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

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language 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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20501 - Materials engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Name of the periodical

Polymers

ISSN

2073-4360

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

—

UT code for WoS article

000414913800057

EID of the result in the Scopus database

—