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An Optimal Tolerance Design Approach of Robot Manipulators for Positioning Accuracy Reliability

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  • Huang, Peng
  • Gu, Yingkui
  • Li, He
  • Yazdi, Mohammad
  • Qiu, Guangqi

Abstract

This paper proposes a novel tolerance design method for robot manipulators to select the optimal tolerances of kinematic parameters. A tolerance optimization model is presented to minimize the failure probability of positioning accuracy with the constraint of manufacturing cost. First, the performance function of positioning accuracy is constructed by performing the differential kinematics and eigen-decomposition concepts, which further combines the chi-square approximation and numerical integration methodologies to calculate the positioning accuracy reliability. An improved genetic algorithm is then presented based on the diversity crossover and differential mutation strategies to optimally and efficiently solve the tolerance design model. The applicability and performance of the proposed method are validated by the tolerance design of a 6-degree-of-freedom robot manipulator. The comparison with the existing techniques illustrates that the proposed method (i) processes better accuracy and efficiency in positioning accuracy reliability analysis; (ii) obtains a tolerance combination of kinematic parameters with a higher positioning accuracy reliability. The proposed method contributes to the optimal tolerance range selection and design of robot manipulators and other moving parts of machinery.

Suggested Citation

  • Huang, Peng & Gu, Yingkui & Li, He & Yazdi, Mohammad & Qiu, Guangqi, 2023. "An Optimal Tolerance Design Approach of Robot Manipulators for Positioning Accuracy Reliability," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002612
    DOI: 10.1016/j.ress.2023.109347
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    1. Yang, Bin & Yang, Wenyu, 2023. "Modular approach to kinematic reliability analysis of industrial robots," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Cui, Da & Wang, Guoqiang & Lu, Yanpeng & Sun, Kangkang, 2020. "Reliability design and optimization of the planetary gear by a GA based on the DEM and Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Kong, Xuefeng & Yang, Jun & Hao, Songhua, 2021. "Reliability modeling-based tolerance design and process parameter analysis considering performance degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    4. 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).
    5. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    6. Meng, Zeng & Zhao, Jingyu & Chen, Guohai & Yang, Dixiong, 2022. "Hybrid uncertainty propagation and reliability analysis using direct probability integral method and exponential convex model," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    7. 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).
    8. Jianjun Wang & Ting Mao & Yiliu Tu, 2021. "Simultaneous multi-response optimisation for parameter and tolerance design using Bayesian modelling method," International Journal of Production Research, Taylor & Francis Journals, vol. 59(8), pages 2269-2293, April.
    9. 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.
    10. Wu, Jinhui & Tao, Yourui & Han, Xu, 2023. "Polynomial chaos expansion approximation for dimension-reduction model-based reliability analysis method and application to industrial robots," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Feng, Liuyang & Zhang, Limao, 2022. "Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    12. 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).
    13. Mi, Jinhua & Beer, Michael & Li, Yan-Feng & Broggi, Matteo & Cheng, Yuhua, 2020. "Reliability and importance analysis of uncertain system with common cause failures based on survival signature," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    14. Jin-Ting Zhang, 2005. "Approximate and Asymptotic Distributions of Chi-Squared-Type Mixtures With Applications," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 273-285, March.
    15. Li, He & Guedes Soares, C, 2022. "Assessment of failure rates and reliability of floating offshore wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    16. Liu, Huan & Tang, Yongqiang & Zhang, Hao Helen, 2009. "A new chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 853-856, February.
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