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Global sensitivity analysis of riveting parameters based on a random sampling-high dimensional model representation

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Abstract

The riveting process involves numerous parameters and complex problems, such as contact phenomena and material nonlinearity; therefore, it is challenging to accurately control the deformation of riveted parts by adjusting the riveting parameters. Therefore, this paper proposes a global sensitivity analysis method to determine the effects of riveting parameters on the maximum deformation of aeronautical thin-wall structures (ATWS). Considering the correlation among variables, the riveting parameters are used as input variables and the maximum deformations of ATWS are used as the output response to establish a high-precision second-order random sampling-high dimensional model representation response function. The structure and correlative sensitivity analysis method is then used to analyze the response function, and an importance ranking of the input variables is obtained to provide guidance for designs that reduce the riveting deformation of thin-walled plates.

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References

  1. Lei C, Chen Q, Bi Y, Li J, Ke Y (2019) An effective theoretical model for slug rivet assembly based on countersunk hole structure. Int J Adv Manuf Technol 101:1065–1074

    Article  Google Scholar 

  2. Yang D, Qu W, Ke Y (2019) Local-global method to predict distortion of aircraft panel caused in automated riveting process. Assem Autom 39(5):973–985

    Article  Google Scholar 

  3. Hossein CS (2008) Effect of variations in the riveting process on the quality of riveted joints. Int J Adv Manuf Technol 39(11–12):1144–1155

    Google Scholar 

  4. Liu J, Li H, Bi Y, Dong H, Ke Y (2019) Influence of the deformation of riveting-side working head on riveting quality. Int J Adv Manuf Technol 102(9–12):4137–4151

    Article  Google Scholar 

  5. Zhang Y, Bi Q, Yu L, Wang Y (2018) Online compensation of force-induced deformation for high-precision riveting machine based on force–displacement data analysis. Int J Adv Manuf Technol 94(1–4):941–956

    Article  Google Scholar 

  6. Abdelal G, Georgiou G, Cooper J, Robotham A, Levers A, Lunt P (2014) Numerical and experimental investigation of aircraft panel deformations during riveting process. J Manuf Sci Eng 137(1):011009

    Article  Google Scholar 

  7. Chang Z, Wang Z, Jiang B, Zhang J, Guo F, Kang Y (2016) Modeling and predicting of aeronautical thin-walled sheet metal parts riveting deformation. Assem Autom 36(3):295–307

    Article  Google Scholar 

  8. Zheng B, Yu H, Lai X (2017) Assembly deformation prediction of riveted panels by using equivalent mechanical model of riveting process. Int J Adv Manuf Technol 92(5–8):1955–1966

    Article  Google Scholar 

  9. Lin J, Jin S, Zheng C, Li Z, Liu Y (2014) Compliant assembly variation analysis of aeronautical panels using unified substructures with consideration of identical parts. Comput Aided Des 57:29–40

    Article  Google Scholar 

  10. Figueira J, Trabasso L (2015) Riveting-induced deformations on aircraft structures. J Aircr 52(6):2032–2050

    Article  Google Scholar 

  11. Li G, Jiang H, Zhang X, Cui J (2017) Mechanical properties and fatigue behavior of electromagnetic riveted lap joints influenced by shear loading. J Manuf Process 26:226–239

    Article  Google Scholar 

  12. Cheraghi SH (2007) Effect of variations in the riveting process on the quality of riveted joints. Int J Adv Manuf Technol 39:1144–1156

    Article  Google Scholar 

  13. Aman F, Cheraghi SH, Krishnan KK, Lankarani H (2012) Study of the impact of riveting sequence, rivet pitch, and gap between sheets on the quality of riveted lap joints using finite element method. Int J Adv Manuf Technol 67(1–4):545–562

    Google Scholar 

  14. Liu X, Lim Y, Li Y, Tang W, Ma Y, Feng Z, Ni J (2016) Effects of process parameters on friction self-piercing riveting of dissimilar materials. J Mater Process Technol 237:19–30

    Article  Google Scholar 

  15. Lei C, Bi Y, Li J, Ke Y (2017) Effect of riveting parameters on the quality of riveted aircraft structures with slug rivet. Adv Mech Eng 9(11):1–12

    Article  Google Scholar 

  16. Eckert A, Israel M, Neugebauer R, Rössinger M, Wahl M, Schulz F (2012) Local–global approach using experimental and/or simulated data to predict distortion caused by mechanical joining technologies. Prod Eng 7(2–3):339–349

    Google Scholar 

  17. Cui L, Zhang J, Ren B, Chen H (2018) Research on a new aviation safety index and its solution under uncertainty conditions. Saf Sci 107:55–61

    Article  Google Scholar 

  18. Zhang F, Xu X, Cheng L, Wang L, Liu Z, Zhang L (2019) Global moment- independent sensitivity analysis of single-stage thermoelectric refrigeration system. Int J Energy Res 43:9055–9064

    Article  Google Scholar 

  19. Wu H, Zheng H, Wang W, Xiang X, Rong M (2020) A method for tracing key geometric errors of vertical machining center based on global sensitivity analysis. Int J Adv Manuf Technol 106(9–10):3943–3956

