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.

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

Institute of Physics Faculty of Mining and Geology Vysoká škola báňská Technical University of Ostrava 17 Listopadu 15 708 33 Ostrava Czech Republic

Regional Materials Science and Technology Centre Vysoká škola báňská Technical University of Ostrava 17 Listopadu 15 708 33 Ostrava Czech Republic

Technical Faculty Slovak University of Agriculture in Nitra Tr A Hlinku 2 949 76 Nitra Slovakia

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