scholarly journals A Review of Thermal Error Modeling Methods for Machine Tools

2021 ◽  
Vol 11 (11) ◽  
pp. 5216
Author(s):  
Yang Li ◽  
Maolin Yu ◽  
Yinming Bai ◽  
Zhaoyang Hou ◽  
Wenwu Wu
Keyword(s):  
Machine Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Research Trend ◽  
Future Research ◽  
Thermal Error Modeling ◽  
Modeling Methods ◽  
Compensation Methods ◽  
Tool Compensation ◽  
Thermal Error Compensation

Thermal error caused by thermal deformation is one of the most significant factors influencing the accuracy of the machine tool. Compensation is a practical and efficient method to reduce the thermal error. Among all the thermal error compensation processes, thermal error modeling is the premise and basis because the effectiveness of the compensation is directly determined by the accuracy and robustness of modeling. In this paper, an overview of the thermal error modeling methods that have been researched and applied in the past ten years is presented. First, the modeling principle and compensation methods of machine tools are introduced. Then, the methods are classified and summarized in detail. Finally, the future research trend of thermal error modeling is forecasted.

Application of Autoregressive Distributed Lag (ADL) Model to Thermal Error Modeling on NC Machine Tools

2011 ◽  
Vol 103 ◽  
pp. 9-14 ◽  
Author(s):  
En Ming Miao ◽  
Xin Wang ◽  
Ye Tai Fei ◽  
Yan Yan
Keyword(s):  
Error Compensation ◽  
Machine Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Thermal Error Modeling ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Nc Machine ◽  
Nc Machine Tools ◽  
Thermal Error Compensation

Thermal error modeling method is an important field of thermal error compensation on NC machine tools, it is also a key for improving the machining accuracy of machine tools. The accuracy of the model directly affects the quality of thermal error compensation. On the basis of multiple linear regression (MLR) model, this paper proposes an autoregressive distributed lag (ADL) model of thermal error and establishes an accurate ADL model by stepwise regression analysis. The ADL model of thermal error is established with measured data, it proved the ADL model is available and has a high accuracy on predicting thermal error by comparing with MLR models.

Application of principal component algorithm in spindle thermal error modeling of CNC machine tools

2021 ◽  
Vol 29 (11) ◽  
pp. 2649-2660
Author(s):  
Xin-yuan WEI ◽  
◽  
Yu-chen CHEN ◽  
En-ming MIAO ◽  
Xu-gang FENG ◽  
...  
Keyword(s):  
Machine Tools ◽  
Thermal Error ◽  
Principal Component ◽  
Error Modeling ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Thermal Error Modeling

Temperature Variables Selection for Thermal Error Modeling of Heavy-Duty CNC Milling-Boring Machine Tools

2011 ◽  
Vol 188 ◽  
pp. 171-174
Author(s):  
Gang Wei Cui ◽  
D. Gao ◽  
L. Wang ◽  
Y.X. Yao
Keyword(s):  
Machine Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Cnc Milling ◽  
Heavy Duty ◽  
Thermal Error Modeling ◽  
Selection Strategies ◽  
Variables Selection ◽  
Selection For ◽  
Feed Axis

One of the difficult issues in thermal error modeling is to select appropriate temperature variables. In this paper, two selection strategies are introduced to overcome this difficulty. After measuring the temperatures and thermal errors of a heavy-duty CNC milling-boring machine tool by a laser tracker, four temperature variables which are the foundation of thermal error modeling are selected for each feed axis from fifteen temperature variables according to major factor strategy and non-interrelated strategy.

Clustering Regression Modeling for Gear Hobbing Machine Thermal Error Compensation

2009 ◽  
Vol 416 ◽  
pp. 401-405
Author(s):  
Qian Jian Guo ◽  
Xiao Ni Qi
Keyword(s):  
Error Compensation ◽  
Thermal Error ◽  
Regression Modeling ◽  
Error Model ◽  
Error Modeling ◽  
Machining Accuracy ◽  
Thermal Error Modeling ◽  
Gear Hobbing ◽  
Thermal Error Model ◽  
Thermal Error Compensation

This paper proposes a new thermal error modeling methodology called Clustering Regression Thermal Error Modeling which not only improves the accuracy and robustness but also saves the time and cost of gear hobbing machine thermal error model. The major heat sources causing poor machining accuracy of gear hobbing machine are investigated. Clustering analysis method is applied to reduce the number of temperature sensors. Least squares regression modeling approach is used to build thermal error model for thermal error on-line prediction of gear hobbing machine. Model performance evaluation through thermal error compensation experiments shows that the new methodology has the advantage of higher accuracy and robustness.

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