Machine learning methods as applied to modelling thermal conductivity of epoxy-based composites with different fillers for aircraft
Abstract
The thermal conductivity coefficient of epoxy composites for aircraft, which are reinforced with glass fiber and filled with aerosil, γ-aminopropylaerosil, aluminum oxide, chromium oxide, respectively, was simulated. To this end, various machine learning methods were used, in particular, neural networks and boosted trees. The results obtained were found to be in good agreement with the experimental data. In particular, the correlation coefficient in the test sample was 0.99%. The prediction error of neural networks in the test samples was 0.5; 0.3; 0.2%, while that of boosted trees was 1.5; 0.9%.
Keyword : epoxy-based composites, fillers, modelling, aircraft, aerospace applications, machine learning
How to Cite
Yasniy, O., Mytnyk, M., Maruschak, P., Mykytyshyn, A., & Didych, I. (2024). Machine learning methods as applied to modelling thermal conductivity of epoxy-based composites with different fillers for aircraft. Aviation, 28(2), 64–71. https://doi.org/10.3846/aviation.2024.21472
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Konovalenko, I., Maruschak, P., Brevus, V., & Prentkovskis, O. (2021). Recognition of scratches and abrasions on metal surfaces using a classifier based on a convolutional neural network. Metals, 11(4), Article 549. https://doi.org/10.3390/met11040549
Liu, B., Vu-Bac, N., Zhuang, X., Fu, X., & Rabczuk, T. (2022). Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites. Composites Science and Technology, 224, Article 109425. https://doi.org/10.1016/j.compscitech.2022.109425
Mohanty, J. R., Verma, B. B., Parhi, D. R. K., & Ray, P. K. (2009). Application of artificial neural network for predicting fatigue crack propagation life of aluminum alloys. Archives of Computational Materials Science and Surface Engineering, 1(3), 133–138.
Monticeli, F. M., Neves, R. M., Ornaghi, H. L., & Almeida, J. H. S. (2022). Prediction of bending properties for 3D-printed carbon fibre/epoxy composites with several processing parameters using ANN and statistical methods. Polymers, 14(17), Article 3668. https://doi.org/10.3390/polym14173668
Mykytyshyn, A. G. (2002). Development of technology and study of parameters of formation of products from epoxy-filled composites, [Dissertation, Lviv Polytechnic National University]. Lviv (in Ukrainian).
Stephen, C., Thekkuden, D. T., Mourad, A. H. I., Shivamurthy, B., Selvam, R., & Rohit Behara, S. (2022). Prediction of impact performance of fiber reinforced polymer composites using finite element analysis and artificial neural network. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44, Article 408. https://doi.org/10.1007/s40430-022-03711-8
Stukhlyak, P. D., Buketov, A.V., Panin, S. V., Maruschak, P. O., Moroz, K. M., Poltaranin, M. A., Vukherer, T., Kornienko, L. A., & Lyukshin, B. A. (2015). Structural fracture scales in shock-loaded epoxy composites. Physical Mesomechanics, 18, 58–74. https://doi.org/10.1134/S1029959915010075
Yasnii, О. P., Pastukh, O. А., Pyndus, Y. І., Lutsyk, N. S., & Didych I. S. (2018). Prediction of the diagrams of fatigue fracture of D16T aluminum alloy by the methods of machine learning. Materials Science, 54, 333–338. https://doi.org/10.1007/s11003-018-0189-9
Yasniy, O., Didych, I., Fedak, S., & Lapusta, Y. (2020). Modeling of AMg6 aluminum alloy jump-like deformation properties by machine learning methods. Procedia Structural Integrity, 28, 1392–1398. https://doi.org/10.1016/j.prostr.2020.10.110
Yasniy, O., Fedak, S., & Lapusta, Y. (2022a). Prediction of jump-like creep using preliminary plastic strain. Procedia Structural Integrity, 36, 166–170. https://doi.org/10.1016/j.prostr.2022.01.019
Yasniy, O., Pasternak, I., & Sobashek, L. (2022b). Modelling of AL-6061 aluminum alloy deformation diagrams by machine learning methods. Procedia Structural Integrity, 42, 1344–1349. https://doi.org/10.1016/j.prostr.2022.12.171
Zhang, L., & Wei, X. (2022) Prediction of fatigue crack growth under variable amplitude loading by artificial neural network-based Lagrange interpolation. Mechanics of Materials, 171, Article 104309. https://doi.org/10.1016/j.mechmat.2022.104309