A modeling study to evaluate the quality of wood surface

Abstract

The goal of this study was to develop a model to predict sanding conditions of different type of materials such as Lebnon cedar (Cedrus libani) and European Black pine (Pinus nigra). Specimens were prepared using different values of grit size, cutting speed, feed rate, and sanding direction. Surface quality values of specimens were measured employing a laser-based robotic measurement system and stylus type measurement equipment. Full factorial design based Analysis of Variance was applied to determine the effective factors. These factors were used to develop the Artificial Neural Networks models for two different measurement systems. The MATLAB Neural Network Toolbox was used to predict the Artificial Neural Networks models. According to the results, the Artificial Neural Networks models were performed using Mean Absolute Percentage Error and R-square values. Mean Absolute Percentage Error values for laser and stylus equipment were found as 2,405 % and 3,766 %, respectively. R-square values were determined as 96,2% and 92,7 % for laser and stylus measurement equipment, respectively. These results showed that the proposed models can be successfully used to predict the surface roughness values.