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.
Prediction of rubber vulcanization using an artificial neural network
