Prediction of the Effect of CO2 Laser Cutting Conditions on Spruce Wood Cut Characteristics Using an Artificial Neural Network

Abstract

In addition to traditional chip methods, performance lasers are often used in the field of wood processing. When cutting wood with CO2 lasers, it is primarily the area of optimization of parameters that is important, which include mainly laser performance and cutting speed. They have a significant impact on the production efficiency and cut quality. The article deals with the use of an artificial neural network (ANN) to predict spruce wood cut characteristics using CO2 lasers under several conditions. The mutual impact of the laser performance (P) and the number of annual circles (AR) for prediction of the characteristics of the cutting kerf and the heat affected zone (HAZ) were examined. For this purpose, the artificial neural network in Statistica 12 software was used. The predicted parameters can be used to qualitatively characterize the cutting kerf properties of the spruce wood cut by CO2 lasers. All the predictions are in good agreement with the results from the available literary sources. The laser power P = 200 W provides a good cutting quality in terms of cutting kerf widths ratio defined as the ratio of cutting kerf width at the lower board to the cutting kerf width at upper board and, therefore, they are optimal for cutting spruce wood at 1.210−2 ms−1.