This paper presents a new machine learning-based calibration framework for strength simulation models of self-piercing riveted (SPR) joints. Strength simulations were conducted through the integrated modeling of SPR joints from process to performance, while physical quasi-static tensile tests were performed on combinations of DP600 high-strength steel and 5754 aluminum alloy sheets under lap-shear loading conditions. A sensitivity study of the critical simulation parameters (e.g., friction coefficient and scaling factor) was conducted using the controlled variables method and Sobol sensitivity analysis for feature selection. Subsequently, machine-learning-based surrogate models were used to train and accurately represent the mapping between the detailed joint profile and its load-displacement curve. Calibration of the simulation model is defined as a dual-objective optimization task to minimize errors in key load displacement features between simulations and experiments. A multi-objective genetic algorithm (MOGA) was chosen for optimization. The three combinations of SPR joints illustrated the effectiveness of the proposed framework, and good agreement was achieved between the calibrated models and experiments.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00502-3
Yu-Xiang Ji
,
Li Huang
,
Qiu-Ren Chen
,
Charles K. S. Moy
,
Jing-Yi Zhang
,
Xiao-Ya Hu
,
Jian Wang
,
Guo-Bi Tan
,
Qing Liu
. A machine learning-based calibration method for strength simulation of self-piercing riveted joints[J]. Advances in Manufacturing, 2024
, 12(3)
: 465
-483
.
DOI: 10.1007/s40436-024-00502-3
1. Zhang W, Xu J (2022) Advanced lightweight materials for automobiles:a review. Mater Design 221:110994. https://doi.org/10.1016/j.matdes.2022.110994
2. Li D, Chrysanthou A, Patel I et al (2017) Self-piercing riveting-a review. Int J Adv Manuf Tech 92:1777-1824
3. Li M, Liu Z, Huang L et al (2022) Automatic identification framework of the geometric parameters on self-piercing riveting cross-section using deep learning. J Manuf Process 83:427-437
4. Li M, Liu Z, Huang L et al (2023) Multi-fidelity data-driven optimization design framework for self-piercing riveting process parameters. J Manuf Process 99:812-824
5. Sui B, Du D, Chang B et al (2007) Simulation and analysis of self-piercing riveting process in aluminum sheets. Mater Sci Tech-Lond 5:713-717
6. Wan S, Hu S, Li SY et al (2007) Process parameters and joint evaluation of self-piercing riveting with half-hollow rivets. J Tianjin Univ 4:494-498
7. Liu XQ (2007) Experimental investigation and finite element numerical simulation of self-piercing riveted process. Dissertation, Tianjin University
8. Bouchard PO, Laurent T, Tollier L (2008) Numerical modeling of self-pierce riveting-from riveting process modeling down to structural analysis. J Mater Process Tech 202(1/3):290-300
9. Casalino G, Rotondo A, Ludovico A (2008) On the numerical modelling of the multiphysics self piercing riveting process based on the finite element technique. Adv Eng Softw 39(9):787-795
10. Kang SW, Huang ZC, Chen XM (2008) Introduction of fretting in self-piercing riveting. Coal Mine Machinery 6:7-9
11. Grujicic M, Snipes J, Ramaswami S et al (2014) Process modeling, joint-property characterization and construction of joint connectors for mechanical fastening by self-piercing riveting. Multidiscip Model Materials and Structures 10(4):631-658
12. Hanssen A, Olovsson L, Porcaro R et al (2010) A large-scale finite element point-connector model for self-piercing rivet connections. Eur J Mech A-Solid 29(4):484-495
13. Chung CS, Kim HK (2016) Fatigue strength of self-piercing riveted joints in lap-shear specimens of aluminium and steel sheets. Fatigue Fract Eng M 39(9):1105-1114
14. Yan W, Xie Z, Yu C et al (2017) Experimental investigation and design method for the shear strength of self-piercing rivet connections in thin-walled steel structures. J Constr Steel Res 133:231-240
15. Bock FE, Blaga LA, Klusemann B (2020) Mechanical performance prediction for friction riveting joints of dissimilar materials via machine learning. Proc Manuf 47:615-622
16. Zhao H, Han L, Liu Y et al (2021) Quality prediction and rivet/die selection for SPR joints with artificial neural network and genetic algorithm. J Manuf Process 66:574-594
17. Jäckel M, Coppieters S, Hofmann M et al (2017) Mechanical joining of materials with limited ductility:analysis of process-induced defects. AIP Conf Proc 1896:110009. https://doi.org/10.1063/1.5008136
18. Kang H, Lee JH, Choe Y et al (2021) Prediction of lap shear strength and impact peel strength of epoxy adhesive by machine learning approach. Nanomaterials 11(4):872. https://doi.org/10.3390/nano11040872
19. Qi BF (2020) Crashworthiness analysis of heterogeneous metal automobile front rail. Dissertation, Dalian University of Technology.
