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Analysis of thermal characteristics of machine tool spindle based on lion swarm optimization algorithm
Heat is very important to the rotation accuracy of the spindle, and the accurate calculation of the convection heat transfer coefficient is the premise of obtaining accurate results from the finite element steady-state thermal analysis. In this paper, the convective heat transfer coefficient of the main shaft was optimized by using the lion swarm optimization (LSO) algorithm. Compared with other optimization algorithms such as genetic algorithm, the LSO algorithm has faster convergence speed, higher accuracy, and better global optimal solution. Firstly, the critical point temperature of the spindle system and the thermal elongation of the spindle were measured by using Lion's thermal analysis module. Secondly, the inaccurate but close to real convective heat transfer coefficient value of the main shaft was calculated by empirical formula. Inaccurate convective heat transfer coefficient was led to inaccurate finite element simulation temperature results, so used the lion optimization algorithm to optimize the convective heat transfer coefficient, the convection heat transfer coefficient was regarded as the target value of interest, and the root mean square error between the experimental temperature value and the simulated temperature value was regarded as the fitness function. After 150 generations, the optimal convective heat transfer coefficient with the smallest error was searched. Finally, the finite element steady-state thermal analysis of the spindle system was carried out on the searched optimal convective heat transfer coefficient, and the comparison with the experimental results proved the effectiveness of the proposed method.
Analysis of thermal characteristics of machine tool spindle based on lion swarm optimization algorithm
Heat is very important to the rotation accuracy of the spindle, and the accurate calculation of the convection heat transfer coefficient is the premise of obtaining accurate results from the finite element steady-state thermal analysis. In this paper, the convective heat transfer coefficient of the main shaft was optimized by using the lion swarm optimization (LSO) algorithm. Compared with other optimization algorithms such as genetic algorithm, the LSO algorithm has faster convergence speed, higher accuracy, and better global optimal solution. Firstly, the critical point temperature of the spindle system and the thermal elongation of the spindle were measured by using Lion's thermal analysis module. Secondly, the inaccurate but close to real convective heat transfer coefficient value of the main shaft was calculated by empirical formula. Inaccurate convective heat transfer coefficient was led to inaccurate finite element simulation temperature results, so used the lion optimization algorithm to optimize the convective heat transfer coefficient, the convection heat transfer coefficient was regarded as the target value of interest, and the root mean square error between the experimental temperature value and the simulated temperature value was regarded as the fitness function. After 150 generations, the optimal convective heat transfer coefficient with the smallest error was searched. Finally, the finite element steady-state thermal analysis of the spindle system was carried out on the searched optimal convective heat transfer coefficient, and the comparison with the experimental results proved the effectiveness of the proposed method.
Analysis of thermal characteristics of machine tool spindle based on lion swarm optimization algorithm
ZHANG ZhuangZhuang (author) / WANG HongJun (author) / WANG ZengXin (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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