Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F23%3A00002613" target="_blank" >RIV/75081431:_____/23:00002613 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2073-4360/15/17/3636" target="_blank" >https://www.mdpi.com/2073-4360/15/17/3636</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing

Original language description

Modelling the flow properties of rubber blends makes it possible to predict their rheological behaviour during the processing and production of rubber-based products. As the nonlinear nature of such complex processes complicates the creation of exact analytical models, it is appropriate to use artificial intelligence tools in this modelling. The present study was implemented to develop a highly efficient artificial neural network model, optimised using a novel training algorithm with fast parallel computing to predict the results of rheological tests of rubber blends performed under different conditions. A series of 120 real dynamic viscosity–time curves, acquired by a rubber process analyser for styrene–butadiene rubber blends with varying carbon black contents vulcanised at different temperatures, were analysed using a Generalised Regression Neural Network. The model was optimised by limiting the fitting error of the training dataset to a pre-specified value of less than 1%. All repeated calculations were made via parallel computing with multiple computer cores, which significantly reduces the total computation time. An excellent agreement between the predicted and measured generalisation data was found, with an error of less than 4.7%, confirming the high generalisation performance of the newly developed model.

Czech name

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Czech description

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Type

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

CEP classification

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OECD FORD branch

20501 - Materials engineering

Project

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Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2023

Confidentiality

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

Name of the periodical

Polymers

ISSN

2073-4360

e-ISSN

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Volume of the periodical

15

Issue of the periodical within the volume

17

Country of publishing house

CH - SWITZERLAND

Number of pages

20

Pages from-to

1-20

UT code for WoS article

001062705500001

EID of the result in the Scopus database

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