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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F17%3A10237387" target="_blank" >RIV/61989100:27350/17:10237387 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27360/17:10237387 RIV/61989100:27740/17:10237387

Výsledek na webu

<a href="http://www.mdpi.com/2073-4360/9/10/519" target="_blank" >http://www.mdpi.com/2073-4360/9/10/519</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/polym9100519" target="_blank" >10.3390/polym9100519</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Polymers

ISSN

2073-4360

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

17

Strana od-do

—

Kód UT WoS článku

000414913800057

EID výsledku v databázi Scopus

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