Prediction of rheometric properties of compounds by using artificial neural networks
The ability of an Artificial Neural Network (ANN) to evaluate the variability of rheometric properties of rubber compounds from their formulation is presented. Because of the complexity and non-linearity of mixing processes, an exact mathematical treatment of the problem is extremely difficult, or e...
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | JOUR |
| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_00359475_v74_n1_p116_Schwartz |
| Aporte de: |
| id |
todo:paper_00359475_v74_n1_p116_Schwartz |
|---|---|
| record_format |
dspace |
| spelling |
todo:paper_00359475_v74_n1_p116_Schwartz2023-10-03T14:47:34Z Prediction of rheometric properties of compounds by using artificial neural networks Schwartz, G.A. The ability of an Artificial Neural Network (ANN) to evaluate the variability of rheometric properties of rubber compounds from their formulation is presented. Because of the complexity and non-linearity of mixing processes, an exact mathematical treatment of the problem is extremely difficult, or even impossible. The use of artificial neural networks (ANNs) might be very useful to analyze these processes, since they have the ability to map nonlinear relationships without prior information about process or system models. In this work a three-layer ANN is used and the optimum parameters are determined. The results are compared with theoretical and experimental published data. The dependence of the rheometric properties as a function of compound components is also analyzed. Finally, the sensibility matrix concept is introduced. The sensibility matrix allows us to calculate the minimum expected variability, for a given compound, due to the weight tolerances of its components. Fil:Schwartz, G.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00359475_v74_n1_p116_Schwartz |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| description |
The ability of an Artificial Neural Network (ANN) to evaluate the variability of rheometric properties of rubber compounds from their formulation is presented. Because of the complexity and non-linearity of mixing processes, an exact mathematical treatment of the problem is extremely difficult, or even impossible. The use of artificial neural networks (ANNs) might be very useful to analyze these processes, since they have the ability to map nonlinear relationships without prior information about process or system models. In this work a three-layer ANN is used and the optimum parameters are determined. The results are compared with theoretical and experimental published data. The dependence of the rheometric properties as a function of compound components is also analyzed. Finally, the sensibility matrix concept is introduced. The sensibility matrix allows us to calculate the minimum expected variability, for a given compound, due to the weight tolerances of its components. |
| format |
JOUR |
| author |
Schwartz, G.A. |
| spellingShingle |
Schwartz, G.A. Prediction of rheometric properties of compounds by using artificial neural networks |
| author_facet |
Schwartz, G.A. |
| author_sort |
Schwartz, G.A. |
| title |
Prediction of rheometric properties of compounds by using artificial neural networks |
| title_short |
Prediction of rheometric properties of compounds by using artificial neural networks |
| title_full |
Prediction of rheometric properties of compounds by using artificial neural networks |
| title_fullStr |
Prediction of rheometric properties of compounds by using artificial neural networks |
| title_full_unstemmed |
Prediction of rheometric properties of compounds by using artificial neural networks |
| title_sort |
prediction of rheometric properties of compounds by using artificial neural networks |
| url |
http://hdl.handle.net/20.500.12110/paper_00359475_v74_n1_p116_Schwartz |
| work_keys_str_mv |
AT schwartzga predictionofrheometricpropertiesofcompoundsbyusingartificialneuralnetworks |
| _version_ |
1782028231949418496 |