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Original Article

Quantification of the out-of-plane loading fatigue response of bistable CFRP laminates using a machine learning approach

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Received 31 Jan 2024, Accepted 08 Apr 2024, Published online: 30 Apr 2024

References

  • M.W. Hyer, Some observations on the cured shape of thin unsymmetric laminates, J. Compos. Mater., vol. 15, no. 2, pp. 175–194, 3 1981. DOI: 10.1177/002199838101500207.
  • M.W. Hyer, Calculations of the room-temperature shapes of unsymmetric laminates, J. Compos. Mater., vol. 15, no. 4, pp. 296–310, 7 1981. DOI: 10.1177/002199838101500401.
  • M.W. Hyer, The room-temperature shapes of four-layer unsymmetric cross-ply laminates, J. Compos. Mater., vol. 16, no. 4, pp. 318–340, 1982. DOI: 10.1177/002199838201600406.
  • V. Deshpande, S.A. Chowdhury, O. Myers, and S. Li, High-fidelity analytical modeling of asymmetric CFRP composites using Reissner–mindlin theory and hygroscopic degradation, Compos. Sci. Technol., vol. 236, pp. 109983, 5 2023. DOI: 10.1016/j.compscitech.2023.109983.
  • A.P. Vassilopoulos, and T. Keller, Fatigue of Fiber-Reinforced Composites. Springer, London, 2011.
  • H. Mao, and S. Mahadevan, Fatigue damage modelling of composite materials, Compos. Struct., vol. 58, no. 4, pp. 405–410, 2002. DOI: 10.1016/S0263-8223(02)00126-5.
  • C. Nelon, O. Myers, and A. Hall, The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: a review, J. Compos. Mater., vol. 56, no. 9, pp. 1417–1452, 2 2022. DOI: 10.1177/00219983211037048.
  • Z. Hashin, and A. Rotem, A fatigue failure criterion for fiber reinforced materials, J. Compos. Mater., vol. 7, no. 4, pp. 448–464, 1973. DOI: 10.1177/002199837300700404.
  • J. Degrieck, and W.V. Paepegem, Fatigue damage modeling of fibre-reinforced composite materials: review, Appl. Mech. Rev., vol. 54, pp. 279–300, 2001.
  • F. Ellyin, and H. El-Kadi, A fatigue failure criterion for fiber reinforced composite laminae, Compos. Struct., vol. 15, no. 1, pp. 61–74, 1990. DOI: 10.1016/0263-8223(90)90081-O.
  • Z. Fawaz, and F. Ellyin, Fatigue failure model for fibre-reinforced materials under general loading conditions, J. Compos. Mater., vol. 28, no. 15, pp. 1432–1451, 1994. DOI: 10.1177/002199839402801503.
  • W. Hwang, and K. Han, Fatigue of composites—fatigue modulus concept and life prediction, J. Compos. Mater., vol. 20, no. 2, pp. 154–165, 1986. DOI: 10.1177/002199838602000203.
  • H.A. Whitworth, Cumulative damage in composites, J. Eng. Mater. Technol., vol. 112, no. 3, pp. 358–361, 7 1990. DOI: 10.1115/1.2903338.
  • F. Wu, and W. Yao, A fatigue damage model of composite materials, Int. J. Fatigue., vol. 32, no. 1, pp. 134–138, 2010. DOI: 10.1016/j.ijfatigue.2009.02.027.
  • S. Shiri, M. Yazdani, and M. Pourgol-Mohammad, A fatigue damage accumulation model based on stiffness degradation of composite materials, Mater. Des., vol. 88, pp. 1290–1295, 2015. DOI: 10.1016/j.matdes.2015.09.114.
  • H.W. Bergmann, and R. Prinz, Fatigue life estimation of graphite/epoxy laminates under consideration of delamination growth, Numer. Meth. Eng., vol. 27, no. 2, pp. 323–341, 9 1989. DOI: 10.1002/nme.1620270208.
  • R. Prinz, Damage Rates for Interlaminar Failure of Fatigued CFRP Laminates, Springer Netherlands, Netherlands, pp. 189–194, 1990.
