Study on optimal independent variables for the thermal error model of CNC machine tools
- 71 Downloads
In the technology of thermal error compensation for CNC machine tools, it is particularly important to select modeling variables which can stably reflect the relationship between temperature field and thermal expansion in terms of modeling. This paper analyzes the theories and experiments on the thermal properties of the temperature-sensitive points distributed on one-dimension pole. It is found that the prediction model performs better in prediction accuracy and robustness when established with linear points as independent variables than with nonlinear ones. However, because of the complicated structure of machine tools, it is rather hard to fix the positions of linear points, which consequently lead to the proposal of a comprehensive temperature-feature extraction method that uses feature extraction algorithm and weight optimization to construct linear temperature-sensitive points. Experimental facilities verified the feasibility of its proposal. What’s more, based on the effectiveness of building linear measuring points, it is proposed to arrange the temperature sensors along the deforming direction. With the feeding system of a gantry machine tool as the testing platform, the thermal error model established according to the proposed method is actually tested under different working conditions. The result shows this proposed method has higher prediction precision and robustness.
KeywordsCNC machine tool Temperature-sensitive point Thermal error Model variable Thermal properties Feature extraction
Unable to display preview. Download preview PDF.
This study is supported by the National Natural Science Foundation of China (no. 51375382) and the Science and Technology Support Plan Project of Sichuan Province, China (no. 2016GZ0205).
- 8.Guo Q, Xu R, Yang T, He L, Cheng X, Li Z, Yang JG (2015) Application of GRAM and AFSACA-BPN to thermal error optimization modeling of CNC machine tools. Int J Adv Manuf Technol 83(5–8):995–1002Google Scholar
- 10.Zhang T, Ye W, Shan Y (2015) Application of sliced inverse regression with fuzzy clustering for thermal error modeling of CNC machine tool. Int J Adv Manuf Technol 85(9–12):2761–2771Google Scholar
- 17.Xia JY, Hu YM, Wu B, Shi TL (2008) Analysis of the thermal dynamic characteristic of machine tools based on unidimensional heat transfer. Mechanical Sci Technol Aeros Eng 27(10):1121–1126Google Scholar
- 21.Zhang BL, Gu TX, Mo ZY (1999) Principles and methods of numerical parallel computation. National Defense Industry Press, BeijingGoogle Scholar
- 22.ISO 230-3 (2001) Test code for machine tool—part 3: determination of thermal effects. ISO copyright office, GenevaGoogle Scholar
- 24.Wei X, Gao F, Li Y, Li YH, Ma Z (2016) Optimization of thermal error model critical point for gantry machine tool feeding system. Chin J Sci Instrum 37(6):1340–1346Google Scholar