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A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends
In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures is proposed for the first time The carbon black contents in the rubber blend and cure temperature w...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880289/ https://www.ncbi.nlm.nih.gov/pubmed/35215566 http://dx.doi.org/10.3390/polym14040653 |
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author | Kopal, Ivan Labaj, Ivan Vršková, Juliána Harničárová, Marta Valíček, Jan Ondrušová, Darina Krmela, Jan Palková, Zuzana |
author_facet | Kopal, Ivan Labaj, Ivan Vršková, Juliána Harničárová, Marta Valíček, Jan Ondrušová, Darina Krmela, Jan Palková, Zuzana |
author_sort | Kopal, Ivan |
collection | PubMed |
description | In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures is proposed for the first time The carbon black contents in the rubber blend and cure temperature were used as input parameters, while the minimum and maximum elastic torque, scorch time, and optimal cure time, obtained from the analysis of 11 rheological cure curves registered at 10 various temperatures, were considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data and the training algorithm is described. Less than 55% of the experimental data were used to significantly reduce the total number of input and target data points needed for training the model. Satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, was found. It is concluded that the generalized regression neural network is a powerful tool for intelligently modelling the curing process of rubber blends even in the case of a small dataset, and it can find a wide range of practical applications in the rubber industry. |
format | Online Article Text |
id | pubmed-8880289 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88802892022-02-26 A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends Kopal, Ivan Labaj, Ivan Vršková, Juliána Harničárová, Marta Valíček, Jan Ondrušová, Darina Krmela, Jan Palková, Zuzana Polymers (Basel) Article In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures is proposed for the first time The carbon black contents in the rubber blend and cure temperature were used as input parameters, while the minimum and maximum elastic torque, scorch time, and optimal cure time, obtained from the analysis of 11 rheological cure curves registered at 10 various temperatures, were considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data and the training algorithm is described. Less than 55% of the experimental data were used to significantly reduce the total number of input and target data points needed for training the model. Satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, was found. It is concluded that the generalized regression neural network is a powerful tool for intelligently modelling the curing process of rubber blends even in the case of a small dataset, and it can find a wide range of practical applications in the rubber industry. MDPI 2022-02-09 /pmc/articles/PMC8880289/ /pubmed/35215566 http://dx.doi.org/10.3390/polym14040653 Text en © 2022 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 Ondrušová, Darina Krmela, Jan Palková, Zuzana A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends |
title | A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends |
title_full | A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends |
title_fullStr | A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends |
title_full_unstemmed | A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends |
title_short | A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends |
title_sort | generalized regression neural network model for predicting the curing characteristics of carbon black-filled rubber blends |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880289/ https://www.ncbi.nlm.nih.gov/pubmed/35215566 http://dx.doi.org/10.3390/polym14040653 |
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