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    ANALYSIS OF VOLUMETRIC SWELLING AND SHRINKAGE OF HEAT TREATED WOODS: EXPERIMENTAL AND ARTIFICIAL NEURAL NETWORK MODELING APPROACH
    (Univ Bio-Bio, 2016) Tiryaki, Sebahattin; Bardak, Selahattin; Aydin, Aytac; Nemli, Gokay
    Shrinkage and swelling characteristics of wood as a hygroscopic material affect negatively its effective utilization for a variety of applications. Heat treatment is widely used for minimizing the negative effects of volumetric swelling and shrinkage of wood. The present study aims to develop artificial neural network (ANN) models for predicting volumetric swelling and shrinkage of heat treated woods. For this purpose, wood samples were subjected to heat treatment at varying temperatures (130, 150, 170 and 190 degrees C) for varying durations (2, 4, 6 and 8 h). Experimental results have showed that volumetric swelling and shrinkage of wood decreased by heat treatment. Then, neural networks models capable of predicting the swelling and shrinkage of the treated woods were developed based on the resulting data. It was seen that ANN models allowed volumetric swelling and shrinkage of such woods to predict successfully with a limited set of experimental data. This approach was able to predict volumetric swelling and shrinkage of wood with a mean absolute percentage error equal to 2,599% and 2,647% in test phase, respectively. The developed models might thus serve as a robust tool to predict volumetric swelling and shrinkage with less number of experiments.
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    INFLUENCE OF RESIDUE TYPE ON QUALITY PROPERTIES OF PARTICLEBOARD MANUFACTURED FROM FAST-GROWN TREE OF HEAVEN (AILANTHUS ALTISSIMA (MILL.) SWINGLE)
    (Inst Technol Drewna, 2019) Bardak, Selahattin; Nemli, Gokay; Tiryaki, Sebahattin
    In this study, the effect of residue types (soundwood, branchwood and bark) on the quality properties of particleboards made from the tree of heaven (Ailanthus altissima (Mill.) Swingle) was investigated. For this purpose, the soundwood, branchwood and bark mixed at different ratios were used in the production of particleboards. Modulus of rupture (MOR), modulus of elasticity (MOE), internal bond strength (IB), thickness swelling (TS) and formaldehyde emission (FE) of the specimens were then tested. The chemical and anatomical properties of the residue types were also determined. Residue type was found to have an impact on the properties of particleboards. The addition of bark and branchwood improved the thickness swelling (2 h immersion) and formaldehyde emission. However, use of branchwood negatively affected the thickness swelling for 24 h immersion. Based on the findings of this study, it can be concluded that different parts of Ailanthus altissima (Mill.) Swingle can be used to manufacture particleboard panels. The branchwood and bark contents significantly affected the quality properties of the manufactured particleboards. The chemical and anatomical properties of the branchwood and bark were also found to be parameters influencing the quality properties of the particleboards. The results indicate that the contents of added bark and branchwood should not exceed 10% and 20% respectively.
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    Investigation and neural network prediction of wood bonding quality based on pressing conditions
    (Elsevier Sci Ltd, 2016) Bardak, Selahattin; Tiryaki, Sebahattin; Nemli, Gokay; Aydin, Aytac
    This paper presents an application of artificial neural network (ANN) to predict the bonding strength of the wood joints pressed under different conditions. An experimental investigation firstly was carried out and then an ANN model was developed based on the experimental data. In the experimental investigation, Oriental beech (Fagus orientalis L) and Oriental spruce (Picea orientalis (L.) Link.) samples bonded with polyvinyl acetate (PVAc) adhesive were pressed at four different temperatures (20, 40, 60 and 80 degrees C) for four different durations (2, 8, 14 and 20 min). The experimental results showed that higher values of bonding strength were obtained when high temperatures were combined with short pressing duration. Similar findings could be also obtained with longer pressing time for lower temperatures. The first case may be recommended to increase the efficiency of the production process, allowing a greater quantity of production per unit time. The ANN results showed a good agreement with the experimental results. It was shown that prediction error was within acceptable limits. The results revealed that the developed ANN model is capable of giving adequate prediction for bonding strength with an acceptable accuracy level. The desired outputs of bonding strength can be thus obtained by conducting less number of time-consuming and costly experimental investigations using the proposed model. (C) 2016 Elsevier Ltd. All rights reserved.
