Prediction of rubber vulcanization using an artificial neural network
Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commerci...
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Association of Chemical Engineers of Serbia
2021
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oai:doaj.org-article:38f278ba3e994f6ab0af514def5b9e202021-11-10T07:28:21ZPrediction of rubber vulcanization using an artificial neural network0367-598X2217-742610.2298/HEMIND210511026Lhttps://doaj.org/article/38f278ba3e994f6ab0af514def5b9e202021-01-01T00:00:00Zhttp://www.doiserbia.nb.rs/img/doi/0367-598X/2021/0367-598X2105277L.pdfhttps://doaj.org/toc/0367-598Xhttps://doaj.org/toc/2217-7426Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.Lubura Jelena D.Kojić PredragPavličević JelenaIkonić BojanaOmorjan RadovanBera OskarAssociation of Chemical Engineers of Serbiaarticlerubber curingmachine learningrubber rheological propertiesChemical technologyTP1-1185ENSRHemijska Industrija , Vol 75, Iss 5, Pp 277-283 (2021) |
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DOAJ |
| collection |
DOAJ |
| language |
EN SR |
| topic |
rubber curing machine learning rubber rheological properties Chemical technology TP1-1185 |
| spellingShingle |
rubber curing machine learning rubber rheological properties Chemical technology TP1-1185 Lubura Jelena D. Kojić Predrag Pavličević Jelena Ikonić Bojana Omorjan Radovan Bera Oskar Prediction of rubber vulcanization using an artificial neural network |
| description |
Determination of rubber rheological properties is indispensable in order to
conduct efficient vulcanization process in rubber industry. The main goal of
this study was development of an advanced artificial neural network (ANN)
for quick and accurate vulcanization data prediction of commercially
available rubber gum for tire production. The ANN was developed by using the
platform for large-scale machine learning TensorFlow with the Sequential
Keras-Dense layer model, in a Python framework. The ANN was trained and
validated on previously determined experimental data of torque on time at
five different temperatures, in the range from 140 to 180 oC, with a step of
10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to
minimize error, where the ANN model with Softplus showed the most accurate
predictions. Numbers of neurons and layers were varied, where the ANN with
two layers and 20 neurons in each layer showed the most valid results. The
proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to
predict the torque dependence on time for two test temperatures (150 and 170
oC). The obtained solutions were confirmed as accurate predictions, showing
the mean absolute percentage error (MAPE) and mean squared error (MSE)
values were less than 1.99 % and 0.032 dN2 m2, respectively. |
| format |
article |
| author |
Lubura Jelena D. Kojić Predrag Pavličević Jelena Ikonić Bojana Omorjan Radovan Bera Oskar |
| author_facet |
Lubura Jelena D. Kojić Predrag Pavličević Jelena Ikonić Bojana Omorjan Radovan Bera Oskar |
| author_sort |
Lubura Jelena D. |
| title |
Prediction of rubber vulcanization using an artificial neural network |
| title_short |
Prediction of rubber vulcanization using an artificial neural network |
| title_full |
Prediction of rubber vulcanization using an artificial neural network |
| title_fullStr |
Prediction of rubber vulcanization using an artificial neural network |
| title_full_unstemmed |
Prediction of rubber vulcanization using an artificial neural network |
| title_sort |
prediction of rubber vulcanization using an artificial neural network |
| publisher |
Association of Chemical Engineers of Serbia |
| publishDate |
2021 |
| url |
https://doaj.org/article/38f278ba3e994f6ab0af514def5b9e20 |
| work_keys_str_mv |
AT luburajelenad predictionofrubbervulcanizationusinganartificialneuralnetwork AT kojicpredrag predictionofrubbervulcanizationusinganartificialneuralnetwork AT pavlicevicjelena predictionofrubbervulcanizationusinganartificialneuralnetwork AT ikonicbojana predictionofrubbervulcanizationusinganartificialneuralnetwork AT omorjanradovan predictionofrubbervulcanizationusinganartificialneuralnetwork AT beraoskar predictionofrubbervulcanizationusinganartificialneuralnetwork |
| _version_ |
1718440458716184576 |