring point temperature in establishing linear prediction model is solved. The results show that the prediction precision of thermal error by LS-SVM model reaches 90%

30% higher than that by the existing linear prediction model for NC machine.

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references

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