Title: Optimal design of cross shaft based on multi-objective genetic algorithm

Authors: Yanfeng Mao; Gongfa Li; Du Jiang; Bo Tao; Yongcheng Cao; Shidong Li; Nannan Sun; Zeshen Li

Addresses: Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China; Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China ' Hubei Jingmen Wusan Machinery Equipment Manufacturing Co., Ltd, Jingshan 431800, China ' Hubei Jingmen Wusan Machinery Equipment Manufacturing Co., Ltd, Jingshan 431800, China ' Huaxia Xingguang Industrial Design Jiangsu Co., Ltd., Suqian 223800, China ' Guangdong Xinhui Cimc Special Transportation Equipment Co., Ltd., Jiangmen 529144, China

Abstract: The cross shaft is the crucial component for torque transmission in the coupling. In general, the cross shaft is prone to fatigue and deformation due to the ample torque and the unreasonable structure of the bearing. In this paper, the key dimensional parameters of the cross shaft are screened based on the sensitivity analysis. Next, these dimensions were taken as design variables, and the optimisation objectives were to reduce the maximum equivalent stress and the maximum total deformation. Finally, the multi-objective genetic algorithm is used to complete the optimisation design of the cross shaft. The maximum total deformation of the cross shaft is reduced by 0.4717 mm, and the maximum equivalent stress is reduced by 130.35 MPa compared with the initial structure.

Keywords: cross shaft; sensitivity analysis; structure optimisation; response surface optimisation; multi-objective genetic algorithm.

DOI: 10.1504/IJWMC.2021.120903

International Journal of Wireless and Mobile Computing, 2021 Vol.21 No.3, pp.243 - 254

Received: 10 Oct 2021
Accepted: 21 Nov 2021

Published online: 16 Feb 2022 *

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