Analysis of volumetric swelling and shrinkage of heat treated woods: Experimental and artificial neural network modeling approach
Keywords:Dimensional stability, heat treatment, hygroscopicity, neural network, swelling, wood properties.
AbstractShrinkage 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 ºC) 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.
Almeida, G.; Hernandez, R.E. 2006. Changes in physical properties of tropical and temperate hardwoods below and above the fiber saturation point. Wood Science and Technology 40:599-613.
Avramidis, S.; Iliadis, L. 2005. Predicting wood thermal conductivity using artificial neural networks. Wood and Fiber Science 37:682-690.
Avunduk, E.; Tumac, D.; Atalay, A.K. 2014. Prediction of roadheader performance by artificial neural network. Tunnelling and Underground Space Technology 44:3-9.
Bal, B.C. 2015. Physical properties of beech wood thermally modified in hot oil and in hot air at various temperatures. Maderas. Ciencia y tecnología 17(4):789-798.
Bardak, S.; Tiryaki, S.; Nemli, G.; Aydin, A. 2016. Investigation and neural network prediction of wood bonding quality based on pressing conditions. International Journal of Adhesion and Adhesives 68:115-123.
Bas, D.; Boyaci, I.H. 2007. Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering 78:836-845.
Baysal, E.; Kart, S.; Toker H.; Degirmentepe, S. 2014. Some physical characteristics of thermally modified Oriental-beech wood. Maderas. Ciencia y tecnología 16(3):291-298.
Camille, A.I.; Kmeid, Z. 2005. Advanced wood engineering: glulam beams. Construction and Building Materials 19:99-106.
Ceylan, I. 2008. Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Drying Technology 26:1469-1476.
de Moura, L.F.; Brito, J.O.; Nolasco, A.M.; Uliana, L.R. 2011. Effect of thermal rectification on machinability of Eucalyptus grandis and Pinus caribaea var. hondurensis woods. European Journal of Wood and Wood Products 69:641-648.
Eckelman, C. 1998. The shrinking and swelling of wood and its effect on furniture. FNR 163, Purdue University, Cooperative Extension Service, West Lafayette.
Esteban, L.G.; Fernandez, F.G.; de Palacios, P. 2011. Prediction of plywood bonding quality using an artificial neural network. Holzforschung (65)2:209-214.
Esteves, B.; Marques, A.V.; Domingos, I.; Pereira, H. 2007. Influence of steam heating on the properties of pine (Pinus pinaster) and eucalypt (Eucalyptus globulus) wood. Wood Science and Technology 41:193-207.
Feist, W.C.; Sell, J. 1987. Weathering behavior of dimensionally stabilized wood treated by heating under pressure of nitrogen gas. Wood and Fiber Science 19(2):183-195.
Gryc, V.; Vavrčík, H.; Horáček, P. 2007. Variability in swelling of spruce (Picea abies [L.] Karst.) wood with the presence of compression wood. Journal of Forest Science 53(6):243-252.
Guler, C.; Copur, Y.; Akgul, M.; Buyuksari, U. 2007. Some chemical, physical and mechanical properties of juvenile wood from black pine (Pinus nigra Arnold) plantations. Journal of Applied Sciences 7(5):755-758.
Gunduz, G.; Korkut, S.; Korkut, D.S. 2008. The effects of heat treatment on physical and technological properties and surface roughness of Camiyanı Black pine (Pinus nigra Arn. subsp. pallasiana var. pallasiana) wood. Bioresource Technology 99:2275-2280.
Hagan, M.T.; Demuth, H.B.; Jesus, O.D. 2002. An introduction to the use of neural networks in control systems. International Journal of Robust and Nonlinear Control 12(11): 959-985.
Haghbakhsh, R.; Adib, H.; Keshavarz, P.; Koolivand, M.; Keshtkari, S. 2013. Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, hightemperature conditions. Thermochimica Acta 551:124-130.
Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. Second Edition. Prentice Hall.
Haykin, S. 2008. Neural Computing, 2nd ed. Prentice Hall, Princeton.
Hiziroglu, S. 2004. Dimensional changes in wood. NREM-5009, Oklahoma State University, Division of Agricultural Sciences and Natural Resources, Stillwater.
Inoue, M.; Norimoto, M.; Tanahashi, M.; Rowell, R.M. 1993. Steam or heat fixation of compressed wood. Wood and Fiber Science 25:224-235.
