Optimal sensor placement for thermal error identification of NC machine tools based on LS-SVM and genetic algorithms

LIN Wei-Qing; FU Jian-zhong; Chen Zi-Chen

doi:null

您当前的位置：

首页 >

文章列表页 >

Optimal sensor placement for thermal error identification of NC machine tools based on LS-SVM and genetic algorithms

Article | 更新时间：2020-08-12

- Optimal sensor placement for thermal error identification of NC machine tools based on LS-SVM and genetic algorithms
- Optics and Precision Engineering Vol. 16, Issue 9, Pages: 1682-1687(2008)
- 作者机构：
  
  1. 浙江大学机械工程系
  2. 福建农林大学机电学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Received：21 November 2007，
  
  Revised：21 March 2008，
  
  Published Online：25 September 2008，
  
  Published：25 September 2008
- 稿件说明：
移动端阅览
林伟青,傅建中,陈子辰. 基于LS-SVM与遗传算法的数控机床热误差辨识温度传感器优化策略[J]. 光学精密工程, 2008,16(9):1682-1687

LIN Wei-Qing, FU Jian-zhong, Chen Zi-Chen. Optimal sensor placement for thermal error identification of NC machine tools based on LS-SVM and genetic algorithms[J]. Optics and precision engineering, 2008, 16(9): 1682-1687.
林伟青,傅建中,陈子辰. 基于LS-SVM与遗传算法的数控机床热误差辨识温度传感器优化策略[J]. 光学精密工程, 2008,16(9):1682-1687 DOI：

LIN Wei-Qing, FU Jian-zhong, Chen Zi-Chen. Optimal sensor placement for thermal error identification of NC machine tools based on LS-SVM and genetic algorithms[J]. Optics and precision engineering, 2008, 16(9): 1682-1687. DOI：

摘要

提出一种在数控机床热误差辨识建模过程中，利用最小二乘支持向量机结合遗传算法对温度传感器进行筛选与优化的新方法。对布置在一台数控车床上的温度传感器进行了优化，首先根据热模态理论，对传感器进行分组，利用最小二乘支持向量机方法构建数控机床热误差辨识模型，再根据遗传算法对其进行传感器优化布置。结果表明，遗传算法与最小二乘支持向量机方法的结合，不但很好地避免温度测点的相互影响，保证模型精度，而且节约了硬件成本，提高了辨识建模速度。

Abstract

A novel method based on least squares support vector machine and genetic algorithm to select temperature sensors of NC machine tool was presented. The measurement points were grouped based on the thermal mode theory. Then the genetic algorithm was used to determine the optimum sensors’ positions. Thermal error regression model was given by the least squares support vector machine (LS-SVM) finally. The compensation model is made for machine tools. The result shows that the two methods of genetic algorithms and LS-SVM were combined well which not only avoided the correlation of the temperature sensors and ensured the accuracy of the model but also saved the cost and shortened the modeling time.

关键词

Keywords

references

Views

447

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Vernier effect based on optical fiber Sagnac interference loop with two angle shift spliced polarization maintaining fibers and its application on temperature sensor

Measurement of particles by optical fiber coupling

Fully digital smart temperature sensor with quartz tuning fork resonator

Micro integrated pressure, temperature, and relative humidity sensor using adhesive bonding with SU-8

A temperature sensor array based on flexible MEMS skin technology

Related Author

Zhen-ming DING

Bin-qing WU

Chun-liu ZHAO

CHEN Bao-xue

CHENG Li

SUI Guo-rong

XU Jun

YOU Bo

Related Institution

College of Optical and Electronic Technology, China Jiliang University

School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology

哈尔滨理工大学自动化学院, 黑龙江哈尔滨 150080

哈尔滨理工大学测控技术与通信工程学院,黑龙江哈尔滨 150080

哈尔滨理工大学计算中心,黑龙江哈尔滨 150080

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 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.

⁰