Evaluación con red neuronal del proceso de corte láser por CO 2 en materiales compuestos de fibra de cabuya

Abstract

The application of Machine Learning today has allowed the development of learning models to solve problems in different fields of industry. This research work focused on relating neural networks (ANN) with the manufacture of composite materials (polyester matrix + fiber cabuya) and CO2 laser cutting machining. The objective is to develop a neural network to evaluate the application of machine learning to predict the surface finish characteristic of the material under study. The established cutting parameters were laser power and cutting speed. The surface finish characteristic to be evaluated was the surface roughness of the cut composite material. The sheet of the constructed composite material was subjected to CO2 laser cutting, which generated a set of 84 specimens. Experimental data was generated by measuring surface roughness through laboratory tests. The programming of the neural network was done with the Scikit-learn package. This is one of the most widely used open source libraries for machine learning available in Python. The results achieved by the prediction of the network based on the experimental data are related to the values predicted by the neural network model (ANN) and the performance of the network was evaluated using statistical metrics. The statistical results obtained were 0.946, 0.139 and 0.301 corresponding to the coefficient of determination (R2), the mean square error (MSE), and the mean absolute error (MAE) respectively. Therefore, it could be concluded that the performance of the developed neural network has a high validity and ability to predict surface roughness.