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.
Highlights
Thermoplastic polyurethanes (TPUs) belong to the family of thermoplastic elastomers, which combine mechanical properties of rubber based materials with the good processability and recyclability of thermoplastics
This work aimed at an investigation of temperature dependencies of the observed visco-elastic parameters E0 (T), E00 (T), and of tan δ(T) of the investigated TPU at a given constant frequency of its dynamic mechanical loading, three multi-layer feed-forward artificial neural network models were created utilizing the error back-propagation learning algorithm with an architecture consisting of one input, one hidden and one output layer [28]
The α, βand γ-transitions occur as a direct result of the three phase morphology, in particular crystalline hard segments phase, crystalline soft segments phase, and amorphous soft segments phase of TPU
Summary
Thermoplastic polyurethanes (TPUs) belong to the family of thermoplastic elastomers, which combine mechanical properties of rubber based materials with the good processability and recyclability of thermoplastics. They represent linear segmented semi-crystalline multi-block copolymers, composed of an alternation of short stiff hard crystalline segments and rather long soft amorphous flexible chains. The domain structure formed by micro-phase separation of TPUs, due to the thermodynamic immiscibility or incompatibility between the hard and soft phase, endows these polymeric materials with their elastomeric properties, such that can be seen in cross-linked rubber networks [1,2]. TPUs are of great importance from an industrial and from an academic point of view [3]
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