20. Xie Y (2019) Research on the performance of self-piercing riveting and its application on front longero simulation. Dissertation, Hefei University of Technology
21. Xu JS (2018) Simulation study on failure of self-piercing riveted joints between steel and aluminum. Dissertation, Jilin University
22. Zhang ZY, Shi BJ, Zhong JB (2019) Optimisation of parameter matching of semi-hollow self-pierce riveted joints in aluminium alloy car bodies. Mach Design Manuf Eng 48(3):64-68
23. Fang Y, Huang L, Zhan Z et al (2022) A framework for calibration of self-piercing riveting process simulation model. J Manuf Process 76:223-235
24. Haque R (2018) Quality of self-piercing riveting (SPR) joints from cross-sectional perspective:a review. Arch Civ Mech Eng 18(1):83-93
25. Ma Y, Lou M, Li Y et al (2018) Effect of rivet and die on self-piercing rivetability of AA6061-T6 and mild steel CR4 of different gauges. J Mater Process Tech 251:282-294
26. Huang L, Lasecki JV, Guo H et al (2014) Finite element modeling of dissimilar metal self-piercing riveting process. SAE Int J Mater Manu 7(3):698-705
27. Huang L, Wu Y, Huff G et al (2020) Sensitivity study of self-piercing rivet insertion process using smoothed particle Galerkin method. In:The 16th international LS-DYNA conference, 10-11 June, pp 1-11
28. Yue Z, Chen Q, Huang L et al (2023) A surrogate model based calibration method for structural adhesive joint progressive failure simulations. J Adhesion 99(10):1579-1606
29. Bhadeshia H (2009) Neural networks and information in materials science. Stat Anal Data Min 1(5):296-305
30. Li M, Liu Z, Huang L et al (2022) A new multi-fidelity surrogate modelling method for engineering design based on neural network and transfer learning. Eng Computation 39(6):2209-2230
31. Efron B, Stein C (1981) The Jackknife estimate of variance. Ann Stat 9(3):586-596
32. Soboĺ I (1993) Sensitivity estimates for nonlinear mathematical models. Math Model Comput Exp 1:407
33. Wan H, Ren W, Wang N (2015) Parameter selection and sampling methods for global sensitivity analysis of Gaussian process models. J Vib Eng 28(5):714-720
34. McKay MD, Beckman RJ, Conover WJ (2000) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 42(1):55-61
35. Gunantara N (2018) A review of multi-objective optimization:methods and its applications. Cogent Eng 5(1):1502242. https://doi.org/10.1080/23311916.2018.1502242
36. Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip O 26:369-395
37. Gao Y, Shi L, Yao P (2000) Study on multi-objective genetic algorithm. In:Proceedings of the 3rd world congress on intelligent control and automation, IEEE, 28 June-2 July, Hefei
38. Huang L, Guo H, Shi Y et al (2017) Fatigue behavior and modeling of self-piercing riveted joints in aluminum alloy 6111. Int J Fatigue 100:274-284
39. Jiang P, Zhou Q, Shao X et al (2020) Surrogate-model-based design and optimization. Springer, Singapore