  • R. Aoki, R. Higuchi, and T. Yokozeki, Fatigue simulation for progressive damage in CFRP laminates using intra-laminar and inter-laminar fatigue damage models, Int. J. Fatigue., vol. 143, pp. 106015, 2 2021. DOI: 10.1016/j.ijfatigue.2020.106015.
  • S.A. Khan, S.S.R. Koloor, W.K. Jye, G. Siebert, and M.N. Tamin, A fatigue model to predict interlaminar damage of FRP composite laminates subjected to mode I load, Polymers (Basel)., vol. 15, no. 3, pp. 527, 1 2023. DOI: 10.3390/polym15030527.
  • G. Kemmann, and O. Myers, An experimental investigation of combined symmetric-asymmetric composite laminates, J. Compos. Sci., vol. 3, no. 3, pp. 71, 2019. DOI: 10.3390/jcs3030071.
  • S. Phatak, O.J. Myers, S. Li, and G. Fadel, Defining relationships between geometry and behavior of bistable composite laminates, J. Compos. Mater., vol. 55, no. 22, pp. 3049–3059, 2021. DOI: 10.1177/00219983211005824.
  • H. Yu Meng, S. Jie Zhang, Y. Zhou, Y. Qing Hou, Q. Feng Chen, and J. Jun Jia, Snap behaviors of bistable unsymmetric cross-ply composite cylindrical shells with different thicknesses, Mech. Adv. Mater. Struct., vol. 29, no. 27, pp. 6557–6566, 2022. DOI: 10.1080/15376494.2021.1980929.
  • K. Yang, C. Li, and Y. Jiang, A bistable pure carbon fiber composite symmetric laminate and its preparation, modeling, and experimental verification, Mech. Adv. Mater. Struct., pp. 1–12, 2023. DOI: 10.1080/15376494.2023.2264847.
  • F. Mattioni, P. Weaver, and M. Friswell, Multistable composite plates with piecewise variation of lay-up in the planform, Int. J. Solids Struct., vol. 46, no. 1, pp. 151–164, 2009. DOI: 10.1016/j.ijsolstr.2008.08.023.
  • A. Algmuni, F. Xi, and H. Alighanbari, Design and analysis of a grid patch multi-stable composite, Compos. Struct., vol. 246, pp. 112378, 2020. DOI: 10.1016/j.compstruct.2020.112378.
  • F. Dai, H. Li, and S. Du, A multi-stable lattice structure and its snap-through behavior among multiple states, Compos. Struct., vol. 97, pp. 56–63, 2013. DOI: 10.1016/j.compstruct.2012.10.016.
  • K. Potter, Measurements of the snap-through behaviour and snap-through fatigue performance of bistable unsymmetric composite structures, 3rd International Conference on Composite Testing and Model Identification, Porto, Portugal, no. 4, pp. 1-2, 2003.
  • S.A. Chowdhury, S. Li, and O.J. Myers, Fatigue analysis of bistable composite laminate, ASME 2022 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, pp. 1–10, American Society of Mechanical Engineers, Dearborn, Michigan, pp. 1–10, 2022. DOI: 10.1115/SMASIS2022-90215.
  • S.A. Chowdhury, S. Li, and O.J. Myers, Fatigue performance evaluation of bistable composites at different combinations of loading and environmental conditions, J. Compos. Mater., vol. 57, no. 27, pp. 4275–4289, 2023. DOI: 10.1177/00219983231207156.
  • H.R. Ashrafi, M. Jalal, and K. Garmsiri, Prediction of load–displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network, Expert Syst. Appl., vol. 37, no. 12, pp. 7663–7668, 2010. DOI: 10.1016/j.eswa.2010.04.076.
  • M.M. Kantor, V.V. Sudin, and K.A. Solntsev, Analysis of the relationship between the load-displacement curve and characteristics of fracture of low-alloy steel by neural networks, Inorg. Mater. Appl. Res., vol. 11, no. 4, pp. 893–902, 2020. DOI: 10.1134/S2075113320040176.
