Prediction of optimum CNC cutting conditions using artificial neural network models for the best wood surface quality, low energy consumption, and time savings

This study aimed to predict the CNC cutting conditions for best wood surface quality, energy, and time savings using artificial neural network (ANN) models. In process, walnut, ash were used as materials, while three different tool diameters (3 mm, 6 8 mm), spindle speed (12000 rpm, 15000 18000 rpm), feed rate m/min, 9 m/min) determined conditions. After processes completed with machine, energy consumption processing all groups. Surface roughness wettability tests performed on processed samples, their qualities determined. The experimentally obtained data analysed in ANN, models performance obtained. By these prediction models, optimum Using findings of study, condition values can be walnut smoothest wettable surface. Furthermore, such minimum shorter