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

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.

The effect of EUV modification of WC-Co indexable knives on the tool life during particleboard milling

The effect of EUV modification of WC-Co indexable knives on the tool life during particleboard milling. The paper presents the results of lifetime tests of WC-Co composite blades after modification of their flank surfaces using intense pulses of extreme ultraviolet (EUV). Wear tests consisting in milling a three-layer particleboard with constant cutting parameters were carried out on a CNC machining center. A statistically insignificant increase in the lifetime of modified tools was demonstrated, as well as an inversely proportional relationship with the duration of the EUV pulses. The EUV modification increased the coefficient of variation in the lifetime of WC-Co indexable knives.