Abstract
This article discusses the possibility and expediency of modeling the kinetic characteristics of the curing process of polymer composites (using carbon fiber as an example) based on the use of artificial neural networks. Using neural network modeling, the dependence of the kinetic function of the polymer composite on its degree of cure was obtained. The neural network operability is compared with experimental data and classical approximation methods.
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References
Struzziero, G., Teuwen, J.J.E., Skordos, A.A.: Numerical optimisation of thermoset composites manufacturing processes: a review. Compos. A 124, 105499 (2019). https://doi.org/10.1016/j.compositesa.2019.105499
Liy, M., Zhuz, Q., Geubellez, P.H., Tucker, C.L.I.I.I.: Optimal curing for thermoset matrix composites: thermochemical considerations. Polym. Compos. 22(1), 118–131 (2001). https://doi.org/10.1002/pc.10524
Rai, N., Pitchumani, R.: Optimal cure cycles for the fabrication of thermosetting-matrix composites. Polym. Compos. 18(4), 566–581 (1997). https://doi.org/10.1002/pc.10309
Shah, P.H., Halls, V.A., Zheng, J.Q., Batra, R.C.: Optimal cure cycle parameters for minimizing residual stresses in fiber-reinforced polymer composite laminates. J. Compos. Mater. 52(6), 773–792 (2017). https://doi.org/10.1177/0021998317714317
Baronin, G.S., Buznik, V.M., Zavrazhina, C. V., et al.: Thermophysical properties of fluoropolymer composites with cobalt nanoparticles. In: AIP Conference Proceedings. Mechanics, Resource and Diagnostics of Materials and Structures (MRDMS-2017), vol. 1915, p. 040003 (2017). https://doi.org/10.1063/1.5017351
Vafayan, M., Abedini, H., Ghreishy, M.H.R., Beheshty, M.H.: Effect of cure kinetic simulation model on optimized thermal cure cycle for thin-sectioned composite parts. Polym. Compos. 34(7), 1172–1179 (2013). https://doi.org/10.1002/pc.22526
Kumar, K.V., Safiullah, M., Ahmad, A.N.K.: Root cause analysis of heating rate deviations in autoclave curing of CFRP structures. Int. J. Innov. Res. Stud. 2(5), 369–378 (2013)
Poodts, E., Minak, G., Mazzocchetti, L., Giorgini, L.: Fabrication, process simulation and testing of a thick CFRP component using the RTM process. Compos. B 56, 673–680 (2014). https://doi.org/10.1016/j.compositesb.2013.08.088
Himmelblau, D.M.: Applications of artificial neural networks in chemical engineering. Korean J. Chem. Eng. 17(4), 373–392 (2000)
Mishchenko, S.V., Dmitriev, O.S., Shapovalov, A.V.: An automated system for investigation and selection of the optimum conditions of curing of thin-walled composite parts. Chem. Petrol. Eng. 29, 144–148 (1993). https://doi.org/10.1007/BF01149368
Dmitriev, O.S., Zhivenkova, A.A.: Numerical-analytical solution of the nonlinear coefficient inverse heat conduction problem. J. Eng. Phys. Thermophys. 91(6), 1353–1364 (2018). https://doi.org/10.1007/s10891-018-1869-x
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Dmitriev, O., Barsukov, A. (2021). Neural Network Modeling of the Kinetic Characteristics of Polymer Composites Curing Process. In: Dolinina, O., et al. Recent Research in Control Engineering and Decision Making. ICIT 2020. Studies in Systems, Decision and Control, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-030-65283-8_16
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DOI: https://doi.org/10.1007/978-3-030-65283-8_16
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