Application for ANN Data Analysis

Published: 8 February 2022| Version 2 | DOI: 10.17632/rfxtdz5pr2.2
Contributors:
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Marta Harničárová,
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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 has been proposed. The carbon black contents in the rubber blend and cure temperature have been 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, have been considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data, as well as the training algorithm has been described. Only a small part of the experimental data has been used in order to significantly reduce the total number of input and target data points needed for training the model. A satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, has been found. It has been concluded that the generalized regression neural network is a very powerful tool for intelligent modelling the curing process of rubber blends even in the case of small dataset, and it can find a wide practical application in the rubber industry.

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Institutions

Slovenska polnohospodarska univerzita, Trencianska Univerzita Alexandra Dubceka v Trencine, Vysoka skola technicka a ekonomicka v Ceskych Budejovicich

Categories

Rubber, Thermal Curing Process, Neural Network

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