Machine learning-based rivet–die design framework for self-piercing riveting of GFRP composite and DQ steel sheets

Durga Rao Boddepalli; R. Ganesh Narayanan; B. R. Manoj; Brajesh Asati; Ishwar Kapoor

doi:10.1007/s12289-026-02052-4

journal article·2026-09

Machine learning-based rivet–die design framework for self-piercing riveting of GFRP composite and DQ steel sheets

Durga Rao Boddepalli, R. Ganesh Narayanan, B. R. Manoj, Brajesh Asati, Ishwar Kapoor

International Journal of Material Forming· Volume 19· Issue 3

Publisher page

Bibliographic record

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

Metals & alloys Composites

OpenAlex topics: Advanced Welding Techniques Analysis · Mechanical Behavior of Composites · Metal Forming Simulation Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (46)

Show source-provided references

1. Liu HT, Liu JY, Wu WJ (2024) Energy absorption characteristics and multi-objective optimization of 3D bionic negative Poisson’s ratio honeycomb. Mater Today Commun 39:109044. https://doi.org/10.1016/j.mtcomm.2024.10904410.1016/j.mtcomm.2024.109044 ↗

2. Delogu M, Zanchi L, Dattilo CA, Pierini M (2017) Innovative composites and hybrid materials for electric vehicles lightweight design in a sustainability perspective. Mater Today Commun 13:192–209. https://doi.org/10.1016/j.mtcomm.2017.09.01210.1016/j.mtcomm.2017.09.012 ↗

3. Matsuzaki R, Shibata M, Todoroki A (2008) Improving performance of GFRP/aluminum single lap joints using bolted/co-cured hybrid method. Compos Part A Appl Sci Manuf 39(2):154–163. https://doi.org/10.1016/j.compositesa.2007.11.00910.1016/j.compositesa.2007.11.009 ↗

4. Mouritz AP, Gellert E, Burchill P, Challis K (2001) Review of advanced composite structures for naval ships and submarines. Compos Struct 53(1):21–42. https://doi.org/10.1016/S0263-8223(00)00175-610.1016/S0263-8223(00)00175-6 ↗

5. Jeyanthi S, Paul J, Kumar C, Kumar NR, Dhinakaran S, Prasad KH (2014) An investigation on dynamic mechanical analysis and low velocity impact analysis of natural thermoplastic composite frontal beams. Int J Appl Eng Res 9(23):13709–13718

6. Kim JW, Kim HS, Lee DG (2015) Manufacturing and characterization of glass fiber/polypropylene prepreg for automotive bumper beam. J Comput Theor Nanosci 12(5):842–846. https://doi.org/10.1166/jctn.2015.381410.1166/jctn.2015.3814 ↗

7. Zindani D, Maity SR, Bhowmik S (2018) A decision-making approach for material selection of polymeric composite bumper beam. In: Advances in Manufacturing and Materials Engineering, pp 112–128. https://doi.org/10.4018/978-1-5225-5216-1.ch00610.4018/978-1-5225-5216-1.ch006 ↗

8. Zamani P, Jaamialahmadi A, da Silva LFM (2023) Fatigue life evaluation of Al-GFRP bonded lap joints under four-point bending using strain-life criteria. Int J Adhes Adhes 126:103338. https://doi.org/10.1016/j.ijadhadh.2023.10333810.1016/j.ijadhadh.2023.103338 ↗

9. Tata Steel, Unveiling the secrets of DQ steel: grades, properties, applications and manufacturing techniques. Tata Steel eCAFez.

10. Sandeep R, Natarajan A (2022) Advances in joining technologies for the innovation of 21st century lightweight aluminium–CFRP hybrid structures. Proc Inst Mech Eng Part E J Process Mech Eng 236(3):1239–1255. https://doi.org/10.1177/0954408921107302710.1177/09544089211073027 ↗

11. Lambiase F, Scipioni SI, Lee CJ, Ko DC, Liu F (2021) A state-of-the-art review on advanced joining processes for metal–composite and metal–polymer hybrid structures. Materials (Basel) 14(8):1890. https://doi.org/10.3390/ma1408189010.3390/ma14081890 ↗

12. Kasaei MM, Beygi R, Carbas RJC, Marques EAS, da Silva LFM (2023) A review on mechanical and metallurgical joining by plastic deformation. Discov Mech Eng 2:5. https://doi.org/10.1007/s44245-023-00012-910.1007/s44245-023-00012-9 ↗

