A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints

Abstract

In lightweight automotive vehicles, the application of self-piercing rivet (SPR) joints is becoming increasingly widespread. Considering the importance of automotive service performance, the fatigue performance of SPR joints has received considerable attention. Therefore, this study proposes a data-driven approach to predict the fatigue life and failure modes of SPR joints. The dataset comprises three specimen types: cross-tensile, cross-peel, and tensile-shear. To ensure data consistency, a finite element analysis was employed to convert the external loads of the different specimens. Feature selection was implemented using various machine-learning algorithms to determine the model input. The Gaussian process regression algorithm was used to predict fatigue life, and its performance was compared with different kernel functions commonly used in the field. The results revealed that the Matern kernel exhibited an exceptional predictive capability for fatigue life. Among the data points, 95.9% fell within the 3-fold error band, and the remaining 4.1% exceeded the 3-fold error band owing to inherent dispersion in the fatigue data. To predict the failure location, various tree and artificial neural network (ANN) models were compared. The findings indicated that the ANN models slightly outperformed the tree models. The ANN model accurately predicts the failure of joints with varying dimensions and materials. However, minor deviations were observed for the joints with the same sheet. Overall, this data-driven approach provided a reliable predictive model for estimating the fatigue life and failure location of SPR joints.

Keywords

Self-piercing rivet (SPR) joints / Fatigue life prediction / Failure mode prediction / Machine learning

References

[1] Li D, Chrysanthou A, Patel I, et al. Self-piercing riveting-a review. Int J Adv Manuf Technol, 2017, 92: 1777-1824.

[2] Gay A, Lefebvre F, Bergamo S, et al. Fatigue performance of a self-piercing rivet joint between aluminum and glass fiber reinforced thermoplastic composite. Int J Fatigue, 2016, 83(2): 127-134.

[3] Zhang X, He X, Wei W, et al. Fatigue characterization and crack propagation mechanism of self-piercing riveted joints in titanium plates. Int J Fatigue, 2020, 134.

[4] Zhao L, He X, Xing B, et al. Influence of sheet thickness on fatigue behavior and fretting of self-piercing riveted joints in aluminum alloy 5052. Mater Des, 2015, 87: 1010-1017.

[5] Xing B, Tang F, Song C, et al. Static and fatigue behavior of self-piercing riveted joints with two overlap areas. J Mater Res Technol, 2021, 14: 1333-1338.

[6] Han L, Chrysanthou A, O’Sullivan JM. Fretting behaviour of self-piercing riveted aluminium alloy joints under different interfacial conditions. Mater Des, 2006, 27: 200-208.

[7] Huang L, Bonnen J, Lasecki J, et al. Fatigue and fretting of mixed metal self-piercing riveted joint. Int J Fatigue, 2016, 83(2): 230-239.

[8] Jia Y, Huang Z, Zhang Y, et al. Forming quality and fatigue behavior of self-piercing riveted joints of DP590 and AA6061 plates. Adv Mater Sci Eng, 2021, 2021: 4381544.

[9] Kang J, Rao H, Zhang R, et al. Tensile and fatigue behaviour of self-piercing rivets of CFRP to aluminium for automotive application. Mater Sci Eng, 2016, 137.

[10] Zhang X, He X, Gu F, et al. Self-piercing riveting of aluminium–lithium alloy sheet materials. J Mater Process Tech, 2019, 268: 192-200.

[11] Rupp A, Störzel K, Grubisic V (1995) Computer aided dimensioning of spot-welded automotive structures. SAE Technical Paper 950711. https://doi.org/10.4271/950711

[12] Presse J, Künkler B, Michler T. Stress-based approach for fatigue life calculation of multi-material connections hybrid joined by self-piercing rivets and adhesive. Thin Wall Struct, 2021, 159.

[13] Rao HM, Kang J, Huff G, et al. Impact of specimen configuration on fatigue properties of self-piercing riveted aluminum to carbon fiber reinforced polymer composite. Int J Fatigue, 2018, 113: 11-22.

[14] Cox A, Hong J (2016) Fatigue evaluation procedure development for self-piercing riveted joints using the battelle structural stress method. SAE Technical Paper 2016-01-0384. https://doi.org/10.4271/2016-01-0384

[15] Wu G, Li D, Lai WJ, et al. Fatigue behaviors and mechanism-based life evaluation on SPR-bonded aluminum joint. Int J Fatigue, 2021, 142.

