KOPAL, Ivan, Juliána VRŠKOVÁ, Darina ONDRUŠOVÁ, Marta HARNIČÁROVÁ, Jan VALÍČEK and Zuzana KOLENIČOVÁ. Modeling the thermal decomposition of friction composite systems based on yarn reinforced polymer matrices using artificial neural networks. Material Iwiss. Weinheim: Verlag GmbH, 2019, vol. 50, No 5, p. 616-628. ISSN 0933-5137. |
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@article{48881, author = {Kopal, Ivan and Vršková, Juliána and Ondrušová, Darina and Harničárová, Marta and Valíček, Jan and Koleničová, Zuzana}, article_location = {Weinheim}, article_number = {5}, keywords = {Friction composites; thermal decomposition; thermogravimetry; artificial neural network modelling; polymers}, language = {eng}, issn = {0933-5137}, journal = {Material Iwiss}, title = {Modeling the thermal decomposition of friction composite systems based on yarn reinforced polymer matrices using artificial neural networks}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mawe.201800178}, volume = {50}, year = {2019} }
TY - JOUR ID - 48881 AU - Kopal, Ivan - Vršková, Juliána - Ondrušová, Darina - Harničárová, Marta - Valíček, Jan - Koleničová, Zuzana PY - 2019 TI - Modeling the thermal decomposition of friction composite systems based on yarn reinforced polymer matrices using artificial neural networks JF - Material Iwiss VL - 50 IS - 5 SP - 616-628 EP - 616-628 PB - Verlag GmbH SN - 09335137 KW - Friction composites KW - thermal decomposition KW - thermogravimetry KW - artificial neural network modelling KW - polymers UR - https://onlinelibrary.wiley.com/doi/abs/10.1002/mawe.201800178 L2 - https://onlinelibrary.wiley.com/doi/abs/10.1002/mawe.201800178 N2 - The presented work deals with the application of artificial neural networks in the modelling of the thermal decomposition process of friction composite systems based on polymer matrices reinforced by yarns. The thermal decomposition of the automotive clutch friction composite system consisting of a polymer blend reinforced by yarns from organic, inorganic and metallic fibres impregnated with resin, as well as its individual components, was monitored by a method of non-isothermal thermogravimetry over a wide temperature range. A supervised eedforward back-propagation multi-layer artificial neural network model, with temperature as the only input parameter, has been developed to predict the thermogravimetric curves of weight loss and time derivative of weight loss of studied friction composite system and its individual components acquired at a fixed constant heating rate under a pure dry nitrogen atmosphere at a constant flow rate. It has been proven that an optimized model with a 1-25-6 architecture of an artificial neural network trained by a Levenberg-Marquardt algorithm is able to predict simultaneously all the analyzed experimental thermogravimetric curves with a high level of reliability and that it thus represents the highly effective artificial intelligence tool for the modelling of thermal stability also of relatively complicated friction composite systems. ER -
KOPAL, Ivan, Juliána VRŠKOVÁ, Darina ONDRUŠOVÁ, Marta HARNIČÁROVÁ, Jan VALÍČEK and Zuzana KOLENIČOVÁ. Modeling the thermal decomposition of friction composite systems based on yarn reinforced polymer matrices using artificial neural networks. \textit{Material Iwiss}. Weinheim: Verlag GmbH, 2019, vol.~50, No~5, p.~616-628. ISSN~0933-5137.
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