  • M.-K. Kazi, F. Eljack, and E. Mahdi, Predictive ann models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves, Compos. Struct., vol. 254, pp. 112885, 2020. DOI: 10.1016/j.compstruct.2020.112885.
  • S. Mouloodi, H. Rahmanpanah, C. Burvill, and H.M. Davies, Prediction of load in a long bone using an artificial neural network prediction algorithm, J. Mech. Behav. Biomed. Mater., vol. 102, pp. 103527–103527, 2020. DOI: 10.1016/j.jmbbm.2019.103527.
  • H. Rahmanpanah, S. Mouloodi, C. Burvill, S. Gohari, and H.M. Davies, Prediction of load-displacement curve in a complex structure using artificial neural networks: a study on a long bone, Int. J. Eng. Sci., vol. 154, pp. 103319, 2020. DOI: 10.1016/j.ijengsci.2020.103319.
  • S. Zambal, C. Eitzinger, M. Clarke, J. Klintworth, and P.-Y. Mechin, A digital twin for composite parts manufacturing: effects of defects analysis based on manufacturing data, 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), Porto, Portugal, pp. 803–808, 2018. DOI: 10.1109/INDIN.2018.8472014.
  • C. González, and J. Fernández-León, A machine learning model to detect flow disturbances during manufacturing of composites by liquid moulding, J. Compos. Sci., vol. 4, no. 2, pp. 71, 6 2020. DOI: 10.3390/jcs4020071.
  • S. Stieber, N. Schröter, A. Schiendorfer, A. Hoffmann, and W. Reif, FlowFrontNet: Improving Carbon Composite Manufacturing with CNNs, Springer, Berlin, Germany, pp. 411–426, 2021.
  • Z. Jiang, L. Gyurova, Z. Zhang, K. Friedrich, and A.K. Schlarb, Neural network based prediction on mechanical and wear properties of short fibers reinforced polyamide composites, Mater. Des., vol. 29, no. 3, pp. 628–637, 2008. DOI: 10.1016/j.matdes.2007.02.008.
  • H.T. Fan, and H. Wang, Predicting the open-hole tensile strength of composite plates based on probabilistic neural network, Appl. Compos. Mater., vol. 21, no. 6, pp. 827–840, 2014. DOI: 10.1007/s10443-014-9387-2.
  • C. Hu, W.N.J. Hau, W. Chen, and Q.-H. Qin, The fabrication of long carbon fiber reinforced polylactic acid composites via fused deposition modelling: experimental analysis and machine learning, J. Compos. Mater., vol. 55, no. 11, pp. 1459–1472, 2021. DOI: 10.1177/0021998320972172.
  • T.-T. Le, Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method, J. Compos. Mater., vol. 55, no. 6, pp. 787–811, 2021. DOI: 10.1177/0021998320953540.
  • W. Sai, G.B. Chai, and N. Srikanth, Fatigue life prediction of glare composites using regression tree ensemble‐based machine learning model, Advcd. Theory and Sims., vol. 3, no. 6, pp. 1–11, 2020. DOI: 10.1002/adts.202000048.
  • F. Aymerich, and M. Serra, Prediction of fatigue strength of composite laminates by means of neural networks, KEM., vol. 144, pp. 231–242, 1997. DOI: 10.4028/www.scientific.net/KEM.144.231.
  • J.A. Lee, D.P. Almond, and B. Harris, Use of neural networks for the prediction of fatigue lives of composite materials, Compos. A Appl. Sci. Manufact., vol. 30, no. 10, pp. 1159–1169, 1999. DOI: 10.1016/S1359-835X(99)00027-5.
  • H.E. Kadi, and Y. Al-Assaf, Prediction of the fatigue life of unidirectional glass fiber/epoxy composite laminae using different neural network paradigms, Compos. Struct., vol. 55, no. 2, pp. 239–246, 2002. DOI: 10.1016/S0263-8223(01)00152-0.
  • H.E. Kadi, and Y. Al-Assaf, Energy-based fatigue life prediction of fiberglass/epoxy composites using modular neural networks, Compos. Struct., vol. 57, no. 1–4, pp. 85–89, 2002. DOI: 10.1016/S0263-8223(02)00071-5.
