The presented work deals with the creation of a new radial basis function artificial neural network-based model of dynamic thermo-mechanical response and damping behavior of thermoplastic elastomers in the whole temperature interval of their entire lifetime and a wide frequency range of dynamic mechanical loading. The created model is based on experimental results of dynamic mechanical analysis of the widely used thermoplastic polyurethane, which is one of the typical representatives of thermoplastic elastomers. Verification and testing of the well-trained radial basis function neural network for temperature and frequency dependence of dynamic storage modulus, loss modulus, as well as loss tangent prediction showed excellent correspondence between experimental and modeled data, including all relaxation events observed in the polymeric material under study throughout the monitored temperature and frequency interval. The radial basis function artificial neural network has been confirmed to be an exceptionally high-performance artificial intelligence tool of soft computing for the effective predicting of short-term viscoelastic behavior of thermoplastic elastomer systems based on experimental results of dynamic mechanical analysis.

Alexander Dubček University of Trenčín Faculty of Industrial Technologies in Púchov Ivana Krasku 491 30 020 01 Púchov Slovakia

Institute of Technology and Business in České Budějovice Faculty of Technology Department of Mechanical Engineering Okružní 10 370 01 České Budějovice Czech Republic

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

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