scholarly journals A Temperature Sensor Clustering Method for Thermal Error Modeling of Heavy Milling Machine Tools

2017 ◽  
Vol 7 (1) ◽  
pp. 82 ◽  
Author(s):  
Fengchun Li ◽  
Tiemin Li ◽  
Haitong Wang ◽  
Yao Jiang
Keyword(s):  
Temperature Sensor ◽  
Machine Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Milling Machine ◽  
Clustering Method ◽  
Thermal Error Modeling ◽  
Sensor Clustering

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.

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