Modelling and interaction analysis of the self-pierce riveting process using regression analysis and FEA

Abstract

Self-pierce riveting (SPR) is a major joining method used in the automotive industry. However, there still lacks a fast and easy-to-use joint quality prediction tool available for the automotive engineers. In this study, the simple but effective regression analysis method was applied to quickly predict the SPR joint quality. Two regression models were developed for the prediction of the interlock and the minimum remaining bottom sheet thickness (Tmin). The prediction accuracy of the developed regression models was validated by comparing with the experimental results. Under the studied joint configurations, the mean absolute errors (MAE) of the interlock and Tmin were 0.047 mm and 0.053 mm, respectively, and the corresponding mean absolute percentage errors (MAPE) were 10.4% and 12.3%. With the developed models, the interaction effects between rivet and die parameters on the joint interlock and Tmin were also systematically analysed. The results revealed that the rivet and die parameters demonstrated significant influences on the interlock but not on the Tmin. These interaction effects were further examined by analysing the deformations of the rivet and substrate materials. Moreover, the die-to-rivet volume ratio (R) was found to be critical for the formation of interlock, and a larger interlock is more likely achieved when the R is close to 1.0.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Data availability

Not applicable

References

  1. 1.

    Reinhert P (2004) The new Jaguar XJ - The first all aluminium car in monocoque design. Alum Int Today 16:21–24

    Google Scholar 

  2. 2.

    Abe Y, Kato T, Mori K (2006) Joinability of aluminium alloy and mild steel sheets by self piercing rivet. J Mater Process Technol 177:417–421. https://doi.org/10.1016/j.jmatprotec.2006.04.029

    Article  Google Scholar 

  3. 3.

    He X, Zhao L, Deng C, Xing B, Gu F, Ball A (2015) Self-piercing riveting of similar and dissimilar metal sheets of aluminum alloy and copper alloy. Mater Des 65:923–933. https://doi.org/10.1016/J.MATDES.2014.10.002

    Article  Google Scholar 

  4. 4.

    Kotadia HR, Rahnama A, Sohn IR, Kim J, Sridhar S (2019) Performance of dissimilar metal self-piercing riveting (SPR) joint and coating behaviour under corrosive environment. J Manuf Process 39:259–270. https://doi.org/10.1016/J.JMAPRO.2019.02.024

    Article  Google Scholar 

  5. 5.

    Han L, Thornton M, Shergold M (2010) A comparison of the mechanical behaviour of self-piercing riveted and resistance spot welded aluminium sheets for the automotive industry. Mater Des 31:1457–1467. https://doi.org/10.1016/J.MATDES.2009.08.031

    Article  Google Scholar 

  6. 6.

    Li D, Chrysanthou A, Patel I, Williams G (2017) Self-piercing riveting-a review. Int J Adv Manuf Technol 92:1777–1824. https://doi.org/10.1007/s00170-017-0156-x

    Article  Google Scholar 

  7. 7.

    He X, Gu F, Ball A (2012) Recent development in finite element analysis of self-piercing riveted joints. Int J Adv Manuf Technol 58:643–649. https://doi.org/10.1007/s00170-011-3414-3

    Article  Google Scholar 

  8. 8.

    Haque R (2018) Quality of self-piercing riveting (SPR) joints from cross-sectional perspective: a review. Arch Civ Mech Eng 18:83–93. https://doi.org/10.1016/j.acme.2017.06.003

    Article  Google Scholar 

  9. 9.

    Kam DH, Jeong TE, Kim MG, Shin J (2019) Self-piercing riveted joint of vibration-damping steel and aluminum alloy. Appl Sci 9. https://doi.org/10.3390/app9214575

  10. 10.

    Zhang X, He X, Xing B, Wei W, Lu J (2020) Quasi-static and fatigue characteristics of self-piercing riveted joints in dissimilar aluminium-lithium alloy and titanium sheets. J Mater Res Technol. 9:5699–5711. https://doi.org/10.1016/j.jmrt.2020.03.095

    Article  Google Scholar 

  11. 11.

    Han L, Thornton M, Li D, Shergold M (2010) Effect of setting velocity on self-piercing riveting process and joint behaviour for automotive applications. SAE Tech Pap. https://doi.org/10.4271/2010-01-0966

  12. 12.

    Sun X, Khaleel MA (2005) Performance optimization of self-piercing rivets through analytical rivet strength estimation. J Manuf Process 7:83–93. https://doi.org/10.1016/S1526-6125(05)70085-2

    Article  Google Scholar 

  13. 13.

    Xu Y (2006) Effects of factors on physical attributes of self-piercing riveted joints. Sci Technol Weld Join 11:666–671. https://doi.org/10.1179/174329306X131866

    Article  Google Scholar 

  14. 14.

