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

doi:10.1007/s40436-024-00498-w

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 538-555.doi: 10.1007/s40436-024-00498-w

• • 上一篇

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

Jian Wang^1,2, Qiu-Ren Chen³, Li Huang^2,3, Chen-Di Wei^3,4, Chao Tong², Xian-Hui Wang¹, Qing Liu²

1. Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China;
2. Material Academy, Jitri, Suzhou, 215100, Jiangsu, People's Republic of China;
3. Key Laboratory for Light-weight Materials, Nanjing Tech University, Nanjing, 210009, People's Republic of China;
4. Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, People's Republic of China

收稿日期:2023-10-08 修回日期:2023-11-12 发布日期:2024-09-07
通讯作者: Qiu-Ren Chen,E-mail:chenqiu@njtech.edu.cn;Xian-Hui Wang,E-mail:11920158@njust.edu.cn E-mail:chenqiu@njtech.edu.cn;11920158@njust.edu.cn
作者简介:Jian Wang Master student at Nanjing University of Science and Technology. His research interests focus on the fatigue life prediction of adhesive- selfpiercing rivet hybrid joints;
Qiu-Ren Chen Distinguished professor of Nanjing Tech University, and a technical expert of JITRI Academy. His main research interests include fatigue durability analysis methods, failure behavior modeling of joints and industrial simulation software development;
Li Huang Doctor, vice director of Materials Data Department at Materials Academy JITRI and guest professor at Nanjing Tech University. His research interests include AI Algorithms & Engineering Applications (AI for Engineering), Integrated Computational Materials Engineering (ICME), Digital Design of Material Manufacturing and Joining Process, and Material Fatigue & Fracture;
Chen-Di Wei Ph.D. student at school of advanced technology, Xi’an Jiaotong-Liverpool University, China. She received a M.S. degree in Space Science and Technology from University College London. Her research interests include adhesive bonded joint technology and machine learning;
Chao Tong Machine learning engineer, he graduated in 2018 with a master’s degree in Material Science and Engineering from Soochow University. He is currently employed at the Materials Bigdata and Applications Division, Delta Advanced Materials Research Institute, Suzhou;
Xian-Hui Wang Doctor, is a professor and doctoral supervisor at Nanjing University of Science and Technology. He currently holds multiple positions including the dean of the Vehicle Engineering Research Institute at Nanjing University of Science and Technology, member of the Professor Committee and Academic Committee of the School of Mechanical Engineering, director of the Jiangsu Province Commercial Vehicle Intelligent Chassis Engineering Research Center, and head of the Nanjing branch of the National Key Laboratory of Advanced Off-road System Technology. His research interests focus on vehicle safety protection and intelligent wire control technology;
Qing Liu Director of Material Academy, JITRI, Suzhou and the director of the Lightweight Materials Center at Nanjing Tech University. His research interests include electron microscopy techniques for characterizing the microstructure of deformed metals, micro-mechanisms and mechanical behaviors of metal deformation, formation mechanisms.
基金资助:
This research was supported by the National Natural Science Foundation of China (Grant No. 52205377), the Key Basic Research Project of Suzhou (Grant Nos. SJC2022029, SJC2022031), and the National Key Research and Development Program (Grant No. 2022YFB4601804).

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

Jian Wang^1,2, Qiu-Ren Chen³, Li Huang^2,3, Chen-Di Wei^3,4, Chao Tong², Xian-Hui Wang¹, Qing Liu²

1. Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China;
2. Material Academy, Jitri, Suzhou, 215100, Jiangsu, People's Republic of China;
3. Key Laboratory for Light-weight Materials, Nanjing Tech University, Nanjing, 210009, People's Republic of China;
4. Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, People's Republic of China

Received:2023-10-08 Revised:2023-11-12 Published:2024-09-07
Contact: Qiu-Ren Chen,E-mail:chenqiu@njtech.edu.cn;Xian-Hui Wang,E-mail:11920158@njust.edu.cn E-mail:chenqiu@njtech.edu.cn;11920158@njust.edu.cn
Supported by:
This research was supported by the National Natural Science Foundation of China (Grant No. 52205377), the Key Basic Research Project of Suzhou (Grant Nos. SJC2022029, SJC2022031), and the National Key Research and Development Program (Grant No. 2022YFB4601804).

摘要/Abstract

摘要： 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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00498-w

关键词: Self-piercing rivet (SPR) joints, Fatigue life prediction, Failure mode prediction, Machine learning

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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00498-w

Key words: Self-piercing rivet (SPR) joints, Fatigue life prediction, Failure mode prediction, Machine learning

Jian Wang, Qiu-Ren Chen, Li Huang, Chen-Di Wei, Chao Tong, Xian-Hui Wang, Qing Liu. A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints[J]. Advances in Manufacturing, 2024, 12(3): 538-555.

