Research on SFLA–SVM Prediction Method for Feed Axis Thermal Error of CNC Machine Tool

SHI Yanlong; TIAN Chunmiao; A Yongga; JI Zeping; GUO Shijie

doi:10.16080/j.issn1671-833x.2021.22.048

您当前的位置：

首页 >

文章列表页 >

Research on SFLA–SVM Prediction Method for Feed Axis Thermal Error of CNC Machine Tool

更新时间：2025-10-30

- Research on SFLA–SVM Prediction Method for Feed Axis Thermal Error of CNC Machine Tool
- Aeronautical Manufacturing Technology Vol. 64, Issue 22, (2021)
- 作者机构：
  
  1. 内蒙古自治区大数据中心,呼和浩特,010051
  2. 内蒙古工业大学,呼和浩特,010051
- 作者简介：
- 基金信息：
- DOI：10.16080/j.issn1671-833x.2021.22.048
  CLC：
- Published：2021
- 稿件说明：
移动端阅览
石颜龙，田春苗，阿勇嘎，季泽平，郭世杰. 混合蛙跳算法优化SVM的进给轴热误差预测研究[J]. 航空制造技术, 2021, 64(22): 48-. SHI Yanlong, TIAN Chunmiao, A Yongga, JI Zeping, GUO Shijie . Research on SFLA–SVM Prediction Method for Feed Axis Thermal Error of CNC Machine Tool. 航空制造技术, 2021, 64(22): 48-.

SHI Yanlong, TIAN Chunmiao, A Yongga, et al. Research on SFLA–SVM Prediction Method for Feed Axis Thermal Error of CNC Machine Tool[J]. Aeronautical Manufacturing Technology, 2021, 64(22).
石颜龙，田春苗，阿勇嘎，季泽平，郭世杰. 混合蛙跳算法优化SVM的进给轴热误差预测研究[J]. 航空制造技术, 2021, 64(22): 48-. SHI Yanlong, TIAN Chunmiao, A Yongga, JI Zeping, GUO Shijie . Research on SFLA–SVM Prediction Method for Feed Axis Thermal Error of CNC Machine Tool. 航空制造技术, 2021, 64(22): 48-. DOI： 10.16080/j.issn1671-833x.2021.22.048.

SHI Yanlong, TIAN Chunmiao, A Yongga, et al. Research on SFLA–SVM Prediction Method for Feed Axis Thermal Error of CNC Machine Tool[J]. Aeronautical Manufacturing Technology, 2021, 64(22). DOI： 10.16080/j.issn1671-833x.2021.22.048.

摘要

为了更精确地对精密数控机床进给系统热误差进行高性能预测以提高其准静态精度，提出了一种基于混合蛙跳算法优化支持向量机 (SFLA–SVM) 方法，对进给系统热误差模型的关键参数进行寻优。首先，采用谱系聚类法和相对熵提取对进给轴热误差作用较大的测点，实现关键敏感测点优化；其次，基于 SFLA 的 SVM 关键参数精准快速寻优，建立 SFLA–SVM 的热误差预测模型；最后，在精密三轴数控机床上进行预测模型性能对比验证，试验结果显示，相对遗传算法支持向量机（GA–SVM）和遗传算法及 BP 网络 (GA–BP) 方法，基于 SFLA–SVM 预测的热误差均方根误差分别减少 58.53％和 66.0％。

Abstract

In order to accurately predict the thermal error of the feed system of precision CNC machine tools and improve its quasi-static accuracy

a method based on SFLA optimized least squares support vector machine (SFLA–SVM) is proposed to optimize the key parameters of the thermal error model of feed system. Firstly

the sensitive measuring points are selected by using the spectrum clustering method and relative entropy to extract the measuring points which have a great effect on the thermal error of the feed axis. Secondly

the key parameters of SVM based on SFLA are optimized accurately and quickly

and the thermal error prediction model of SFLA–SVM is established. Finally

the performance comparison of the prediction model is carried out on a precision three-axis CNC machine tool. The experimental results show that the root mean square error of the thermal error prediction value of SFLA–SVM is reduced by 58.53% and 66.0% compared with the GA–SVM and GA–BP in the steady state.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Online Accuracy Compensation of Industrial Robots Considering Temperature-Dependent Kinematic Parameters

Separation Method of Completely Adaptive Empirical Mode Decomposition and Wavelet Threshold Transform for Spindle Thermal Error of CNC Machine Tool

Research Advances in Thermal Error Compensation Technology for Motorized Spindle

Comprehensive Error Modeling and Compensation in Manufacturing of Thin-Walled Parts Based on On-Machine Measurement

Summary of Key Technologies for Error Compensation of CNC Machine Tools

Related Author

JIA Jing

JIA Liang

LI Zeyu

XIAO Jiacheng

GUO Shijie

DING Qiangqiang

CHEN Geng

SU Zhe

Related Institution

School of Mechanical Engineering, Inner Mongolia University of Technology

Inner Mongolia Key Laboratory of Robotics and Intelligent Equipment Technology

Tarim University, Alaer

Inner Mongolia Key Laboratory of Robotics and Intelligent Equipment Technology, Inner Mongolia University of Technology

University of Science and Technology Liaoning

The platform construction and operation are provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：34506 Visits Today：453

⁰