13. Kasaei MM, Carbas RJC, Marques EAS, da Silva LFM (2024) Advanced materials modelling in joining by plastic deformation. State of the Art and Future Trends in Materials Modelling 2. Springer Nature Switzerland, Cham, pp 343–380. https://doi.org/10.1007/978-3-031-72900-3_1810.1007/978-3-031-72900-3_18 ↗

14. Liao Y, Zhang J, Wu G, Zhang H, Liu Y (2024) Effect of die misalignment on the joining quality of Al/steel self-piercing riveting structure. Int J Mater Form 17:14. https://doi.org/10.1007/s12289-023-01809-510.1007/s12289-023-01809-5 ↗

15. Settineri L, Atzeni E, Ippolito R (2010) Self piercing riveting for metal–polymer joints. Int J Mater Form 3(Suppl 1):995–998. https://doi.org/10.1007/s12289-010-0937-310.1007/s12289-010-0937-3 ↗

16. Di Franco G, Fratini L, Pasta A, Ruisi VF (2013) On the self-piercing riveting of aluminium blanks and carbon fibre composite panels. Int J Mater Form 6(1):137–144. https://doi.org/10.1007/s12289-011-1067-210.1007/s12289-011-1067-2 ↗

17. Liu W, Sun W, Chen D (2026) Study on forming mechanism of self-piercing riveting based on shear modified GTN damage model. Int J Mater Form 19:16. https://doi.org/10.1007/s12289-026-01977-010.1007/s12289-026-01977-0 ↗

18. Li C, Yang S, Zheng H, Zhang Y, Wu L, Xue W, Liu Y, Chen Z, He L (2025) Integration of machine learning with finite element analysis in materials science: a review. J Mater Sci 60(20):8285–8307. https://doi.org/10.1007/s10853-025-10913-710.1007/s10853-025-10913-7 ↗

19. Pawlik L, Wilk-Jakubowski JL, Frej D, Wilk-Jakubowski G (2025) Applications of computational mechanics methods combined with machine learning and neural networks: a systematic review (2015–2025). Appl Sci 15(19):10816. https://doi.org/10.3390/app15191081610.3390/app151910816 ↗

20. Alabsi BA, Anbar M, Rihan SDA (2023) Conditional tabular generative adversarial based intrusion detection system for detecting DDoS and DoS attacks on the Internet of Things networks. Sensors (Basel) 23(12):5644. https://doi.org/10.3390/s2312564410.3390/s23125644 ↗

21. Breiman L (2001) Random forests. Mach Learn 45(1):5–3210.1023/A:1010933404324 ↗

22. Zhu C, Byrd RH, Lu P, Nocedal J (1997) Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans Math Softw 23(4):550–560. https://doi.org/10.1145/279232.27923610.1145/279232.279236 ↗

23. Fang Y, Huang L, Zhan Z, Huang S, Liu X, Chen Q, Han W (2022) A framework for calibration of self-piercing riveting process simulation model. J Manuf Process 76:223–235. https://doi.org/10.1016/j.jmapro.2022.01.01510.1016/j.jmapro.2022.01.015 ↗

24. Ji YX, Huang L, Chen QR, Moy CK, Zhang JY, Hu XY, Liu Q (2024) A machine learning-based calibration method for strength simulation of self-piercing riveted joints. Adv Manuf 12(3):465–483. https://doi.org/10.1007/s40436-024-00502-310.1007/s40436-024-00502-3 ↗

25. Kim B, Islam MS, Kim K, Kim HY (2025) Real-time quality classification of robot self-piercing rivet processes in CFRP-steel joints using machine learning. J Manuf Process 153:774–787. https://doi.org/10.1016/j.jmapro.2025.08.05610.1016/j.jmapro.2025.08.056 ↗

26. Soproni I, Womack D, Liu Z, Balaji A, Kulange D (2025) Strength prediction of self-piercing riveted joints using practical regression and Bayesian neural network. SAE Technical Paper. https://doi.org/10.4271/2025-01-506810.4271/2025-01-5068 ↗

27. Boddepalli DR, Narayanan RG, Asati B (2026) A performance index-based investigation of self-pierce riveting of GFRP composite and DQ steel sheets using flat die. J Mater Eng Perform. https://doi.org/10.1007/s11665-026-14243-w10.1007/s11665-026-14243-w ↗

28. Boddepalli DR, Narayanan RG, Asati B, Santra S (2026) Experimental and numerical assessment of self-pierce riveting of GFRP composite and DQ steel sheets using pre-hole and conventional methods. Structures 83:110876. https://doi.org/10.1016/j.istruc.2025.11087610.1016/j.istruc.2025.110876 ↗

