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.
This is a preview of subscription content, access via your institution.
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
The authors confirm that the data supporting the findings of this study are available within the article.
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
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
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
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
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
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
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
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
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
Figueira J, Trabasso L (2015) Riveting-induced deformations on aircraft structures. J Aircr 52(6):2032–2050
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
Cheraghi SH (2007) Effect of variations in the riveting process on the quality of riveted joints. Int J Adv Manuf Technol 39:1144–1156
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
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
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
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
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
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
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
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
Kala Z, Valeš J (2017) Global sensitivity analysis of lateral-torsional buckling resistance based on finite element simulations. Eng Struct 134:37–47
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
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
Sobol IM (1993) Sensitivity estimates for nonlinear mathematical models. Math Modell Comput Exp 1(4):407–414
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
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
Xiao H, Duan Y (2016) Sensitivity analysis of correlated inputs: application to a riveting process model. Appl Math Model 40(13–14):6622–6638
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
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
Chen N, Thonnerieux M, Ducloux R, Wan M, Chenot J (2012) Parametric study of riveted joints. Int J Mater Form 7(1):65–79
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
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
Li G, Wang S, Rabitz H (2002) Practical approaches to construct RS-HDMR component functions. J Phys Chem A 106(37):8721–8733
Shields M, Zhang J (2016) The generalization of Latin hypercube sampling. Reliab Eng Syst Saf 148:96–108
Iooss B, Lemaître P (2016) A review on global sensitivity analysis methods. Oper Res Comput Sci Interfaces Ser 59:101–122
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).
The authors declare that there is no conflict of interest.
Consent to participate
Consent to publish
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
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
- Global sensitivity analysis
- Aeronautical thin-wall structures
- Random sampling-high dimensional model