Issue |
Mechanics & Industry
Volume 21, Number 4, 2020
|
|
---|---|---|
Article Number | 402 | |
Number of page(s) | 14 | |
DOI | https://doi.org/10.1051/meca/2019086 | |
Published online | 06 May 2020 |
Regular Article
Study on optimization of thermal spinning process of accumulator shell
1
Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, PR China
2
National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin, PR China
* e-mail: zhupeihao_gp@163.com
Received:
18
May
2019
Accepted:
15
November
2019
In view of the shortcomings of the existing hot spinning process technology of the accumulator shell, a method for optimizing the multi-spinning process parameters is proposed. The Johnson-Cook constitutive model of the accumulator shell material – 34CrMo4 alloy steel − was established with its parameters obtained experimentally. The finite element simulation was carried out for the hot spinning and closing process. Based on which, three parameters with the greatest influence on the spinning formation were studied: spinning temperature, spindle speed and friction coefficient. Combined with the central composite test, the response surface model and the mapping relationship between the three parameters and the maximum mises stress as well as the maximum wall thickness increment of the shell were established. The Pareto optimized solution set was obtained through multi-objective optimization. Under the condition of not affecting product quality, the optimized solution with low spinning temperature and high spindle speed is selected to reduce energy loss and improve work efficiency. The results indicate that the optimized process is experimentally verified to reduce the process temperature by nearly 30 °C, and the efficiency is increased by 25%.
Key words: Accumulator / hot spinning technology / finite element / response surface / multi-objective optimization
© AFM, EDP Sciences 2020
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