Heat treatment changes some physical, mechanical, and chemical properties of wood. Inorganic borates have been used as wood preservatives for many years. The aim of this study was to investigate the effects of impregnation chemicals on some mechanical properties (bending strength (MOR), modulus of elasticity (MOE), tensile strength parallel to the grain (TS), compression strength parallel to the grain (CS), and shear strength parallel to the grain (SS)) of heat-treated oak (Quercus petraea Liebl.). For this purpose, the oak wood specimens were impregnated with 5% aqueous solution of boric acid (BA) and borax (BX). Then specimens were heat-treated at 160, 190, and 220 °C for 2 and 4 h. According to the results of the study, borax retention value was higher than boric acid. The bending strength, modulus of elasticity in bending, tensile strength parallel to the grain, and shear strength parallel to the grain decreased due to heat treatment. The highest mechanical strength losses were determined in samples heat treated at 220 °C for 4 h. Generally the mechanical strength losses of samples impregnated with borax were lower than non-impregnated controls and specimens impregnated with boric acid.

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Kurtoğlu, Ahmet (Dogus Author)Determining wood machining properties and defining convenient usage areas for native wood species is important for evaluating surface quality. Wood machining is a performance criterion indicated after planing, shaping, turning, mortising, boring, and sanding. This study selected 2 softwood species (European black pine and cedar of Lebanon) as well as 2 hardwood species (sessile oak and black poplar), which are commonly used and grown in Turkey. Preparation of samples and machining were carried out according to the 2004 ASTM D 1666 standards for determining machining properties. Sessile oak (Quercus petraea) had an excellent performance in all machining processes. Black poplar (Populus nigra L.) yielded the lowest results for the sanding test. A perfect surface quality was obtained with a feed rate of 8.6 m/min for hardwoods at a 25° cutting angle in planing, and at a 15° angle for softwoods

An artificial neural network (ANN) approach was employed for the prediction and control of surface roughness (Ra and Rz) in a computer numerical control (CNC) machine. Experiments were performed on a CNC machine to obtain data used for the training and testing of an ANN. Experimental studies were conducted, and a model based on the experimental results was set up. Five machining parameters (cutter type, tool clearance strategy, spindle speed, feed rate, and depth of cut) were used. One hidden layer was used for all models, while there were five neurons in the hidden layer of the Ra and Rz models. The RMSE values were calculated as 1.05 and 3.70. The mean absolute percentage error (MAPE) values were calculated as 20.18 and 15.14, which can be considered as a good prediction. The results of the ANN approach were compared with the measured values. It was shown that the ANN prediction model obtained is a useful and effective tool for modeling the Ra and Rz of wood. The results of the present research can be applied in the wood machining industry to reduce energy, time, and cost.

In this paper, the optimization of computer numerical control (CNC) machining parameters were conducted using the Taguchi design method on the surface quality of massive wooden edge glued panels (EGP) made of European larch (Larix decidua Mill). Three machining parameters and their effects on surface roughness were evaluated. These parameters included tool clearance strategy, spindle speed, and feed rate. An analysis of variance (ANOVA) was performed to identify the significant factors affecting the surface roughness (Ra and Rz). Optimum machining parameter combinations were acquired by conducting an analysis of the signal-to-noise (S/N) ratio. Optimal cutting performance for the Ra and Rz was obtained for the cutter at a tool clearance strategy of an offset 16000 rpm spindle speed and 1000 mm/min feed rate. The surface roughness, both the Ra and Rz, increased with increasing feed rate. Optimal cutting performance for Ra and Rz was obtained for a tool clearance strategy of an offset 16000 rpm spindle speed, and 1000 mm/min feed rate cutting settings. Based on the confirmation tests, Ra decreased 2.2 times and Rz 1.8 times compared to the starting cutting parameters.