基于模糊聚类与偏相关分析的机床温度测点优化

赵瑞月; 梁睿君; 叶文华

基于模糊聚类与偏相关分析的机床温度测点优化

1.
南京航空航天大学机电学院,南京 210016

基金项目:

南京航空航天大学基本科研业务费专项科研项目(NS2010122)资助

详细信息

作者简介:
赵瑞月(1986-)，硕士，研究方向为数控机床热误差建模与补偿，zry3786265@163.com:叶文华(联系人)，教授，博士生导师，whye@nuaa.edu.cn

赵瑞月(1986-)，硕士，研究方向为数控机床热误差建模与补偿，zry3786265@163.com:叶文华(联系人)，教授，博士生导师，whye@nuaa.edu.cn

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出版历程
- 收稿日期: 2011-06-16
- 刊出日期: 2015-06-10

Optimization of Temperature Measuring Points for Machine Tool Based on Fuzzy Clustering and Partial Correlation Analysis

1.
College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016

摘要

摘要: 机床温度测点优化是热误差补偿技术的难点之一,采用模糊聚类与偏相关分析相结合的方法对温度测点优化。首先利用模糊聚类和F统计量对温度变量进行分类,再根据温度变量与热误差之间的偏相关系数确定每类中的关键温度变量,最后采用关键温度变量建立热误差线性回归模型。此方法在精密卧式加工中心MCH63上进行了验证。结果表明,该方法有效地减少了温度测点的数量,机床轴向热误差由45μm左右减少到7μm。此外,利用F统计量对阈值进行优化,可以快速准确地确定温度变量的分类。
- 热误差 /
- 模糊聚类 /
- 偏相关分析 /
- 测点优化
Abstract: Optimization of temperature measuring points for machine tool is one of the difficulties of thermal error compensation.Fuzzy group combined with partial correlation analysis was adopted to optimize the temperature measuring points.Temperature measuring points were classified by fuzzy group analysis and F-statistics firstly.Then according to the partial correlation coefficient between thermal error and the temperature measuring points,the key temperature variables are determined.Lastly,by using these key temperature variables,linear regression model of thermal error was established to test prediction accuracy.This method was tested in precision horizontal machining center MCH63.The result shows as following,this method reduces the number of temperature measuring points efficiently;thermal error reduces from about 45 μm to 7 μm.Threshold which is optimized by F-statistics,can quickly and exactly determine the grouping result of the temperature variables.
- thermal error /
- fuzzy group /
- partial correlation analysis /
- measuring points optimization

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参考文献(11)

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