KOPAL, Ivan, Ivan LABAJ, Juliána VRŠKOVÁ, Marta HARNIČÁROVÁ, Jan VALÍČEK, Darina ONDRUŠOVÁ, Jan KRMELA and Zuzana PALKOVÁ. A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends. Polymers. Basel, Switzerland: MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, vol. 14, No 4, p. nestránkováno, 18 pp. ISSN 2073-4360. 2022.
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Basic information
Original name A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends
Authors KOPAL, Ivan (guarantor), Ivan LABAJ, Juliána VRŠKOVÁ, Marta HARNIČÁROVÁ (203 Czech Republic, belonging to the institution), Jan VALÍČEK (203 Czech Republic, belonging to the institution), Darina ONDRUŠOVÁ, Jan KRMELA and Zuzana PALKOVÁ (703 Slovakia, belonging to the institution).
Edition Polymers, Basel, Switzerland, MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2022, 2073-4360.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 20501 Materials engineering
Country of publisher Switzerland
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/75081431:_____/22:00002337
Organization unit Institute of Technology and Business in České Budějovice
UT WoS 000761440500001
Keywords in English rubber blends; curing process; modelling; generalized regression neural network
Tags KSTR5, RIV22, SCOPUS
Changed by Changed by: Mgr. Nikola Petříková, učo 28324. Changed: 20/3/2023 17:22.
Abstract
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.
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