Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network
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 temper...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-10-01
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| Series: | Polymers |
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| Online Access: | https://www.mdpi.com/2073-4360/9/10/519 |
| _version_ | 1811259948665929728 |
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| author | Ivan Kopal Marta Harničárová Jan Valíček Milena Kušnerová |
| author_facet | Ivan Kopal Marta Harničárová Jan Valíček Milena Kušnerová |
| author_sort | Ivan Kopal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T18:39:11Z |
| format | Article |
| id | doaj.art-f698e67c93fe451c9e896764a6e123da |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-04-12T18:39:11Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-f698e67c93fe451c9e896764a6e123da2022-12-22T03:20:50ZengMDPI AGPolymers2073-43602017-10-0191051910.3390/polym9100519polym9100519Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural NetworkIvan Kopal0Marta Harničárová1Jan Valíček2Milena Kušnerová3Institute of Physics, Faculty of Mining and Geology, Vysoká škola báňská—Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava, Czech RepublicInstitute of Physics, Faculty of Mining and Geology, Vysoká škola báňská—Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava, Czech RepublicInstitute of Physics, Faculty of Mining and Geology, Vysoká škola báňská—Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava, Czech RepublicInstitute of Physics, Faculty of Mining and Geology, Vysoká škola báňská—Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava, Czech RepublicThis 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.https://www.mdpi.com/2073-4360/9/10/519thermoplastic polyurethanesvisco-elastic propertiesdynamic mechanical analysisstiffness-temperature modelartificial neural networks |
| spellingShingle | Ivan Kopal Marta Harničárová Jan Valíček Milena Kušnerová Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network Polymers thermoplastic polyurethanes visco-elastic properties dynamic mechanical analysis stiffness-temperature model artificial neural networks |
| title | Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network |
| title_full | Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network |
| title_fullStr | Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network |
| title_full_unstemmed | Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network |
| title_short | Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network |
| title_sort | modeling the temperature dependence of dynamic mechanical properties and visco elastic behavior of thermoplastic polyurethane using artificial neural network |
| topic | thermoplastic polyurethanes visco-elastic properties dynamic mechanical analysis stiffness-temperature model artificial neural networks |
| url | https://www.mdpi.com/2073-4360/9/10/519 |
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