Multi-objective optimisation design of cross-shaft based on Kriging response surface optimisation model

被引:0
|
作者
Xie Y. [1 ,2 ]
Xiong S. [1 ,3 ]
Yun J. [1 ,3 ]
Mao Y. [1 ,3 ]
Li B. [1 ,3 ]
Tang X. [1 ,3 ]
Cao Y. [4 ]
机构
[1] Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Hubei, Wuhan
[2] Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Hubei, Wuhan
[3] Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Hubei, Wuhan
[4] Technology Research and Development, Hubei Jingmen Wusan Machinery Equipment Manufacturing Co., Ltd., Hubei, Jingmen
基金
中国国家自然科学基金;
关键词
ANSYS workbench; cross shaft; multi-objective optimisation; sensitivity analysis;
D O I
10.1504/IJWMC.2024.137166
中图分类号
学科分类号
摘要
Cross-universal coupling is a key component of the mechanical transmission system and the cross-shaft is the core component of the coupling for torque transmission. Under normal circumstances, cross-shafts are most susceptible to fatigue and deformation, mainly due to the large torques they carry and the irrationality of their structure. Traditional design methods rely on practical experience to determine the key dimensions of the cross-shaft, resulting in long design cycles and low reliability. To address this problem, parametric modelling of the cross-shaft is carried out in this paper and imported into ANSYS Workbench. In addition, static and finite element analyses are carried out to find the weak parts of the cross shaft as the objective function. Finally, sensitivity analysis is carried out using the main structural parameters of the cross-shaft as design variables. Based on the linear correlation matrix and sensitivity graph, the three design variables that have the greatest impact on the objective function, journal height, thickness and body length, are selected. © 2024 Inderscience Enterprises Ltd.
引用
收藏
页码:207 / 214
页数:7
相关论文
共 50 条
  • [21] Multi-objective optimisation of traffic signal control based on particle swarm optimisation
    Jian L.
    Jian, Li (litaann@163.com), 1600, Inderscience Publishers (11): : 547 - 553
  • [22] Biogeography-based optimisation with migration velocity for multi-objective optimisation problems
    Li, Wuzhao
    Mao, Yanfen
    Guo, Weian
    Wang, Lei
    Wu, Qidi
    INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2017, 10 (01) : 43 - 50
  • [23] Population-based ant colony optimisation for multi-objective function optimisation
    Angus, Daniel
    PROGRESS IN ARTIFICIAL LIFE, PROCEEDINGS, 2007, 4828 : 232 - 244
  • [24] Automatic calibration of SWMM parameters based on multi-objective optimisation model
    Wang, Tao
    Zhang, Longlong
    Zhai, Jiaqi
    Wang, Lizhen
    Zhao, Yifei
    Liu, Kuan
    JOURNAL OF HYDROINFORMATICS, 2024, 26 (03) : 683 - 706
  • [25] Multi-objective aircraft vector manoeuvre optimisation model
    Cecen, Ramazan Kursat
    INTERNATIONAL JOURNAL OF SUSTAINABLE AVIATION, 2021, 7 (02) : 113 - 122
  • [26] A multi-objective emergency vehicle scheduling optimisation model
    Yao, Jiao
    Shao, Chuwei
    Xia, Xiaomei
    Wang, Pincheng
    Wei, Yu
    Wang, Jin
    INTERNATIONAL JOURNAL OF SENSOR NETWORKS, 2020, 34 (04) : 236 - 243
  • [27] Population extremal optimisation for discrete multi-objective optimisation problems
    Randall, M.
    Lewis, A.
    INFORMATION SCIENCES, 2016, 367 : 390 - 402
  • [28] The cross-entropy method in multi-objective optimisation: An assessment
    Bekker, James
    Aldrich, Chris
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2011, 211 (01) : 112 - 121
  • [29] MULTI-OBJECTIVE DESIGN OPTIMISATION OF A 3D-RAIL STAMPING PROCESS USING A ROBUST MULTI-OBJECTIVE OPTIMISATION PLATFORM (RMOP)
    Lee, Dongseop
    Coma, Marti
    Espinoza, Hector
    Fruitos, Oscar
    Pons-Prats, Jordi
    COMPUTATIONAL PLASTICITY XII: FUNDAMENTALS AND APPLICATIONS, 2013, : 1246 - 1257
  • [30] Hypervolume-Based DIRECT for Multi-Objective Optimisation
    Yin, Cheryl Wong Sze
    Al-Dujaili, Abdullah
    Suresh, S.
    PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION), 2016, : 1201 - 1208