Employing artificial neural networks for minimizing surface roughness and power consumption in abrasive machining of wood

One of the biggest challenges in machining processes of wood is to detect the optimum values of process parameters for reducing the final production cost. In the present study, the effects of various process parameters on surface roughness and power consumption in abrasive machining process of wood using experimental data collected from the literature were modeled by artificial neural networks (ANNs). The results have indicated that accurate prediction of the experimental data by neural network models was achieved with the mean absolute percentage error (MAPE) less than 2.51 % for power consumption and 2.65 % for surface roughness in the testing phase. Besides, the values of determination coefficient (R²) were found as 0.994 and 0.985 in the prediction of surface roughness and power consumption by the ANN modeling, respectively. Based on the results, it can be said that by means of the proposed models the surface roughness and power consumption can easily be predicted with very high degrees of accuracy in abrasive machining process of wood. Consequently, the present study can effectively be applied to the wood industry to reduce the time, energy consumption and high experimental costs because it eliminates the need for a large number of experiments.