Investigation of optimal parameters with artificial neural networks in machining of some wood species in cnc router machine

The aim of this study is to determine the optimal machining conditions using Artificial Neural Networks (ANN) according to the parameters of various spindle speed, feed rate, cutting width, wood density and moisture content in machining of some wood species in CNC routing machine. In the study, Beech (Fagus orientalis Lipsky.), Anatolian chestnut (Castenea sativa Mill.), Scots pine (Pinus sylvestris L.) and Spruce (Picea orientalis (L.) Link) woods that grow naturally in Turkey were used. Samples were machined parallel and perpendicular to grain directions with a multipurpose cutter in 4 spindle speeds, 3 feed rates, 3 moisture contents and 3 cutting depths in 3-axis CNC routing machine using Alphacam 2019. ANOVA and Duncan tests were used for evaluating the data. ANN method was used for determining the optimal machining conditions. As a result, the smoothest surfaces in all sections of tree species were observed in high rpm. and low feed rate, low and middle moisture content and low depth of cut. Power consumption was reduced under conditions of low rpm and feed rate, low moisture content and depth of cut. With ANN, the lowest, highest and optimal surface roughness and cutting power values were determined in regard to three-tree species at constant cutting depths and moisture contents based on the machining conditions and the cutting mark lengths.