基于最小二乘支持向量机的外圆磨削表面粗糙度预测系统

黄吉东; 王龙山; 李国发; 张秀芝; 王家忠

doi:10.3788/OPE.20101811.2407

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基于最小二乘支持向量机的外圆磨削表面粗糙度预测系统

微纳技术与精密机械 | 更新时间：2020-08-12

- 基于最小二乘支持向量机的外圆磨削表面粗糙度预测系统
- Prediction system of surface roughness based on LS-SVM in cylindrical longitudinal grinding
- 光学精密工程 2010年18卷第11期页码：2407-2412
- 作者机构：
  
  1. 吉林大学机械科学与工程学院,吉林长春 130020
  2. 北华大学机械工程学院,吉林吉林 132021
  3. 河北农业大学机电工程学院,河北保定 071001
- 作者简介：
- 基金信息：
  
  吉林省科技发展计划资助项目(No.20060534)()
- DOI：10.3788/OPE.20101811.2407
  中图分类号： TG580.6;TP391.4
- 收稿日期：2010-03-10，
  
  修回日期：2010-05-07，
  
  网络出版日期：2010-11-25，
  
  纸质出版日期：2010-11-25
- 稿件说明：
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黄吉东, 王龙山, 李国发, 张秀芝, 王家忠. 基于最小二乘支持向量机的外圆磨削表面粗糙度预测系统[J]. 光学精密工程, 2010,18(11): 2407-2412

HUANG Ji-dong, WANG Long-shan, LI Guo-fa, ZHANG Xiu-zhi, WANG Jia-zhong. Prediction system of surface roughness based on LS-SVM in cylindrical longitudinal grinding[J]. Editorial Office of Optics and Precision Engineering, 2010,18(11): 2407-2412
黄吉东, 王龙山, 李国发, 张秀芝, 王家忠. 基于最小二乘支持向量机的外圆磨削表面粗糙度预测系统[J]. 光学精密工程, 2010,18(11): 2407-2412 DOI： 10.3788/OPE.20101811.2407.

HUANG Ji-dong, WANG Long-shan, LI Guo-fa, ZHANG Xiu-zhi, WANG Jia-zhong. Prediction system of surface roughness based on LS-SVM in cylindrical longitudinal grinding[J]. Editorial Office of Optics and Precision Engineering, 2010,18(11): 2407-2412 DOI： 10.3788/OPE.20101811.2407.

摘要

为解决磨削加工中影响因素多

难以实现自动化加工的困难

对磨削系统的表面粗糙度预测系统进行了研究。在分析目前常用预测方法的基础上

建立了基于最小二乘支持向量机的外圆纵向磨削表面粗糙度预测模型。该模型采用等式约束

把原来求解一个二次规划问题转化成求解一个线性方程组

方法简单且有效。比较实验显示

该方法响应时间快、测量精度高

测量精度误差比BP神经网络预测方法小4%

比进化神经网络(BP+GA)预测方法小1.3%

所提供的预测方法可以实现对工件表面粗糙度的在线预测。将其应用于外圆纵向磨削智能系统中

实时计算预测值与给定粗糙度的差值

引导磨削专家系统修正磨削参数

实现智能控制

取得了较好的效果。

Abstract

A prediction model of surface roughness based on the Least Square Support Vector Machine(LS-SVM) in cylindrical longitudinal grinding is proposed. By converting the inequality constraints into equality constraints

the model transformes solving the SVM from a Quadratic Programming (QP) problem to a group of linear equations

which simplifies the learning process and improves the calculating efficiency. Experimental results indicate that the construction speed of the prediction model based on LS-SVM is more faster

and the measurement error(MSE) is less 4% and 1.3% than those of the BP neutrol algorithm and BP+GA algorithm

respectively.The method has been used in a intelligent system for cylindrical longitudinal grinding to predict the surface roughness of a workpiece in real time. By calculating the differences of predicating values and giving values and by directing the correct of grinding parameters

it completes a closed loop and intelligent control and obtains good grinding results.

关键词

Keywords

references

<td valign='top'>[1]MALKIN S. Grinding Technology Theory and Applications of Machining with Abrasives [M]. Chichester: Eillis Horwood Limited, 1988.<td valign='top'>[2]ALIYM. ZHANG L CH . A methodology for fuzzy modeling of engineering systems [J]. Fuzzy Sets and Systems, 2001,118:181-197.<td valign='top'>[3]刘贵杰,巩亚东,王宛山. 基于神经网络的磨削砂轮状态的在线监测[J]. 东北大学学报,2002,23(10):984-987. LIU G J, GONG Y D, WANG W SH. On-line monitoring for grinding wheel states based on neural network.[J]. Journal of Northeastearn University, 2002,23(10):984-987.(in Chinese)<td valign='top'>[4]李国发,王龙山,丁宁. 基于进化神经网络外圆纵向磨削表面粗糙度的在线预测 [J]. 中国机械工程,2005,16(3):223-226. LI G F, WANG L SH, DING N. On-line prediction of surface roughness in cylindrical longitudinal grinding based on evolutionary neural network [J]. China Mechanical Engineering., 2005,16(3):223-226. (in Chinese).<td valign='top'>[5]王家忠. 外圆纵向智能磨削关键技术研究 . 长春:吉林大学,2006. WANG J ZH.Study on the Key Techniques of Intelligent Cylindrical Traverse Grinding . Changchun:Jilin University,2006. (in Chinese)<td valign='top'>[6]贺秋伟,王龙山,刘庆民,等. 基于支持向量机回归的小尺寸零件精密测量[J]. 光学精密工程,2007,15(4):557-563. HE Q W, WANG L SH, LIU Q M, et al..Precision measurement for small size parts based on support vector regression[J]. Opt. Precision Eng., 2007,15(4):557-563. (in Chinese)<td valign='top'>[7]SUYKENS J A K, VANDEWALLE J. Least square support vector machine classifiers [J].Neural Processing Letters.1999,9(3):293-300<a href='http://dx.doi.org/10.1023/A:1018628609742' target='_blank'><img src='../../images/crossref.gif' width='41' height='15' border='0' /><td valign='top'>[8]SUYKENS J A K, VANDEWALLE J, DE MOOR B. Optimal control by least squares support vector machines [J]. Neural Networks., 2001,14:105-113.<td valign='top'>[9]吴德会. 基于质量信息集成的智能质量控制技术研究 . 合肥:合肥工业大学,2006. WU D H. Research on Intelligent Quality Control Technology Based on Quality Information Integration .Hefei:Hefei University of Technology,2006. (in Chinese)<td valign='top'>[10]BURGES C J C. A tutorial on support vector machines for Pattern recognition [J].Knowledge Discovery and Data Mining,1998,2(2):121-167.

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