DOI: 10.5937/jaes0-30826
This is an open access article distributed under the CC BY 4.0
Volume 20 article 914 pages: 145-149
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.
This work was supported by the Instituto Tecnológico Metropolitano de Medellín (Colombia), under the research line of Advanced Computing and Digital Design (CADD) and Mathematical Modelling, and Programming and Optimization Applied to Engineering, which belongs to the research group of Advanced Materials and Energy (MATyER). It was also supported by Universidad Surcolombiana, and Institución Universitaria Pascual Bravo.
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