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

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 artif...

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Autores principales: Kopal, Ivan, Labaj, Ivan, Vršková, Juliána, Harničárová, Marta, Valíček, Jan, Tozan, Hakan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490080/
https://www.ncbi.nlm.nih.gov/pubmed/37688262
http://dx.doi.org/10.3390/polym15173636
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author Kopal, Ivan
Labaj, Ivan
Vršková, Juliána
Harničárová, Marta
Valíček, Jan
Tozan, Hakan
author_facet Kopal, Ivan
Labaj, Ivan
Vršková, Juliána
Harničárová, Marta
Valíček, Jan
Tozan, Hakan
author_sort Kopal, Ivan
collection PubMed
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.
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spelling pubmed-104900802023-09-09 Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing Kopal, Ivan Labaj, Ivan Vršková, Juliána Harničárová, Marta Valíček, Jan Tozan, Hakan Polymers (Basel) Article 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. MDPI 2023-09-02 /pmc/articles/PMC10490080/ /pubmed/37688262 http://dx.doi.org/10.3390/polym15173636 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kopal, Ivan
Labaj, Ivan
Vršková, Juliána
Harničárová, Marta
Valíček, Jan
Tozan, Hakan
Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing
title Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing
title_full Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing
title_fullStr Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing
title_full_unstemmed Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing
title_short Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing
title_sort intelligent modelling of the real dynamic viscosity of rubber blends using parallel computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490080/
https://www.ncbi.nlm.nih.gov/pubmed/37688262
http://dx.doi.org/10.3390/polym15173636
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