基于多元回归和特征选择的数控机床主轴热误差建模

Chien-Chang Chen, W. Hung
{"title":"基于多元回归和特征选择的数控机床主轴热误差建模","authors":"Chien-Chang Chen, W. Hung","doi":"10.1109/ECICE52819.2021.9645651","DOIUrl":null,"url":null,"abstract":"The positioning precisions of X, Y, and Z machine directions are susceptible to temperature variations around machine tools to shift the cutter positioning when the CNC machine tool spindles during high-speed rotation. In this context, the study proposes a modeling method of thermal error compensation for the displacement of the cutter position. In the X-direction, the mechanical structure is closer to symmetrical form, which evenly distributes the thermal energy, so the thermal error is always small. Therefore, this study only deals with the thermal error in the Y and Z directions. The explanatory power improvement of the multiple regression model largely depends on the feature selection. The paper proposes the backward elimination (BE) algorithm base on mean squares of K-fold errors minimization as feature selection of multiple regression model to establish thermal error compensation modeling. Firstly, BE fits the complete model with all features, and then deletes the feature one by one using the selected test criterion until deleting any feature cannot improve the model explanatory power. The K-fold Cross Validation (KCV) evaluates model performance in limited training data and be used as a criterion for model selection. KCV cut the data into K subsets to keep k-1 subsets as model training, and the remaining subsets as model validation. The procedure is repeated k-times until the last subset is set as the validation set, then the average error across all k trials is computed. To evaluate each feature to be eliminated through KCV, the smallest mean squares error is selected from the N results to determine the variable for elimination each time, where N is the number of features. The multiple regression model was established by using the features selected for the Y and Z axes. Test results show that the method can reduce the peak-to-peak value of thermal error from about 55 μm to below 14 μm in the Y direction, and in Z direction is from about 74 μm to below 19 μm.","PeriodicalId":176225,"journal":{"name":"2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Thermal Error Modeling of CNC Machine Tool Spindle Based on Multiple Regression and Features Selection\",\"authors\":\"Chien-Chang Chen, W. Hung\",\"doi\":\"10.1109/ECICE52819.2021.9645651\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The positioning precisions of X, Y, and Z machine directions are susceptible to temperature variations around machine tools to shift the cutter positioning when the CNC machine tool spindles during high-speed rotation. In this context, the study proposes a modeling method of thermal error compensation for the displacement of the cutter position. In the X-direction, the mechanical structure is closer to symmetrical form, which evenly distributes the thermal energy, so the thermal error is always small. Therefore, this study only deals with the thermal error in the Y and Z directions. The explanatory power improvement of the multiple regression model largely depends on the feature selection. The paper proposes the backward elimination (BE) algorithm base on mean squares of K-fold errors minimization as feature selection of multiple regression model to establish thermal error compensation modeling. Firstly, BE fits the complete model with all features, and then deletes the feature one by one using the selected test criterion until deleting any feature cannot improve the model explanatory power. The K-fold Cross Validation (KCV) evaluates model performance in limited training data and be used as a criterion for model selection. KCV cut the data into K subsets to keep k-1 subsets as model training, and the remaining subsets as model validation. The procedure is repeated k-times until the last subset is set as the validation set, then the average error across all k trials is computed. To evaluate each feature to be eliminated through KCV, the smallest mean squares error is selected from the N results to determine the variable for elimination each time, where N is the number of features. The multiple regression model was established by using the features selected for the Y and Z axes. Test results show that the method can reduce the peak-to-peak value of thermal error from about 55 μm to below 14 μm in the Y direction, and in Z direction is from about 74 μm to below 19 μm.\",\"PeriodicalId\":176225,\"journal\":{\"name\":\"2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECICE52819.2021.9645651\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECICE52819.2021.9645651","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

数控机床主轴高速旋转时,机床X、Y、Z方向的定位精度易受机床周围温度变化的影响而发生刀具定位偏移。在此背景下,提出了一种刀具位置位移热误差补偿的建模方法。在x方向上,机械结构更接近于对称形式,使热能分布均匀,因此热误差始终很小。因此,本研究仅处理Y和Z方向的热误差。多元回归模型解释力的提高很大程度上取决于特征的选择。提出了基于K-fold误差均方最小化的反向消去算法作为多元回归模型的特征选择,建立热误差补偿模型。首先,BE用所有特征拟合完整的模型,然后使用选定的检验准则逐一删除特征,直到删除任何特征都不能提高模型的解释能力。K-fold交叉验证(KCV)在有限的训练数据中评估模型的性能,并用作模型选择的标准。KCV将数据切成K个子集,保留K -1个子集作为模型训练,其余子集作为模型验证。这个过程重复k次,直到最后一个子集被设置为验证集,然后计算所有k次试验的平均误差。为了通过KCV评估每个要消除的特征,从N个结果中选择最小的均方误差来确定每次要消除的变量,其中N为特征的数量。利用选取的Y轴和Z轴特征建立多元回归模型。实验结果表明,该方法可以将Y方向的热误差峰间值从55 μm左右减小到14 μm以下,Z方向的热误差峰间值从74 μm左右减小到19 μm以下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thermal Error Modeling of CNC Machine Tool Spindle Based on Multiple Regression and Features Selection
The positioning precisions of X, Y, and Z machine directions are susceptible to temperature variations around machine tools to shift the cutter positioning when the CNC machine tool spindles during high-speed rotation. In this context, the study proposes a modeling method of thermal error compensation for the displacement of the cutter position. In the X-direction, the mechanical structure is closer to symmetrical form, which evenly distributes the thermal energy, so the thermal error is always small. Therefore, this study only deals with the thermal error in the Y and Z directions. The explanatory power improvement of the multiple regression model largely depends on the feature selection. The paper proposes the backward elimination (BE) algorithm base on mean squares of K-fold errors minimization as feature selection of multiple regression model to establish thermal error compensation modeling. Firstly, BE fits the complete model with all features, and then deletes the feature one by one using the selected test criterion until deleting any feature cannot improve the model explanatory power. The K-fold Cross Validation (KCV) evaluates model performance in limited training data and be used as a criterion for model selection. KCV cut the data into K subsets to keep k-1 subsets as model training, and the remaining subsets as model validation. The procedure is repeated k-times until the last subset is set as the validation set, then the average error across all k trials is computed. To evaluate each feature to be eliminated through KCV, the smallest mean squares error is selected from the N results to determine the variable for elimination each time, where N is the number of features. The multiple regression model was established by using the features selected for the Y and Z axes. Test results show that the method can reduce the peak-to-peak value of thermal error from about 55 μm to below 14 μm in the Y direction, and in Z direction is from about 74 μm to below 19 μm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信