    Article  Google Scholar 

  20. Zhang F, Xu X, Wang L, Liu Z, Zhang L (2020) Global sensitivity analysis of two-stage thermoelectric refrigeration system based on response variance. Int J Energy Res 44(8):6623–6630

    Article  Google Scholar 

  21. Kala Z, Valeš J (2017) Global sensitivity analysis of lateral-torsional buckling resistance based on finite element simulations. Eng Struct 134:37–47

    Article  Google Scholar 

  22. Zhu X, Huang J, Quan L, Xiang Z, Shi B (2019) Comprehensive sensitivity analysis and multi-objective optimization research of permanent magnet flux-intensifying motors. IEEE Trans Ind Electron 66(4):2613–2627

    Article  Google Scholar 

  23. Zhang F, Xu X, Cheng L, Tan S, Wang W, Wu M (2020) Mechanism reliability and sensitivity analysis method using truncated and correlated normal variables. Saf Sci 125:104615

    Article  Google Scholar 

  24. Sobol IM (1993) Sensitivity estimates for nonlinear mathematical models. Math Modell Comput Exp 1(4):407–414

    MathSciNet  MATH  Google Scholar 

  25. Helton JC, Davis FJ, Johnson JD (2005) A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling. Reliab Eng Syst Saf 89(3):305–330

    Article  Google Scholar 

  26. Li G, Rabitz H, Yelvington P, Oluwole O, Bacon F, Kolb C, Schoendorf J (2010) Global sensitivity analysis for systems with independent and/or correlated inputs. J Phys Chem A 114(19):6022–6032

    Article  Google Scholar 

  27. Xiao H, Duan Y (2016) Sensitivity analysis of correlated inputs: application to a riveting process model. Appl Math Model 40(13–14):6622–6638

    Article  MathSciNet  MATH  Google Scholar 

  28. Li G, Hu J, Wang S, Georgopoulos P, Schoendorf J, Rabitz H (2006) Random sampling-high dimensional model representation (RS-HDMR) and orthogonality of its different order component functions. J Phys Chem A 110(7):2474–2485

    Article  Google Scholar 

  29. Spiessl SM, Kucherenko S, Becker DA, Zaccheus O (2019) Higher-order sensitivity analysis of a final repository model with discontinuous behaviour using the RS-HDMR meta-modeling approach. Reliab Eng Syst Saf 187:149–158

    Article  Google Scholar 

  30. Chen N, Thonnerieux M, Ducloux R, Wan M, Chenot J (2012) Parametric study of riveted joints. Int J Mater Form 7(1):65–79

    Article  Google Scholar 

  31. Cui J, Qi L, Jiang H, Li G, Zhang X (2017) Numerical and experimental investigations in electromagnetic riveting with different rivet dies. Int J Mater Form 11(6):839–853

    Article  Google Scholar 

  32. Chang Z, Wang Z, Xie L, Kang Y, Xu M, Wang Z (2018) Prediction of riveting deformation for thin-walled structures using local-global finite element approach. Int J Adv Manuf Technol 97(5–8):2529–2544

    Article  Google Scholar 

  33. Li G, Wang S, Rabitz H (2002) Practical approaches to construct RS-HDMR component functions. J Phys Chem A 106(37):8721–8733

    Article  Google Scholar 

  34. Shields M, Zhang J (2016) The generalization of Latin hypercube sampling. Reliab Eng Syst Saf 148:96–108

    Article  Google Scholar 

  35. Iooss B, Lemaître P (2016) A review on global sensitivity analysis methods. Oper Res Comput Sci Interfaces Ser 59:101–122

    Google Scholar 

Download references

Funding

The authors gratefully appreciate the support of the Natural Science Foundation of Shaanxi Province (2019JM-377), Postgraduate Tutor Guidance Ability Improvement Plan in 2019 at Northwestern Polytechnical University (2019), and Xi’an Science and Technology Innovation Platform Construction Project/Key Laboratory Construction Project (2019220614SYS021CG043).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an, 710600, China

    Junqing Yin, Jinyu Gu & Yongdang Chen

  2. Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an, 710600, China

    Junqing Yin, Jinyu Gu & Yongdang Chen

  3. School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an, 710129, China

    Feng Zhang

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  1. Junqing Yin
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  2. Jinyu Gu
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  4. Feng Zhang
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Contributions

Junqing Yin and Feng Zhang were responsible for the design and implementation of the experimental scheme of the article. Jinyu Gu was responsible for completing the article. Junqing Yin and Feng Zhang were responsible for the revision of the article. Yongdang Chen and Feng Zhang were involved in the discussion and significantly contributed to making the final draft of the article. All the authors read and approved the final manuscript.

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Correspondence to Feng Zhang.

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Yin, J., Gu, J., Chen, Y. et al. Global sensitivity analysis of riveting parameters based on a random sampling-high dimensional model representation. Int J Adv Manuf Technol 113, 465–472 (2021). https://doi.org/10.1007/s00170-021-06593-7

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  • DOI: https://doi.org/10.1007/s00170-021-06593-7

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