Kord, B.; Kialashaki, A.; Kord, B. 2010. The within-tree variation in wood density and shrinkage, and their relationship in Populus euramericana. Turkish Journal of Agriculture and Forestry 34:121-126.
Korkut, S.; Budakci, M. 2010. The effects of hıgh-temperature heat-treatment on physıcal propertıes and surface roughness of rowan (Sorbus aucuparia l.) wood. Wood Research 55(1):67-78.
Korkut, S.; Aytin, A. 2015. Evaluation of physical and mechanical properties of Wild cherry wood heat-treated using the Thermowood process. Maderas. Ciencia y tecnología 17(1):171-178.
Lewis, C.D. 1982. International and business forecasting methods. Butterworths, London.
May, R.J.; Maier, H.R.; Dandy, G.C. 2010. Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Networks 23(2):283-294.
Okan, O.T.; Deniz, I.; Tiryaki, S. 2015. Application of artificial neural networks for predicting tensile index and brightness in bleaching pulp. Maderas. Ciencia y tecnología 17(3):571-584.
Ozsahin, S. 2012. The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board. BioResources 7:1053-1067.
Ozsahin, S. 2013. Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis. European Journal of Wood and Wood Products 71:769-777.
Paul, W.; Ohlmeyer, M.; Leithoff, H. 2007. Thermal modification of OSB-strands by a onestep heat pretreatment: influence of temperature on weight loss, hygroscopicity and improved fungal resistance. Holz als Roh- und Werkstoff 65:57-63.
Rastislav, S.; Miroslav, M.; Stanislav, K.; Richard, L.; Vladimír, V. 2006. A simple method for determination of kinetics of radial, tangential and surface swelling of wood. Drvna industrija 57 (2):75-82.
Rojas, G.; Ortiz, O. 2010. Identification of knotty core in Pinus radiata logs from computed tomography images using artificial neural network. Maderas. Ciencia y tecnología 12(3):229-239.
Schneid, E.; de Cademartori, P.H.G.; Gatto, D. 2014. The effect of thermal treatment on physical and mechanical properties of Luehea divaricata hardwood. Maderas. Ciencia y tecnología 16(4):413-422.
Shi, J.L.; Kocaefe, D.; Zhang, J. 2007. Mechanical behaviour of quebec wood species heattreated using thermowood process. Holz als Roh- und Werkstoff 65:255-259.
Stam, A.J. 1964. Wood and cellulose science. The Ronald Press Company, New York.
Taylor, R. 1990. Interpretation of the correlation coefficient: a basic review. Journal of Diagnostic Medical Sonography 6:35-39.
Tiryaki, S.; Malkocoglu, A.; Ozsahin, S. 2014. Using artificial neural networks for modeling surface roughness of wood in machining process. Construction and Building Materials 66:329-335.
Tiryaki, S.; Aydin, A. 2014. An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Construction and Building Materials 62:102-108.
Tiryaki, S.; Hamzacebi, C. 2014. Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks. Measurement 49:266-274.
TS 4083. 1983. Wood-determination of radial and tangential shrinkage. Turkish Standards Institution, Ankara.
TS 4084. 1983. Wood-determination of radial and tangential swelling. Turkish Standards Institution, Ankara.
Unsal, O.; Korkut, S.; Atik, C. 2003. The effect of heat treatment on some properties and colour in Eucalyptus (Eucalyptus camaldulensis Dehn.) wood. Maderas. Ciencia y tecnología 5(2):145-152.
Usta, I.; Guray, A. 2000. Comparison of swelling and shrinkage characteristics of Corcisan Pine (Pinus nigra var. mantima). Turkish Journal of Agriculture and Forestry 24:461-464.
Yildiz, S.; Gezer, E.D.; Yildiz, U.C. 2006. Mechanical and chemical behavior of spruce wood modified by heat. Building and Environment 41(12):1762-1766.
Zhang, D.; Sun, L.; Cao, J. 2006. Modeling of temperature-humidity for wood drying based on time-delay neural network. Journal of Forestry Research 17(2):141-144.
Zhang, G.; Ptuwo, B.E.; Hu, M.Y. 1998. Forecasting with ANN: the state of the art. International Journal of Forecasting 14:35-62.