scholarly journals Applying Integrated Grey System Theory and Sensor Technology to Study Influence of Cutting Conditions on Thermal Error Modeling of Machine Tools

2021 ◽  
Vol 33 (1) ◽  
pp. 415
Author(s):  
Kun-Chieh Wang ◽  
Chi-Hsin Yang ◽  
Long Wu ◽  
Zijian Ai
Keyword(s):  
System Theory ◽  
Machine Tools ◽  
Thermal Error ◽  
Grey System Theory ◽  
Error Modeling ◽  
Cutting Conditions ◽  
Sensor Technology ◽  
Grey System ◽  
Thermal Error Modeling

Study of the Thermal Error Robust Modeling of 2-Axes Machine Tools Based on the Grey System Theory

2010 ◽  
Vol 426-427 ◽  
pp. 674-677
Author(s):  
Xiu Shan Wang ◽  
Li Yan ◽  
Ling Wang ◽  
De Jun Kong
Keyword(s):  
System Theory ◽  
Machine Tools ◽  
Thermal Error ◽  
Grey System Theory ◽  
Error Modeling ◽  
Field Analysis ◽  
Grey System ◽  
Thermally Induced ◽  
Final Error ◽  
Thermal Error Model

Thermally induced errors can contribute as much as 70% of the final error of the part dimensions produced on machine tools [1-5]. Thermal errors of machine tools can be treated as the superposition of a series of the errors coming from different heat source. 4 key temperature points of a turning lathe were obtained based on the temperature field analysis. A novel thermal error model on the basis of grey system theory was proposed. The basic principle and method of grey system model and its application in thermal error modeling on machine tools had been presented briefly.

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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