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Error modelling and motion reliability analysis of a multi-DOF redundant parallel mechanism with hybrid uncertainties

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  • Zeng, Chen-dong
  • Qiu, Zhi-cheng
  • Zhang, Fen-hua
  • Zhang, Xian-min

Abstract

Accuracy is one of the most important properties of mechanisms. However, the inherent uncertainties will cause an error between the actual motion and the desired motion, leading to a motion reliability problem. The error model and motion reliability of a type of multi-DOF (degrees of freedom) redundant parallel mechanism (MDRPM) with hybrid uncertainties are studied. Firstly, the error model of the mechanism is established and verified. Then, the motion inside the joints is analyzed, and the joint clearance is regarded as an interval variable, while the other variables are treated as random variables. On this basis, a hybrid motion reliability method based on the quasi-Monte Carlo simulation method (QMCSM) is developed for a mechanism with hybrid uncertainties. Compared with the traditional simulation method, the amount of computation required by the QMCSM is significantly reduced, and it is more suitable for solving the large-scale motion reliability problem of the MDRPM. A numerical simulation and experiments are performed. The simulation results show that the feasibility and adaptability of the motion reliability method in this paper are different. The experimental results demonstrate that the error model is effective, so the motion reliability analysis based on the error model is reliable.

Suggested Citation

  • Zeng, Chen-dong & Qiu, Zhi-cheng & Zhang, Fen-hua & Zhang, Xian-min, 2023. "Error modelling and motion reliability analysis of a multi-DOF redundant parallel mechanism with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001746
    DOI: 10.1016/j.ress.2023.109259
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    References listed on IDEAS

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    1. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    2. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    3. Yang, Bin & Yang, Wenyu, 2023. "Modular approach to kinematic reliability analysis of industrial robots," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "Adaptive structural reliability analysis method based on confidence interval squeezing," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Liu, Xiaohang & Zheng, Shansuo & Wu, Xinxia & Chen, Dianxin & He, Jinchuan, 2021. "Research on a seismic connectivity reliability model of power systems based on the quasi-Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Zhang, Qian & Pan, Ning & Meloni, Marco & Lu, Dong & Cai, Jianguo & Feng, Jian, 2021. "Reliability analysis of radially retractable roofs with revolute joint clearances," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    7. Wu, Weidong & Rao, S.S., 2007. "Uncertainty analysis and allocation of joint tolerances in robot manipulators based on interval analysis," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 54-64.
    8. Zhang, Dequan & Shen, Shuoshuo & Wu, Jinhui & Wang, Fang & Han, Xu, 2023. "Kinematic trajectory accuracy reliability analysis for industrial robots considering intercorrelations among multi-point positioning errors," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    9. Chen, Junhua & Chen, Longmiao & Qian, Linfang & Chen, Guangsong & Zhou, Shijie, 2022. "Time-dependent kinematic reliability analysis of gear mechanism based on sequential decoupling strategy and saddle-point approximation," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    10. Zhang, Yu & Dong, You & Xu, Jun, 2023. "An accelerated active learning Kriging model with the distance-based subdomain and a new stopping criterion for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    11. Ökten, Giray & Liu, Yaning, 2021. "Randomized quasi-Monte Carlo methods in global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
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