  • M. Al-Assadi, H.A.E. Kadi, and I.M. Deiab, Using artificial neural networks to predict the fatigue life of different composite materials including the stress ratio effect, Appl. Compos. Mater., vol. 18, no. 4, pp. 297–309, 2011. DOI: 10.1007/s10443-010-9158-7.
  • S.A. Tawfik, D.S. Dancila, and E. Armanios, Planform effects upon the bistable response of cross-ply composite shells, Compos. A Appl. Sci. Manufact., vol. 42, no. 7, pp. 825–833, 2011. DOI: 10.1016/j.compositesa.2011.03.012.
  • F. Mattioni, P.M. Weaver, K.D. Potter, and M.I. Friswell, Analysis of thermally induced multistable composites, Int. J. Solids Struct., vol. 45, no. 2, pp. 657–675, 2008. DOI: 10.1016/j.ijsolstr.2007.08.031.
  • S. Tawfik, X. Tan, S. Ozbay, and E. Armanios, Anticlastic stability modeling for cross-ply composites, J. Compos. Mater., vol. 41, no. 11, pp. 1325–1338, 2007. DOI: 10.1177/0021998306068073.
  • M. Cantera, J. Romera, I. Adarraga, and F. Mujika, Modelling and testing of the snap-through process of bi-stable cross-ply composites, Compos. Struct., vol. 120, pp. 41–52, 2015. DOI: 10.1016/j.compstruct.2014.09.064.
  • M. Kuhn, and K. Johnson, Applied Predictive Modeling, Springer New York, New York, NY, 2013.
  • E. Alpaydın, and F. Bach, Introduction to Machine Learning, 3rd ed., MIT Press, Cambridge, MA, 2014.
  • G. Bonaccorso, Machine Learning Algorithms, 2nd ed., Packt Publishing, Birmingham, 2018.
  • F. Pedregosa, et al., Scikit-learn: machine learning in python, J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
  • J.H. Friedman, Greedy function approximation: a gradient boosting machine, Ann. Statist., vol. 29, no. 5, pp. 1189–1232, 2001. DOI: 10.1214/aos/1013203451.
  • J.H. Friedman, Stochastic gradient boosting, Comput. Stat. Data Anal., vol. 38, no. 4, pp. 367–378, 2002. DOI: 10.1016/S0167-9473(01)00065-2.
  • G.E. Batista, and D.F. Silva, How k-nearest neighbor parameters affect its performance, Argentine Symposium on Artificial Intelligence, Mar Del Plata, Argentina, pp. 1–12, 2009.
  • M. Heath, Scientific Computing: An Introductory Survey, 2nd ed., McGraw Hill, New York, NY, 2002.
  • J. Nocedal, Updating quasi-newton matrices with limited storage, Math. Comp., vol. 35, no. 151, pp. 773–782, 1980. DOI: 10.2307/2006193.
  • A.J. Shepherd, Second-Order Methods for Neural Networks., 1st ed., Springer, Berlin, Germany, 1997.
  • R.C. Schweitzer, and J.B. Morris, A tutorial on neural networks using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithm and molecular descriptors with application to the prediction of dielectric constants through the development of quantitative structure property relationships (qsprs), 1999.
  • D. Byatt, I.D. Coope, and C.J. Price, Effect of limited precision on the BFGS quasi-newton algorithm, ANZIAMJ., vol. 45, pp. 283–285, 2004. DOI: 10.21914/anziamj.v45i0.888.
  • H. Pakdel, and B. Mohammadi, Stiffness degradation of composite laminates due to matrix cracking and induced delamination during tension-tension fatigue, Eng. Fract. Mech., vol. 216, pp. 106489, 7 2019. DOI: 10.1016/j.engfracmech.2019.106489.
  • H.A.E. Kadi, and Y. Al-Assaf, The use of neural networks in the prediction of the fatigue life of different composite materials, 16th International Conference on Composite Materials, ICCM-16, pp. 7–13. International Committee on Composite Materials, Kyoto, Japan, vol. 7, 2007.

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