PREDICTION OF ENERGY CONSUMPTION IN THE LEADWELL V-40 IT CNC MACHINING CENTER THROUGH ARTIFICIAL NEURAL NETWORKS

Miguel Angel Rodriguez Cabal; Juan Gonzalo Ardila Marín; Juan Sebastian Rudas Florez

doi:10.5937/jaes0-30826

Miguel Angel Rodriguez Cabal Instituto Tecnológico Metropolitano, Faculty of Engineering, Medellín, Colombia
Juan Gonzalo Ardila Marín Universidad Surcolombiana, Faculty of Engineering, Neiva, Colombia
Sebastián Rudas Institución Universitaria Pascual Bravo, Faculty of Engineering, Medellin, Colombia

DOI: https://doi.org/10.5937/jaes0-30826

Keywords: neural networks, CNC, energy management, manufacture industry

Abstract

Energy consumption in machining processes has become a problem for today's manufacturing industry. The use of neural networks and optimization algorithms for modeling and prediction of consumption as a function of the cut-off parameters in processes of this type has aroused the interest of research groups. The present work used artificial neural networks (ANN) to predict the energy consumption of a Leadwell V-40iT® five-axis CNC machining center, based on experimental data obtained through a factorial experimental design 5³. ANN was programed in Matlab®. From the study was concluded that the depth per pass (Ap) is the variable that has the most influence on the prediction model of energy consumption with a 77% of relative importance, while the feed rate is the least relevant with 9% of importance.

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