Physics based data driven method for the crashworthiness design of origami composite tubes
Source abstract
Abstract
Abstract A novel method based on a physics informed data driven model is developed to design an origami composite crash tube. The structure consists of two axially stacked basic components, called modules. Each module presents lower and upper square sections with an octagonal section in the middle. The parameters of the octagonal cross-section and the height of each module are optimized to maximize the energy absorption of the tube when subjected to an axial impact. In contrast to standard surrogate modelling techniques, whose accuracy only depends on the amount of available data, a Physics-informed Neural Network (PINN) scheme is adopted to correlate the crushing response of the single modules to that of the whole origami tube, constraining the data driven method to physically consistent predictions. The PINN is first trained on the results obtained with an experimentally validated Finite Element model and then used to optimize the structure. Results show that the PINN can accurately predict the crushing response of the origami tube, while consistently reducing the computational effort required to explore the whole design domain. Also, the comparison with a standard Feed Forward Neural Network (FFNN) shows that the PINN scheme leads to more accurate results.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cellular and Composite Structures · Mechanical Engineering and Vibrations Research · Vibration Control and Rheological Fluids
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (34)
Show source-provided references
1. ASTM Standard D3410/D3410M-16, 2021, Standard Test Method for Compressive Properties of Polymer Matrix Composite Materials with Unsupported Gage Section by Shear Loading. ASTM International, West Conshohocken, PA (2021). https://doi.org/10.1520/D3410/D3410M-16, https://www.astm.org10.1520/D3410/D3410M-16 ↗
2. Avalle, M., Chiandussi, G., & Belingardi, G. (n.d.). Design optimization by response surface methodology: application to crashworthiness design of vehicle structures. https://doi.org/10.1007/s00158-002-0243-x10.1007/s00158-002-0243-x ↗
3. Baler, H.: Ill-posed problems in structural optimization and their practical consequences In Structural Optimization. Springer, Berlin (1994)
4. Boreanaz, M., Belingardi, G., Maia, C.D.F.: Application of the origami shape in the development of automotive crash box. Mater Des Process Commun 2(4), e181 (2020). https://doi.org/10.1002/MDP2.18110.1002/MDP2.181 ↗
5. Boria, S., Scattina, A., Belingardi, G.: Axial energy absorption of CFRP truncated cones. Compos. Struct. 130, 18–28 (2015). https://doi.org/10.1016/J.COMPSTRUCT.2015.04.02610.1016/J.COMPSTRUCT.2015.04.026 ↗
6. Boria, S., Scattina, A., Belingardi, G.: Experimental evaluation of a fully recyclable thermoplastic composite. Compos. Struct. 140, 21–35 (2016). https://doi.org/10.1016/J.COMPSTRUCT.2015.12.04910.1016/J.COMPSTRUCT.2015.12.049 ↗
7. Boria, S., Belingardi, G., Fiumarella, D., Scattina, A.: Experimental crushing analysis of thermoplastic and hybrid composites. Compos. Struct. 226(February), 111241 (2019). https://doi.org/10.1016/j.compstruct.2019.11124110.1016/j.compstruct.2019.111241 ↗
8. Ciampaglia, A., Fiumarella, D., Boursier Niutta, C., Ciardiello, R., Belingardi, G.: Impact response of an origami-shaped composite crashbox: experimental analysis and numerical optimization. Compos. Struct. 256, 113093 (2020a). https://doi.org/10.1016/j.compstruct.2020.11309310.1016/j.compstruct.2020.113093 ↗
9. Ciampaglia, A., Santini, A., Belingardi, G.: Design and analysis of automotive lightweight materials suspension based on finite element analysis. J Mech Eng Sci (2020). https://doi.org/10.1177/095440622094745710.1177/0954406220947457 ↗
10. Dias, J. P., Cadete, R. N., & Pereira, M. S. (2005). Optimization in vehicle crashworthiness design using surrogate models COST TU1101 View project 6 th World Congresses of Structural and Multidisciplinary Optimization Optimization in vehicle crashworthiness design using surrogate models. https://www.researchgate.net/publication/250092920
11. Fiumarella, D., & Raponi, E. (2020). Experimental analysis and numerical optimization of a thermoplastic composite in crashworthiness.
