@article{44383,
   author = {Kopal, Ivan and Labaj, Ivan and Harničárová, Marta and Valíček, Jan and Hrubý, Dušan},
   article_location = {Switzerland},
   article_number = {6},
   keywords = {carbon black filled rubber blends; mechanical properties and characteristics; tensile tests of polymers; stress-strain curves; artificial neural network},
   language = {eng},
   issn = {2073-4360},
   journal = {Polymers},
   title = {Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network},
   volume = {10},
   year = {2018}
}

TY  - JOUR
ID  - 44383
AU  - Kopal, Ivan - Labaj, Ivan - Harničárová, Marta - Valíček, Jan - Hrubý, Dušan
PY  - 2018
TI  - Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network
JF  - Polymers
VL  - 10
IS  - 6
SP  - 1 - 18
EP  - 1 - 18
PB  - MDPI
SN  - 20734360
KW  - carbon black filled rubber blends
KW  - mechanical properties and characteristics
KW  - tensile tests of polymers
KW  - stress-strain curves
KW  - artificial neural network
N2  - "The precise experimental estimation of mechanical properties of rubber blends can be a very costly and time-consuming process. The present work explores the possibilities of increasing its efficiency by using artificial neural networks to study the mechanical behavior of these widely used materials. A multilayer feed-forward back-propagation artificial neural network model, with a strain and the carbon black content as input parameters and stress as an output parameter, has been developed to predict the uniaxial tensile response of vulcanized natural rubber blends with different contents of carbon black in the form of engineering stress-strain curves. A novel procedure has been created for the simulation of the optimized artificial neural network model with input datasets generated by a regression model of an experimental dependence of tensile strain-at-break on the carbon black content in the investigated blends. Errors of the prediction of experimental stress-strain curves, as well as of tensile strain-at-break, tensile stress-at-break and M100 tensile modulus were estimated for all simulated stress-strain curves. The present study demonstrated that the performance of a developed neural network model to predict the stress-strain curves of rubber blends with different contents of carbon black is also exceptionally high in the case of a network that had never learned the input data, which makes it a suitable tool for extensive use in practice."
ER  - 

KOPAL, Ivan, Ivan LABAJ, Marta HARNIČÁROVÁ, Jan VALÍČEK a Dušan HRUBÝ. Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network. \textit{Polymers}. Switzerland: MDPI, 2018, roč.~10, č.~6, s.~1 - 18. ISSN~2073-4360.