29. Hill R (1948) A theory of the yielding and plastic flow of anisotropic metals. Proc R Soc Lond A 193(1033):281–297. https://doi.org/10.1098/rspa.1948.004510.1098/rspa.1948.0045 ↗

30. Rudnytskyj A, Nedilko J, Pilat R, Winiarski V (2022) Investigating the relationship of hardness and flow stress in metal forming. Int J Mech Sci 232:107571. https://doi.org/10.1016/j.ijmecsci.2022.10757110.1016/j.ijmecsci.2022.107571 ↗

31. Warren KC, Lopez-Anido RA, Vel SS, Bayraktar HH (2016) Progressive failure analysis of three-dimensional woven carbon composites in single-bolt, double-shear bearing. Compos Part B Eng 84:266–276. https://doi.org/10.1016/j.compositesb.2015.08.08210.1016/j.compositesb.2015.08.082 ↗

32. Choi KH, Hwang YT, Kim HJ, Kim HS (2019) Progressive failure analysis of woven composites considering structural characteristics based on micro-mechanics. Compos Struct 224:110990. https://doi.org/10.1016/j.compstruct.2019.11099010.1016/j.compstruct.2019.110990 ↗

33. Manufacturer datasheets: CT/E-556 epoxy resin and WR 400 E-glass fabric

34. Daniel IM, Ishai O (1994) Engineering mechanics of composite materials, 3rd edn. Oxford University Press, New York

35. Mallick PK (2007) Fiber-reinforced composites: materials, manufacturing, and design, 3rd edn. CRC Press, Boca Raton. https://doi.org/10.1201/978142000598110.1201/9781420005981 ↗

36. Puck A, Schürmann H (2004) Failure analysis of FRP laminates by means of physically based phenomenological models. In: Murakami E (ed) Failure criteria in fibre-reinforced-polymer composites. Elsevier, Oxford, pp 832–876. https://doi.org/10.1016/B978-008044475-8/50028-710.1016/B978-008044475-8/50028-7 ↗

37. Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K (2019) Modelling tabular data using conditional GAN. In: Advances in Neural Information Processing Systems, vol 32

38. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol 27

39. Cozza IF, Centini MP, Sapio F, Gopalakrishnan V, Buitoni G, Tabarrini M (2023) Application of a machine learning approach for selective catalyst reduction catalyst 3D-CFD modelling: numerical method development and experimental validation. SAE Technical Paper 2023–24–0014. https://doi.org/10.4271/2023-24-001410.4271/2023-24-0014 ↗

40. Koziel S, Leifsson L (2013) Surrogate-based modelling and optimization: applications in engineering. Springer, New York10.1007/978-1-4614-7551-4 ↗

41. Xu Y (2006) Effects of factors on physical attributes of self-piercing riveted joints. Sci Technol Weld Join 11(6):666–671. https://doi.org/10.1179/174329306X13186610.1179/174329306X131866 ↗

42. May MC, Fang Z, Eitel MB, Stricker N, Ghoshdastidar D, Lanza G (2023) Graph-based prediction of missing KPIs through optimization and random forests for KPI systems. Prod Eng 17(2):211–222. https://doi.org/10.1007/s11740-022-01179-y10.1007/s11740-022-01179-y ↗

43. Sharma GVSS, Rao PS, Babu BS (2016) Process-based tolerance assessment of connecting rod machining process. J Ind Eng Int 12:211–220. https://doi.org/10.1007/s40092-015-0138-210.1007/s40092-015-0138-2 ↗

44. Mittal RO, Irani SA, Lehtihet EA (1990) Tolerance control in the machining of discrete components. J Manuf Syst 9(3):233–24610.1016/0278-6125(90)90054-L ↗

45. Casalino G (2011) DOE analysis of the effects of geometrical parameters on the self-piercing riveting of aluminium alloy AA6060T4. Key Eng Mater 473:733–738. https://doi.org/10.4028/www.scientific.net/KEM.473.73310.4028/www.scientific.net/KEM.473.733 ↗

46. Deng JH, Lyu F, Chen RM et al (2019) Influence of die geometry on self-piercing riveting of aluminum alloy AA6061-T6 to mild steel SPFC340 sheets. Adv Manuf 7:209–220. https://doi.org/10.1007/s40436-019-00250-910.1007/s40436-019-00250-9 ↗