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Research on Transient Thermal-Structural Coupling Characteristics and Thermal Error Prediction of Ball Screw Feed System
- Bing Man - Jiangsu Normal University, School of Mechanical and Electrical Engineering, China ,
- YongHuan Guo - Jiangsu Normal University, School of Mechanical and Electrical Engineering, China ,
- Guangzhong Ji - Jiangsu Normal University, School of Mechanical and Electrical Engineering, China ,
- Xiying Fan - Jiangsu Normal University, School of Mechanical and Electrical Engineering, China
ISSN: 1946-3979, e-ISSN: 1946-3987
Published April 21, 2022 by SAE International in United States
Citation: Man, B., Guo, Y., Ji, G., and Fan, X., "Research on Transient Thermal-Structural Coupling Characteristics and Thermal Error Prediction of Ball Screw Feed System," SAE Int. J. Mater. Manf. 15(4):2022, https://doi.org/10.4271/05-15-04-0020.
The thermal error of ball screw is the main factor affecting the accuracy of machine tool. Establishing an accurate thermal error model of ball screw is the key to compensate the error of machine tool. The ultimate goal of the research work in this article is to develop a comprehensive modeling method that can predict the temperature rise and thermal error of ball screw. In view of the problem that the reciprocating motion of ball screw nut was ignored in the traditional thermal error model, a transient thermal-structural coupling model considering the actual working conditions was proposed. ANSYS parametric design language (APDL) was used to set the ball screw nut as the moving heat source load, and the displacement-time relationship between the ball screw nut and the ball screw was defined. The temperature and thermal deformation distribution of the ball screw under the action of the bearing and the heat source of the ball screw nut were simulated. Then, the accuracy of the finite element model was verified by experiments. On this basis, the influence of different working conditions (feed speed, cutting load, and ball screw preload) on the temperature rise of ball screw center position was analyzed. In addition, a reliable model was proposed. Particle swarm optimization (PSO) algorithm was employed to optimize the gray neural network (GNN). The prediction was performed with its temperature rise data as input and thermal error data as output. The results show that the modeling method used in this article can well predict the thermal positioning error of the feed system. This work lays a foundation for thermal error compensation of ball screw.