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Polymers 2017, 9(10), 519; https://doi.org/10.3390/polym9100519

Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network

1
Institute of Physics, Faculty of Mining and Geology, Vysoká škola báňská—Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava, Czech Republic
2
Department of Numerical Methods and Computing Modelling, Faculty of Industrial Technologies in Púchov, Alexander Dubček University of Trenčín, Ivana Krasku 491/30, 020 01 Puchov, Slovakia
3
Regional Materials Science and Technology Centre, Vysoká škola báňská—Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava, Czech Republic
4
Technical Faculty, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 949 76 Nitra, Slovakia
*
Author to whom correspondence should be addressed.
Received: 11 September 2017 / Revised: 12 October 2017 / Accepted: 15 October 2017 / Published: 18 October 2017
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Abstract

This paper presents one of the soft computing methods, specifically the artificial neural network technique, that has been used to model the temperature dependence of dynamic mechanical properties and visco-elastic behavior of widely exploited thermoplastic polyurethane over the wide range of temperatures. It is very complex and commonly a highly non-linear problem with no easy analytical methods to predict them directly and accurately in practice. Variations of the storage modulus, loss modulus, and the damping factor with temperature were obtained from the dynamic mechanical analysis tests across transition temperatures at constant single frequency of dynamic mechanical loading. Based on dynamic mechanical analysis experiments, temperature dependent values of both dynamic moduli and damping factor were calculated by three models of well-trained multi-layer feed-forward back-propagation artificial neural network. The excellent agreement between the modeled and experimental data has been found over the entire investigated temperature interval, including all of the observed relaxation transitions. The multi-layer feed-forward back-propagation artificial neural network has been confirmed to be a very effective artificial intelligence tool for the modeling of dynamic mechanical properties and for the prediction of visco-elastic behavior of tested thermoplastic polyurethane in the whole temperature range of its service life. View Full-Text
Keywords: thermoplastic polyurethanes; visco-elastic properties; dynamic mechanical analysis; stiffness-temperature model; artificial neural networks thermoplastic polyurethanes; visco-elastic properties; dynamic mechanical analysis; stiffness-temperature model; artificial neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kopal, I.; Harničárová, M.; Valíček, J.; Kušnerová, M. Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network. Polymers 2017, 9, 519.

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