参考文献

1. Li D, Chrysanthou A, Patel I et al (2017) Self-piercing riveting-a review. Int J Adv Manuf Technol 92:1777-1824
2. Gay A, Lefebvre F, Bergamo S et al (2016) Fatigue performance of a self-piercing rivet joint between aluminum and glass fiber reinforced thermoplastic composite. Int J Fatigue 83(2):127-134
3. Zhang X, He X, Wei W et al (2020) Fatigue characterization and crack propagation mechanism of self-piercing riveted joints in titanium plates. Int J Fatigue 134:105465. https://doi.org/10.1016/j.ijfatigue.2019.105465
4. Zhao L, He X, Xing B et al (2015) Influence of sheet thickness on fatigue behavior and fretting of self-piercing riveted joints in aluminum alloy 5052. Mater Des 87:1010-1017
5. Xing B, Tang F, Song C et al (2021) Static and fatigue behavior of self-piercing riveted joints with two overlap areas. J Mater Res Technol 14:1333-1338
6. Han L, Chrysanthou A, O'Sullivan JM (2006) Fretting behaviour of self-piercing riveted aluminium alloy joints under different interfacial conditions. Mater Des 27:200-208
7. Huang L, Bonnen J, Lasecki J et al (2016) Fatigue and fretting of mixed metal self-piercing riveted joint. Int J Fatigue 83(2):230-239
8. Jia Y, Huang Z, Zhang Y et al (2021) Forming quality and fatigue behavior of self-piercing riveted joints of DP590 and AA6061 plates. Adv Mater Sci Eng 2021:4381544. https://doi.org/10.1155/2021/4381544
9. Kang J, Rao H, Zhang R et al (2016) Tensile and fatigue behaviour of self-piercing rivets of CFRP to aluminium for automotive application. Mater Sci Eng 137:012025. https://doi.org/10.1088/1757-899X/137/1/012025
10. Zhang X, He X, Gu F et al (2019) Self-piercing riveting of aluminium-lithium alloy sheet materials. J Mater Process Tech 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 (2021) Stress-based approach for fatigue life calculation of multi-material connections hybrid joined by self-piercing rivets and adhesive. Thin Wall Struct 159:107192. https://doi.org/10.1016/j.tws.2020.107192
13. Rao HM, Kang J, Huff G et al (2018) Impact of specimen configuration on fatigue properties of self-piercing riveted aluminum to carbon fiber reinforced polymer composite. Int J Fatigue 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 (2021) Fatigue behaviors and mechanism-based life evaluation on SPR-bonded aluminum joint. Int J Fatigue 142:105948. https://doi.org/10.1016/j.ijfatigue.2020.105948
16. Rao H, Kang J, Huff G et al (2019) Structural stress method to evaluate fatigue properties of similar and dissimilar self-piercing riveted joints. Metals 9(3):359. https://doi.org/10.3390/met9030359
17. Moraes JFC, Rao HM, Jordon JB et al (2018) High cycle fatigue mechanisms of aluminum self-piercing riveted joints. Fatigue Fract Eng Mater Struct 41(1):57-70
18. Huang L, Shi Y, Guo H et al (2016) Fatigue behavior and life prediction of self-piercing riveted joint. Int J Fatigue 88:96-110
19. Kang SH, Kim HK (2015) Fatigue strength evaluation of self-piercing riveted Al-5052 joints under different specimen configurations. Int J Fatigue 80:58-68
20. He L, Wang Z, Akebono H et al (2021) Machine learning-based predictions of fatigue life and fatigue limit for steels. J Mater Sci Technol 90:9-19
21. Sai NJ, Rathore P, Chauhan A (2023) Machine learning-based predictions of fatigue life for multi-principal element alloys. Scr Mater 226:115214. https://doi.org/10.1016/j.scriptamat.2022.115214
22. Amiri N, Farrahi GH, Kashyzadeh KR et al (2020) Applications of ultrasonic testing and machine learning methods to predict the static&fatigue behavior of spot-welded joints. J Manuf Process 52:26-34
23. Silva GC, Beber VC, Pitz DB (2021) Machine learning and finite element analysis:an integrated approach for fatigue lifetime prediction of adhesively bonded joints. Fatigue Fract Eng Mat Struct 44(12):3334-3348
24. Solhmirzaei R, Salehi H, Kodur V et al (2020) Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams. Eng Struct 224:111221. https://doi.org/10.1016/j.engstruct.2020.111221
25. Braun M, Kellner L, Schreiber S et al (2022) Prediction of fatigue failure in small-scale butt-welded joints with explainable machine learning. Proc Struct Integr 38:182-191
26. Ma Y, Shan H, Niu S et al (2021) A comparative study of friction self-piercing riveting and self-piercing riveting of aluminum alloy AA5182-O. Engineering 7(12):1741-1750
27. Choi DH, Han DW, Kim HK (2017) Fatigue life estimation of self-piercing riveted aluminum joints under mixed-mode loading. Int J Fatigue 97:20-28
28. Zhang S, Lei H, Zhou Z et al (2023) Fatigue life analysis of high-strength bolts based on machine learning method and shapley additive explanations (SHAP) approach. Structures 51:275-287
29. Zhao YG, Huang ZC, Jiang YQ (2022) Effect of low-velocity impact on mechanical property and fatigue life of DP590/AA6061 self-piercing riveted joints. Mater Res Express 9(2):026514. https://doi.org/10.1088/2053-1591/ac4d54
30. Rasmussen CE (2004) Gaussian processes in machine learning. In:Advanced lectures on machine learning, Springer, Berlin, pp 63-71. https://doi.org/10.1007/978-3-540-28650-9_4
31. Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharmaceut Biomed 22(5):717-727
32. Myles AJ, Feudale RN, Liu Y et al (2004) An introduction to decision tree modeling. J Chemom 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 (2021) A comparative analysis of gradient boosting algorithms. Artif Intell Rev 54:1937-1967
35. Cai J, Luo J, Wang S et al (2018) Feature selection in machine learning:a new perspective. Neurocomputing 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

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

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

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