Evaluación con red neuronal del proceso de desgaste abrasivo de placas de un material compuesto de látex con partículas de caucho reciclado

Abstract

In this thesis, a Machine Learning approach was investigated in the field of manufacturing new materials for industry. In particular, artificial neural networks were used to predict the Taber wear index (TDI) of latex plates and recycled rubber particles. In recent years, the application of Artificial Intelligence and in particular Machine Learning to scientific disciplines has increased substantially. The purpose was to evaluate how machine learning works, in particular neural networks, and how it should be applied to make a prediction. The preliminary phase of the work was to create the experimentally obtained data set necessary for the secondary phase, which includes the analysis and modeling of neural networks. The generation of the data set involved the manufacture of the material and wear tests based on the ISO 9352 standard. In the context of the neural network, the Google TensorFlow software was used through the Python3 interface. The model developed allows to predict the IDT of the plate taking as independent variables; the volumetric percentage of material, the rotational speed, applied load and the number of cycles. The performance of the network will be evaluated through statistical tests such as the mean square error (MSE), the mean absolute error (MAE) and the coefficient of determination (R2).