12. Hadamard, J.: Sur les problèmes aux dérivées partielles et leur signification physique. Princeton Univ. Bull. 13, 49–52 (1902)
13. Hallquist, J. O. (2006). LS-DYNA ® THEORY MANUAL. www.lstc.com
14. Hou, S., Li, Q., Long, S., Yang, X., Li, W.: Design optimization of regular hexagonal thin-walled columns with crashworthiness criteria. Finite Elem. Anal. Des. 43(6–7), 555–565 (2007). https://doi.org/10.1016/J.FINEL.2006.12.00810.1016/J.FINEL.2006.12.008 ↗
15. ASTM Standard 5379/D5379M − 19, 2021, Standard Test Uethod for Shear Properties of Composite Materials by the V-Notched Beam Method. ASTM International, West Conshohocken, PA (2021). https://doi.org/10.1520/D5379_D5379M-19E01, https://www.astm.org10.1520/D5379_D5379M-19E01 ↗
16. Kingma, D. P., & Ba, J. L. (2014). Adam: A Method for Stochastic Optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. https://doi.org/10.48550/arxiv.1412.698010.48550/arxiv.1412.6980 ↗
17. Koronis, G., Silva, A., Fontul, M.: Green composites: a review of adequate materials for automotive applications. Compos. B Eng. 44(1), 120–127 (2013). https://doi.org/10.1016/J.COMPOSITESB.2012.07.00410.1016/J.COMPOSITESB.2012.07.004 ↗
18. Li, Y., You, Z.: Origami concave tubes for energy absorption. Int. J. Solids Struct. 169, 21–40 (2019). https://doi.org/10.1016/J.IJSOLSTR.2019.03.02610.1016/J.IJSOLSTR.2019.03.026 ↗
19. Ma, J., Chai, S., Chen, Y.: Geometric design, deformation mode, and energy absorption of patterned thin-walled structures. Mech. Mater. 168, 104269 (2022). https://doi.org/10.1016/J.MECHMAT.2022.10426910.1016/J.MECHMAT.2022.104269 ↗
20. Ma, J., & You, Z. (2014). A Novel Origami Crash Box With Varying Profiles. Proceedings of the ASME Design Engineering Technical Conference, 6 B. https://doi.org/10.1115/DETC2013-1349510.1115/DETC2013-13495 ↗
21. Martulli, L.M., Creemers, T., Schöberl, E., Hale, N., Kerschbaum, M., Lomov, S.V., Swolfs, Y.: A thick-walled sheet moulding compound automotive component: Manufacturing and performance. Compos Part A Appl Sci Manufact 128(2019), 105688 (2020). https://doi.org/10.1016/j.compositesa.2019.10568810.1016/j.compositesa.2019.105688 ↗
22. Nemeth, I. (n.d.). Standard Test Method for Tensile Properties of Polymer Matrix Composite Materials.
23. Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019). https://doi.org/10.1016/J.JCP.2018.10.04510.1016/J.JCP.2018.10.045 ↗
24. Raponi, E., Fiumarella, D., Boria, S., Scattina, A., Belingardi, G.: Methodology for parameter identification on a thermoplastic composite crash absorber by the sequential response surface method and efficient global optimization. Compos. Struct. 278(July), 114646 (2021). https://doi.org/10.1016/j.compstruct.2021.11464610.1016/j.compstruct.2021.114646 ↗
25. Rwawiire, S., Tomkova, B., Militky, J., Jabbar, A., Kale, B.M.: Development of a biocomposite based on green epoxy polymer and natural cellulose fabric (bark cloth) for automotive instrument panel applications. Compos. B Eng. 81, 149–157 (2015). https://doi.org/10.1016/J.COMPOSITESB.2015.06.02110.1016/J.COMPOSITESB.2015.06.021 ↗
26. Sakaridis, E., Karathanasopoulos, N., Mohr, D.: Machine-learning based prediction of crash response of tubular structures. Int. J. Impact Eng 166, 104240 (2022). https://doi.org/10.1016/J.IJIMPENG.2022.10424010.1016/J.IJIMPENG.2022.104240 ↗
27. Samaniego, E., Anitescu, C., Goswami, S., Nguyen-Thanh, V.M., Guo, H., Hamdia, K., Zhuang, X., Rabczuk, T.: An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Comput. Methods Appl. Mech. Eng. 362, 112790 (2020). https://doi.org/10.1016/J.CMA.2019.11279010.1016/J.CMA.2019.112790 ↗
28. Vu-Bac, N., Duong, T.X., Lahmer, T., Zhuang, X., Sauer, R.A., Park, H.S., Rabczuk, T.: A NURBS-based inverse analysis for reconstruction of nonlinear deformations of thin shell structures. Comput. Methods Appl. Mech. Eng. 331, 427–455 (2018). https://doi.org/10.1016/J.CMA.2017.09.03410.1016/J.CMA.2017.09.034 ↗
29. Wu, J., Zhang, Y., Li, K., Su, L.: Origami-inspired metamaterials hierarchical structure with tailorable crushing behavior. Constr. Build. Mater. 345, 128328 (2022). https://doi.org/10.1016/J.CONBUILDMAT.2022.12832810.1016/J.CONBUILDMAT.2022.128328 ↗
30. Ye, H., Ma, J., Zhou, X., Wang, H., You, Z.: Energy absorption behaviors of pre-folded composite tubes with the full-diamond origami patterns. Compos. Struct. 221, 110904 (2019). https://doi.org/10.1016/J.COMPSTRUCT.2019.11090410.1016/J.COMPSTRUCT.2019.110904 ↗
31. Ye, H., Zhou, X., Ma, J., Wang, H., You, Z.: Axial crushing behaviors of composite pre-folded tubes made of KFRP/CFRP hybrid laminates. Thin-Walled Structures 149, 106649 (2020). https://doi.org/10.1016/J.TWS.2020.10664910.1016/J.TWS.2020.106649 ↗
32. Zhang, P., Sun, Z., Wang, H., Xiang, X.: Performance study of origami crash tubes based on energy dissipation history. Energies (2022). https://doi.org/10.3390/en1509310910.3390/en15093109 ↗
33. Zhao, X., Hu, Y., Hagiwara, I.: Shape optimization to improve energy absorption ability of cylindrical thin-walled origami structure. J Comput Sci Technol 5(3), 148–162 (2011). https://doi.org/10.1299/JCST.5.14810.1299/JCST.5.148 ↗
34. Zhou, C., Zhou, Y., Wang, B.: Crashworthiness design for trapezoid origami crash boxes. Thin-Wall Struct 117, 257–267 (2017). https://doi.org/10.1016/J.TWS.2017.03.02210.1016/J.TWS.2017.03.022 ↗