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    Modeling of Wood Bonding Strength Based on Soaking Temperature and Soaking Time by means of Artificial Neural Networks
    (Ismail SARITAS, 2016) Tiryaki, Sebahattin; Bardak, Selahattin; Aydin, Aytaç
    Adhesive bonding of wood enablessufficient strength and durability to hold wood pieces together and thusproduce high quality wood products. However, it is well known that manyvariables have an important influence on the strength of an adhesive bonding.The objective of the present paper is to predict the bonding strength of spruce(Picea orientalis (L.) Link.) andbeech (Fagus orientalis Lipsky.) woodjoints subjected to soaking by using artificial neural networks. To obtain thedata for modeling, beech and spruce samples were subjected to the soaking atdifferent temperatures for different periods of time. In the ANN analysis, 70%of the total experimental data were used to train the network, 15% was used totest the validation of the network, and remaining 15% was used to test theperformance of the trained and validated network. A three-layer feedforwardback propagation artificial neural network trained by Levenberg–Marquardtlearning algorithm was found as the optimum network architecture for theprediction of the bonding strength of soaked wood samples. This architecturecould predict wood bonding strength with an acceptable level of the error.Consequently, modeling results demonstrated that artificial neural networks arean efficient and useful modeling tool to predict the bonding strength of woodsamples subjected to the soaking for different temperatures and durations.
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    Performance evaluation of multiple adaptive regression splines, teaching-learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood
    (Springer, 2019) Tiryaki, Sebahattin; Tan, Hueseyin; Bardak, Selahattin; Kankal, Murat; Nacar, Sinan; Peker, Hueseyin
    Understanding the mechanical behaviour of impregnated wood is crucial in making a preliminary decision on the usability of such woods for structural purposes. In this paper, by considering concentration (1, 3 and 5%), pressure (1, 1.5 and 2atm.), and time (30, 60, 90 and 120min), an experimental study was performed, and the mechanical behaviour of impregnated wood was determined as a result of the experimental process. Multiple adaptive regression splines (MARS), teaching-learning based optimization (TLBO) algorithms and conventional regression analysis (CRA) were applied to different regression functions by using experimentally obtained data. The functions were checked against each other to detect the best equation for each parameter and to assess performances of MARS, TLBO and CRA methods in the prediction of mechanical properties. The experimental results showed that higher values of mechanical properties were obtained when lower concentration, pressure and time were chosen. Overall, all the functions successfully predicted the mechanical properties. However, the MARS and TLBO provided better accuracy in predicting the mechanical properties. The modeling results indicated that the MARS and TLBO are promising new methods in predicting the mechanical properties of impregnated wood. With the use of these methods, the mechanical behavior of impregnated wood could be determined with high levels of accuracy. Thus, the proposed methods may facilitate a preliminary decision concerning the usability of such woods for areas where the mechanical properties are important. Finally, the employment of MARS and TLBO algorithms by practitioners in the wood industry is encouraged and recommended for future studies.
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    THE INFLUENCE OF RAW MATERIAL GROWTH REGION, ANATOMICAL STRUCTURE AND CHEMICAL COMPOSITION OF WOOD ON THE QUALITY PROPERTIES OF PARTICLEBOARDS
    (Univ Bio-Bio, 2017) Bardak, Selahattin; Nemli, Gokay; Tiryaki, Sebahattin
    In the present study, the impact of raw material grown region on the physical, mechanical, surface properties and formaldehyde emission of the particleboard was investigated. Ailanthus altissima wood grown in Trabzon had longer fiber length and thicker fiber and trachea cell wall than those of the wood grown in Artvin. The highest amounts of lignin, ash, condensed tannin and solubility values were found in wood grown in Artvin. Ailanthus altissima wood grown in Trabzon had higher amounts of cellulose and hemicellulose than those of the wood grown in Artvin. Particleboards made from wood grown in Artvin had worse surface quality and mechanical strength properties than those of panels made from wood grown in Trabzon. On the other hand, the results showed that particleboards produced from wood grown in Artvin had lower thickness swelling and formaldehyde emision values than those of the panels produced from wood grown in Trabzon.