Predictive modeling of buckling in composite tubes: Integrating artificial neural networks for damage detection

References

• D.K. Hale, The physical properties of composite materials, J. Mater. Sci., vol. 11, no. 11, pp. 2105–2141, 1976. DOI: 10.1007/PL00020339.
   Web of Science ®Google Scholar
• N. Zimmermann and P.H. Wang, A review of failure modes and fracture analysis of aircraft composite materials, Eng. Failure Anal., vol. 115, pp. 104692, 2020. DOI: 10.1016/j.engfailanal.2020.104692.
   Web of Science ®Google Scholar
• D.M. Mcgowan and D.R. Ambur, Compression response of a sandwich fuselage keel panel with and without damage, 1997.
   Google Scholar
• S.S. Wang, Fracture mechanics for delamination problems in composite materials, J. Composite Mater., vol. 17, no. 3, pp. 210–223, 1983. DOI: 10.1177/002199838301700302.
   Web of Science ®Google Scholar
• F.-K. Chang and Z. Kutlu, Delamination effects on composite shells, 1990.
   Google Scholar
• J. Babu, T. Sunny, A.P. Nevin, K.P. Mohan, J. Philip, and J.P. Davim, Assessment of delamination in composite materials: a review, J. Eng. Manuf., vol. 230, no. 11, pp. 1990–2003, 2016.
   Google Scholar
• M. Al-Assadi, H. El Kadi, and I.M. Deiab, Predicting the fatigue life of different composite materials using artificial neural networks, Appl. Compos. Mater., vol. 17, no. 1, pp. 1–14, 2010. DOI: 10.1007/s10443-009-9090-x.
   Web of Science ®Google Scholar
• T. Sabiston, K. Inal, and P. Lee-Sullivan, Application of artificial neural networks to predict fibre orientation in long fibre compression moulded composite materials, Elsevier, vol. 190, pp. 108034, 2020. DOI: 10.1016/j.compscitech.2020.108034.
   Google Scholar
• A. Khan, N. Kim, J.K. Shin, H.S. Kim, and B.D. Youn, Damage assessment of smart composite structures via machine learning: a review, JMST Adv., vol. 1, no. 1-2, pp. 107–124, 2019. DOI: 10.1007/s42791-019-0012-2.
   Google Scholar
• A. Kaveh, N. Kim, J.K. Shin, H.S. Kim, and B.D. Youn, Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders, Acta Mech., vol. 232, no. 3, pp. 921–931, 2021. DOI: 10.1007/s00707-020-02878-2.
   Web of Science ®Google Scholar
• G.F. Gomes, Deep learning enhanced metamodel design based on reduced mode shapes for delamination identification in composite structures, Mech. Adv. Mater. Struct., pp. 1–18, 2024. DOI: 10.1080/15376494.2024.2302921.
   Web of Science ®Google Scholar
• L.E. DE Castro Saiki and G.F. Gomes, Understanding and mitigating delamination in composite materials: a comprehensive review, Mech. Adv. Mater. Struct., pp. 1–21, 2024. DOI: 10.1080/15376494.2024.2333490.
   Web of Science ®Google Scholar
• P.D. Mangalgiri, Composite materials for aerospace applications. Bulletin of Materials Science, vol. 22, pp. 657–664, 1999.
   Google Scholar
• Z. Sun, Z. Lei, R. Bai, H. Jiang, J. Zou, Y. Ma, and C. Yan, Prediction of compression buckling load and buckling mode of hat-stiffened panels using artificial neural network, Eng. Struct., vol. 242, pp. 112275, 2021. DOI: 10.1016/j.engstruct.2021.112275.
   Google Scholar
• Z. u R. Tahir, and P. Mandal, Artificial neural network prediction of buckling load of thin cylindrical shells under axial compression, Eng. Struct., v. vol. 152, pp. 843–855, 2017. DOI: 10.1016/j.engstruct.2017.09.016.
   Web of Science ®Google Scholar
• S.A. Sajjady, S. Rahnama, M. Lofti, and R. Nosouhi, Numerical analysis of delamination buckling in composite cylindrical shell under uniform external pressure: cohesive element method, J. Modern Process. Manuf. Prod., vol. v. 6, no. 3, pp. 77–96, 2017.
   Google Scholar
• R. Ceravolo, M. Civera, E. Lenticchia, G. Miraglia, and C. Surace, Detection and localization of multiple damages through entropy in information theory, Appl. Sci., vol. 11, no. 13, pp. 5773, 2021. DOI: 10.3390/app11135773.
   Google Scholar
• Mechanical APDL command reference, Disponível em: https://www.mm.bme.hu/∼gyebro/files/ans_help_v182/ans_cmd/Hlp_C_CmdTOC.html. Acesso em: 13 ago. 2023.
   Google Scholar
• G.H. Golub and R. Underwood, The Block Lanczos Method for Computing Eigenvalues, In Mathematical Software, pp. 361–377. Elsevier. https://doi.org/10.1016/b978-0-12-587260-7.50018-2.
   Google Scholar
• D.G. Rethwisch, and W.D. Callister Jr., Fundamentals of Materials Science and Engineering: An Integrated Approach, 6th Edition, John Wiley & Sons, 2020.
   Google Scholar
• L.M. Kachanov, Delamination buckling of composite materials. Mech. Elast. Stab., vol 14. Springer, Dordrecht, 1988.
   Google Scholar
• C.A. Diniz, D.B. Bortoluzzi, J.L.J. Pereira, S.S. Cunha Junior, and G.F. Gomes, On the influence of manufacturing parameters on buckling and modal properties of sandwich composite structures, In Structures, vol. 46, pp. 664–680, Elsevier BV, 2022. https://doi.org/10.1016/j.istruc.2022.10.059.
   Google Scholar
• D. Wang, J. Lin, and Y.-G. Wang, Query-Efficient Adversarial Attack Based On Latin Hypercube Sampling. In 2022 IEEE International Conference on Image Processing (ICIP), IEEE, 2022. https://doi.org/10.1109/icip46576.2022.9897705.
   Google Scholar
• R.S. Priyadarsini, V. Kalyanaraman, and S.M. Srinivasan, Numerical and experimental study of buckling of advanced fiber composite cylinders under axial compression, Int. J. Str. Stab. Dyn., vol. 12, no. 04, pp. 1250028, 2012. DOI: 10.1142/S0219455412500289.
   Google Scholar