Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing

Author:

Kopal Ivan1ORCID,Labaj Ivan1ORCID,Vršková Juliána1ORCID,Harničárová Marta23ORCID,Valíček Jan23,Tozan Hakan4

Affiliation:

1. Department of Numerical Methods and Computational Modeling, Faculty of Industrial Technologies in Púchov, Alexander Dubček University of Trenčín, Ivana Krasku 491/30, 020 01 Púchov, Slovakia

2. Department of Electrical Engineering, Automation and Informatics, Faculty of Engineering, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 949 76 Nitra, Slovakia

3. Department of Mechanical Engineering, Faculty of Technology, Institute of Technology and Business in České Budějovice, Okružní 10, 370 01 České Budějovice, Czech Republic

4. College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait

Abstract

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.

Funder

Operational Programme Integrated Infrastructure—project CEDITEK II.

Science Grant Agency

Publisher

MDPI AG

Subject

Polymers and Plastics,General Chemistry

Reference46 articles.

1. Mark, J.E., Erman, B., and Roland, C.M. (2013). The Science and Technology of Rubber, Elsevier. [4th ed.].

2. Dick, J.S. (2003). Basic Rubber Testing: Selecting Methods for a Rubber Test Program, ASTM International. [1st ed.].

3. Gupta, B.R. (2022). Rheology Applied in Polymer Processing, CRC Press. [1st ed.].

4. Wilczynski, K. (2020). Rheology in Polymer Processing: Modeling and Simulation, Hanser Publishers. [1st ed.].

5. (2019). Standard Test Method for Rubber—Measurement of Unvulcanized Rheological Properties Using Rotorless Shear Rheometers (Standard No. ASTM D6204−15).

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