    Ma Y, Lou M, Li Y, Lin Z (2018) Effect of rivet and die on self-piercing rivetability of AA6061-T6 and mild steel CR4 of different gauges. J Mater Process Technol 251:282–294. https://doi.org/10.1016/J.JMATPROTEC.2017.08.020

    Article  Google Scholar 

  15. 15.

    Li DZ, Han L, Shergold M, Thornton M, Williams G (2013) Influence of rivet tip geometry on the joint quality and mechanical strengths of self-piercing riveted aluminium joints. Mater Sci Forum 765:746–750. https://doi.org/10.4028/www.scientific.net/MSF.765.746

    Article  Google Scholar 

  16. 16.

    Mucha J (2011) A study of quality parameters and behaviour of self-piercing riveted aluminium sheets with different joining conditions. Stroj Vestnik/Journal Mech Eng 57:323–333. https://doi.org/10.5545/sv-jme.2009.043

    Article  Google Scholar 

  17. 17.

    Han SL, Li ZY, Gao Y, Zeng QL (2014) Numerical study on die design parameters of self-pierce riveting process based on orthogonal test. J Shanghai Jiaotong Univ 19:308–312. https://doi.org/10.1007/s12204-014-1504-8

    Article  Google Scholar 

  18. 18.

    Jäckel M, Falk T, Landgrebe D (2016) Concept for further development of self-pierce riveting by using cyber physical systems. Procedia CIRP 44:293–297. https://doi.org/10.1016/j.procir.2016.02.073

    Article  Google Scholar 

  19. 19.

    Bhushan RK (2013) Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod 39:242–254. https://doi.org/10.1016/j.jclepro.2012.08.008

    Article  Google Scholar 

  20. 20.

    Singh B, Ahuja N (2002) Development of controlled-release buccoadhesive hydrophilic matrices of diltiazem hydrochloride: Optimization of bioadhesion, dissolution, and diffusion parameters. Drug Dev Ind Pharm 28:431–442. https://doi.org/10.1081/DDC-120003004

    Article  Google Scholar 

  21. 21.

    Anawa EM, Olabi AG (2008) Using Taguchi method to optimize welding pool of dissimilar laser-welded components. Opt Laser Technol 40:379–388. https://doi.org/10.1016/j.optlastec.2007.07.001

    Article  Google Scholar 

  22. 22.

    Bitondo C, Prisco U, Squilace A, Buonadonna P, Dionoro G (2011) Friction-stir welding of AA 2198 butt joints: mechanical characterization of the process and of the welds through DOE analysis. Int J Adv Manuf Technol 53:505–516. https://doi.org/10.1007/s00170-010-2879-9

    Article  Google Scholar 

  23. 23.

    Zhao D, Wang Y, Liang D, Zhang P (2016) Modeling and process analysis of resistance spot welded DP600 joints based on regression analysis. Mater Des. 110:676–684. https://doi.org/10.1016/j.matdes.2016.08.038

    Article  Google Scholar 

  24. 24.

    He X, Xing B, Zeng K, Gu F, Ball A (2013) Numerical and experimental investigations of self-piercing riveting. Int J Adv Manuf Technol 69:715–721. https://doi.org/10.1007/s00170-013-5072-0

    Article  Google Scholar 

  25. 25.

    Carandente M, Dashwood RJ, Masters IG, Han L (2016) Improvements in numerical simulation of the SPR process using a thermo-mechanical finite element analysis. J Mater Process Technol 236:148–161. https://doi.org/10.1016/J.JMATPROTEC.2016.05.001

    Article  Google Scholar 

  26. 26.

    Liu Y, Li H, Zhao H, Liu X (2019) Effects of the die parameters on the self-piercing riveting process. Int J Adv Manuf Technol 105:1–16. https://doi.org/10.1007/s00170-019-04567-4

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Matthias Wissling, Paul Bartig and their team members from Tucker GmbH for their supports during the laboratory tests.

Funding

This research is funded by Jaguar Land Rover Limited.

Author information

Affiliations

Authors

Contributions

Huan Zhao, Li Han, Yunpeng Liu and Xianping Liu worked together to conceive this research. Huan Zhao designed the experiments, analysed the data and completed the original draft. Li Han supervised the experiments and provided critical paper revisions. Yunpeng Liu supported with the FEA simulation model and manuscript revision. Xianping Liu is the project leader and participated in the paper revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xianping Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Not applicable

Consent to participate

Not applicable

Consent to publish

Not applicable

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhao, H., Han, L., Liu, Y. et al. Modelling and interaction analysis of the self-pierce riveting process using regression analysis and FEA. Int J Adv Manuf Technol 113, 159–176 (2021). https://doi.org/10.1007/s00170-020-06519-9

Download citation

Keywords

  • SPR
  • Multiple regression model
  • Interaction effect
  • Rivet length
  • Die geometry
  • Die-to-rivet volume ratio