精密进给系统热误差的协同训练支持向量机回归建模与补偿方法

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精密进给系统热误差的协同训练支持向量机回归建模与补偿方法

更新时间：2025-07-09

- 精密进给系统热误差的协同训练支持向量机回归建模与补偿方法
- Issue 10, Pages: 40-47(2019)
- 作者机构：
  
  1. 西安交通大学陕西省智能机器人重点实验室
  2. 西安交通大学机械制造系统工程国家重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18;TG533
- Published：2019
- 稿件说明：
移动端阅览
[J]. 2019, (10): 40-47.
DOI：

[J]. 2019, (10): 40-47. DOI：

摘要

针对精密进给系统热误差的数据稀缺且获取成本高的问题

提出了一种基于协同训练支持向量机回归算法(COSVR)的精密进给系统热误差建模与补偿方法。通过整合标记数据(温度和热误差)及未标记温度数据建立热误差模型

利用基于西门子840D数控系统开发的补偿方法进行补偿。以精密镗床双驱动滚珠丝杠进给系统X轴为研究对象

进行热特性实验

获取24 m/min进给速度下的标记数据和12 m/min进给速度下的未标记温度数据

利用COSVR整合所有数据建立热误差模型

并通过遗传算法优化的支持向量机回归算法(GA-SVR)仅选用标记数据建立对照模型

获取18 m/min进给速度下的标记数据用于模型性能测试。结果表明:与GA-SVR模型相比

COSVR模型的均方根误差减少了34.14%

且在100 min和520 min时的误差范围分别减小了62.62%和55.85%。COSVR模型具有更好的预测性能且能更有效地降低热误差

进一步提高了精密进给系统热误差的建模精度。

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references

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