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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10490080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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