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In the present study, two mathematical models were developed to optimize the surface roughness for machining condition of Cedar of Lebanon pine (Cedrus libani). Taguchi approach was applied to examine the effect of CNC processing variables. Quality characteristics parameters were selected as arithmetic average roughness (R a) and average maximum height of the profile (R z) for wood material. Analysis of variance (ANO-VA) was used to determine effective machining parameters. Developed mathematical models using response surface methodology (RSM) were optimized by a combined approach of the Taguchi's L 27 orthogonal array based simulated angling algorithm (SA). Optimum machining levels for determining the minimum surface roughness values were carried out three stages. Firstly, the desirability function was used to optimize the mathematical models. Secondly, the results obtained from the desirability function were selected as the initial point for the simulated angling algorithm. Finally, the optimum parameter values were obtained by using simulated angling algorithm. Minimum R a value was obtained spindle speed of 17377 rpm, feed rate of 2,012 m/min, tool radius of 8 mm and depth of cut of 2,009 mm by using desirability function based simulated angling algorithm. For R z these results were found as 16980 rpm, 2,004 m/min, 8,001 mm and 2,003 mm. The R-square values of the R a and R z were 95,91 % and 96,12 %, respectively. The proposed models obtained the minimum surface roughness values and provided better results than the observed values.

The main objective of this work is to develop a mathematical model to evaluate optimum sanding conditions of Europen black pine (Pinus nigra). Samples were sanded using different of grit size, feed rate, cutting speed and depth of cut. Average surface roughness (R a ) values of each type of specimens were measured employing a stylus type of equipment. Interaction between sanding parameters and surface roughness of the species were analyzed using Minitab software and response surface methodology. Based on the fi ndings in the work feed rate, cutting speed, grit size and depth of cut values of 5,39 m/min, 19,75 m/sec, 220 (grit size) and 9 mm were determined as optimum sanding conditions.

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.