Determination of CNC processing parameters for the best wood surface quality via artificial neural network

The optimum adjustment the CNC (Computer Numerical Control) processing parameters is extremely important, especially in finishing processes such as coating, painting, and varnishing where surface quality is required. This work aimed to determine the CNC processing parameters for the best wood surface quality by ANN (Artificial Neural Network). For this aim, the surface roughness values of intermediate values not used in experimental studies were also estimated and the effects of parameter variables for each wood species were revealed. Surface roughness measurements (Ra) were made according to the DIN 4768 to determine the surface quality of wood materials. The prediction model with the best performance was determined through statistical and graphical comparisons. It has been observed that ANN models achieve quite satisfactory results with acceptable deviations. As a result of ANN analysis, the optimum values of tool diameter, spindle speed and feed rate for spruce wood were determined as 2 mm, 10000 rpm and 5 m/min, respectively. These values for beech wood were determined as 4 mm, 12500 rpm, 5 m/min, respectively. The findings of this study can be effectively applied in the furniture industry to reduce time, energy, and cost for experimental research within the range of experimentation conducted