Prediction of surface roughness at the machining of densified wood surfaces

In this research, modeling and interpretation of the effects of the machining parameters on surface roughness (Rz) using artificial neural networks (ANN) and analysis methods (MLP, LVQ and Taguchi Design analysis) werw studied. Black poplar (Populus nigra) was selected as the experimental material. Specimens, which were transversal densified by a thermo-mechanical (TM) process at 0%, 20% and 40% compression ratios. The densified wood was processed at 1000, 1500 and 2000 mm/min feed speeds and in 12,000, 15,000, and 18,000 rpm rotation speeds in a CNC machine by using two different cutters. Rz surface-roughness values were measured. The modeling of Rz with artificial neural networks (ANN) is discussed. While 97% success was achieved in the classification made with multilayer perceptron (MLP) in estimating Rz, this success rate is 100% in the classification made with learning vector quantization (LVQ). MLP and LVQ are operations in the MATLAB R2019b software. Taguchi values were compared with the experimental data results, it was seen that the MSE value was 4.6%, the MAE value was 1.8%, and the RMSE value was 2.1%. These results showed that the presented models can accurately determine the appropriate parameters for the desired Rz. The prediction results and presented models can be applied for targeted industrial applications.