Abstract
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.
Highlights
Natural rubber (NR) is an elastomeric polymer, widely used in preparation of rubber products
The artificial neural network (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 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
Summary
Natural rubber (NR) is an elastomeric polymer, widely used in preparation of rubber products. Studies dedicated to prediction of products of rubber vulcanization, mostly, neglected the reversion phenomenon, whereas the induction period and the main vulcanization kinetics were analysed separately [4,5,6,7,8]. The first step is neuron activation, where it is computed as the weighted sum of its inputs, and the second step, where the activation is transformed to response by using a transfer function. If input is denoted as xi and weight as wi, the activation computation is the sum of wixi, and the output is obtained by applying a transfer function f [9]. Functions, which domains are defined by real numbers, can be used as transfer functions, where the most common are linear, logarithmic sigmoid and hyperbolic tangent functions
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