Optimization of wood machining parameters using artificial neural network in CNC router

This study aims to determine the optimal CNC (Computer Numerical Control) machining conditions using an artificial neural network. For this purpose, Fagus orientalis, Castanea sativa, Pinus sylvestris, and Picea orientalis wood samples at 8%, 12%, and 15% moisture content (MC) were machined on a CNC router in both across and along the grain directions. Based on the experimental data of surface roughness and cutting power analyses, a total of 16 models were used. These were selected in hundreds of models that have the lowest error. The spindle speed, feed rate, and the number of cutter teeth were chosen to be different with the literature based on the length of cutter mark. As a result, optimum machining parameters were determined for each wood MC.

Automated summary

This study aims to determine the optimal CNC machining conditions using an artificial neural network. Wood samples of Fagus orientalis, Castanea sativa, Pinus sylvestris, and Picea orientalis at different moisture content (MC) were machined on a CNC router in both across and along the grain directions. Based on the experimental data of surface roughness and cutting power analyses, 16 models were selected out of hundreds of models with the lowest error to determine the optimum machining parameters for each wood MC.