[16] Rao H, Kang J, Huff G, et al. Structural stress method to evaluate fatigue properties of similar and dissimilar self-piercing riveted joints. Metals, 2019, 9(3): 359.

[17] Moraes JFC, Rao HM, Jordon JB, et al. High cycle fatigue mechanisms of aluminum self-piercing riveted joints. Fatigue Fract Eng Mater Struct, 2018, 41(1): 57-70.

[18] Huang L, Shi Y, Guo H, et al. Fatigue behavior and life prediction of self-piercing riveted joint. Int J Fatigue, 2016, 88: 96-110.

[19] Kang SH, Kim HK. Fatigue strength evaluation of self-piercing riveted Al-5052 joints under different specimen configurations. Int J Fatigue, 2015, 80: 58-68.

[20] He L, Wang Z, Akebono H, et al. Machine learning-based predictions of fatigue life and fatigue limit for steels. J Mater Sci Technol, 2021, 90: 9-19.

[21] Sai NJ, Rathore P, Chauhan A. Machine learning-based predictions of fatigue life for multi-principal element alloys. Scr Mater, 2023, 226.

[22] Amiri N, Farrahi GH, Kashyzadeh KR, et al. Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints. J Manuf Process, 2020, 52: 26-34.

[23] Silva GC, Beber VC, Pitz DB. Machine learning and finite element analysis: an integrated approach for fatigue lifetime prediction of adhesively bonded joints. Fatigue Fract Eng Mat Struct, 2021, 44(12): 3334-3348.

[24] Solhmirzaei R, Salehi H, Kodur V, et al. Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams. Eng Struct, 2020, 224.

[25] Braun M, Kellner L, Schreiber S, et al. Prediction of fatigue failure in small-scale butt-welded joints with explainable machine learning. Proc Struct Integr, 2022, 38: 182-191.

[26] Ma Y, Shan H, Niu S, et al. A comparative study of friction self-piercing riveting and self-piercing riveting of aluminum alloy AA5182-O. Engineering, 2021, 7(12): 1741-1750.

[27] Choi DH, Han DW, Kim HK. Fatigue life estimation of self-piercing riveted aluminum joints under mixed-mode loading. Int J Fatigue, 2017, 97: 20-28.

[28] Zhang S, Lei H, Zhou Z, et al. Fatigue life analysis of high-strength bolts based on machine learning method and shapley additive explanations (SHAP) approach. Structures, 2023, 51: 275-287.

[29] Zhao YG, Huang ZC, Jiang YQ. Effect of low-velocity impact on mechanical property and fatigue life of DP590/AA6061 self-piercing riveted joints. Mater Res Express, 2022, 9(2): .

[30] Rasmussen CE. Gaussian processes in machine learning. Advanced lectures on machine learning, 2004, Berlin: Springer 63-71.

[31] Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharmaceut Biomed, 2000, 22(5): 717-727.

[32] Myles AJ, Feudale RN, Liu Y, et al. An introduction to decision tree modeling. J Chemom, 2004, 18(6): 275-285.

[33] Xu R (2013) Improvements to random forest methodology. Dissertation, Iowa State University

[34] Bentéjac C, Csörgő A, Martínez-Muñoz G. A comparative analysis of gradient boosting algorithms. Artif Intell Rev, 2021, 54: 1937-1967.

[35] Cai J, Luo J, Wang S, et al. Feature selection in machine learning: a new perspective. Neurocomputing, 2018, 300: 70-79.

[36] Woo A (2022) Resistance spot weld fatigue life prediction method compatibility with self-piercing rivets. Dissertation, University of Waterloo

[37] Buntine W, Jakulin A (2012) Applying discrete PCA in data analysis. In: Proceedings of the 20th conference on uncertainty in artificial intelligence, Banff, Canada

Funding

Yalong River Joint Fund http://dx.doi.org/10.13039/501100019490(52205377)

Key Basic Research Project of Suzhou(#SJC2022029，#SJC2022031)

National Key Research and Development Program(2022YFB4601804)