Data for ANN Analysis

Published: 25 October 2021| Version 1 | DOI: 10.17632/rfxtdz5pr2.1
Contributors:
Marta Harničárová,
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Description

In the present work, a new artificial neural network-based model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures has been developed. The variations of 4 curing characteristics, most commonly used in the rubber industry, namely of the minimum and maximum elastic torque, scorch time and optimal cure time, with carbon black contents in the rubber blend and cure temperature, have been obtained on the basis of the analysis of 11 experimental isothermal rheological cure curves registered by an oscillating-disk rheometer at 10 cure temperatures. The computer implementation of the ANN model requires a special pre-processing of the raw experimental data, which is described in detail in the paper. The implementation of ANN model for predicting the curing characteristics of RBs with different contents of CB filler at various cure temperatures was done in the MATLAB® software package, Version 9.0.0.341360 R2016a 64-bit, equipped with a Neural Network Toolbox (Math Works, Natic, MA, USA), that provides a number of built-in tools for sufficiently powerful and user-friendly work with ANNs of a wide range of types and architectures. The GRNN was used to solve the given function approximation problem, in particular for its extremely high learning rate and rapid convergence to optimal regression levels even in the case of sparse data. The satisfactory agreement between the experimental and modelled values has been found for all four curing characteristics, with the maximum error in the prediction for modelled minimum and maximum elastic torque less than 3%, and for modelled scorch time and optimal cure time not exceeding 5% of their experimental values. It can be 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 a small training dataset, and it can find a wide practical application in the area of 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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