Research data for the study titled Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing
Description
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 non-linear nature of such complex processes complicates the creation of exact analytical models, it is appropriate to use artificial intelligence tools in their 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 optimisation of the model was done by limiting the fitting error of the training dataset to a pre-specified value of less than 1%. All repeated calculations were done via the technique of parallel computing with the use of multiple computer cores, which significantly reduces the total computation time. An excellent agreement between the predicted and measured generalisation data with an error of less than 4.7% was found, confirming a high generalisation performance of the newly developed model. The detailed examination of the data used in the study titled "Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing" is facilitated by the file START.m with the author's comments. The raw experimental data is in the Viscosity.xlsx file, which is stored in the RAWDATA directory. The data prepared for visualization is stored in the Data.mat file.
Files
Steps to reproduce
The START.m user script and the set_plot.m user function are optimized for MATLAB® Version R2016a 64-bit (win64) but will also work with earlier versions.
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Funding
This research work has been supported by the Operational Programme Integrated Infrastructure - project CEDITEK II., ITMS2014+ code 313011W442, the project No. KEGA 003TnUAD-4/2022, and the projects VEGA 1/0691/23 and 1/0236/21.