Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks

Abstract In this study, MOR and MOE of the heat-treated wood were predicted by artificial neural networks (ANNs). For this purpose, samples were prepared from beech wood ( Fagus orientalis Lipsky.) and spruce wood ( Picea orientalis (L.) Link.). The samples were exposed to heat treatment at varying temperatures (125, 150, 175 and 200 °C) for varying durations (3, 5, 7 and 9 h). According to the results, the mean absolute percentage errors (MAPE) were determined as 0.74%, 1.01% and 1.04% in prediction of MOR values, and 1.14%, 2.21% and 2.13%, in prediction of MOE values for training, validation and testing data sets, respectively. In the prediction of MOR and MOE, values of R 2 were obtained greater than 0.99 for all data sets with the proposed ANN models. The results show that ANN can be used successfully for predicting MOR and MOE of heat-treated wood.

[1]  H. Pereira,et al.  Influence of steam heating on the properties of pine (Pinus pinaster) and eucalypt (Eucalyptus globulus) wood , 2007, Wood Science and Technology.

[2]  Diyar Akay,et al.  Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting , 2009, Expert Syst. Appl..

[3]  W. E. Hillis,et al.  High temperature and chemical effects on wood stability , 1985, Wood Science and Technology.

[4]  Jun Li Shi,et al.  Mechanical behaviour of Québec wood species heat-treated using ThermoWood process , 2007, Holz als Roh- und Werkstoff.

[5]  R. Guyonnet,et al.  Thermal treatment of wood: analysis of the obtained product , 1989, Wood Science and Technology.

[6]  W. E. Hillis,et al.  High temperature and chemical effects on wood stability , 2004, Wood Science and Technology.

[7]  F. Yapıcı,et al.  Prediction of Modulus of Rupture and Modulus of Elasticity of Heat Treated Anatolian Chestnut (Castanea Sativa) Wood by Fuzzy Logic Classifier , 2012 .

[8]  Süleyman Korkut,et al.  The effects of heat treatment on some technological properties in Uludağ fir (Abies bornmuellerinana Mattf.) wood , 2008 .

[9]  L. García Esteban,et al.  Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model , 2008 .

[10]  Eylem Dizman Tomak,et al.  THE EFFECTS OF NATURAL WEATHERING ON THE PROPERTIES OF HEAT-TREATED ALDER WOOD , 2011 .

[11]  S. Poncsák,et al.  Effect of thermal treatment on the chemical composition and mechanical properties of birch and aspen , 2008, BioResources.

[12]  Engin Derya Gezer,et al.  Mechanical and chemical behavior of spruce wood modified by heat , 2006 .

[13]  Francisco García Fernández,et al.  MOE prediction in Abies pinsapo Boiss. timber: Application of an artificial neural network using non-destructive testing , 2009 .

[14]  Şükrü Özşahin,et al.  Optimization of some panel manufacturing parameters for the best bonding strength of plywood , 2013 .

[15]  Philip H. Mitchell,et al.  Irreversible property changes of small loblolly pine specimens heated in air, nitrogen, or oxygen , 1988 .

[16]  Paul A. Fishwick,et al.  Feedforward Neural Nets as Models for Time Series Forecasting , 1993, INFORMS J. Comput..

[17]  Salim Hiziroglu,et al.  Properties of some thermally modified wood species , 2013 .

[18]  F. S. Wong,et al.  Time series forecasting using backpropagation neural networks , 1991, Neurocomputing.

[19]  H. S. Kol.  Characteristics of heat-treated Turkish pine and fir wood after ThermoWood processing. , 2010, Journal of environmental biology.

[20]  Luis García Esteban,et al.  Prediction of plywood bonding quality using an artificial neural network , 2011 .

[21]  J. Bocquet,et al.  Effect of chemical modifications caused by heat treatment on mechanical properties of Grevillea robusta wood , 2008 .

[22]  B. Ek-Olausson,et al.  Investigation of some technical properties of heat-treated wood , 2003 .

[23]  Sandhya Samarasinghe,et al.  Neural Networks for predicting fracture toughness of individual wood samples , 2007 .

[24]  Jun Cao,et al.  ANN-based data fusion for lumber moisture content sensors , 2006 .

[25]  Helena Pereira,et al.  PINE WOOD MODIFICATION BY HEAT TREATMENT IN AIR , 2008 .

[26]  D. Aydemir,et al.  The effects of thermal treatment on the mechanical properties of wild Pear (Pyrus elaeagnifolia Pall.) wood and changes in physical properties , 2009 .

[27]  David Bailey,et al.  Developing neural-network applications , 1990 .

[28]  H. Rusche.  Die thermische Zersetzung von Holz bei Temperaturen bis 200°C—Erste Mitteilung: Festigkeitseigenschaften von trockenem Holz nach thermischer Behandlung , 1973, Holz als Roh- und Werkstoff.

[29]  D. Kocaefe,et al.  Study of the degradation behavior of heat-treated jack pine (Pinus banksiana) under artificial sunlight irradiation , 2012 .

[30]  P. Bekhta,et al.  Effect of High Temperature on the Change in Color, Dimensional Stability and Mechanical Properties of Spruce Wood , 2003 .

[31]  T. Morén,et al.  The potential of colour measurement for strength prediction of thermally treated wood , 2006, Holz als Roh- und Werkstoff.

[32]  Marco Castellani,et al.  Evolutionary Artificial Neural Network Design and Training for wood veneer classification , 2009, Eng. Appl. Artif. Intell..

[33]  A. Pizzi,et al.  Durability of heat-treated wood , 2002, Holz als Roh- und Werkstoff.

[34]  Marzuki Khalid,et al.  DESIGN OF AN INTELLIGENT WOOD SPECIES RECOGNITION SYSTEM , 2008 .

[35]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[36]  S. Korkut,et al.  The effects of heat treatment on some technological properties of Scots pine (Pinus sylvestris L.) wood. , 2008, Bioresource technology.

[37]  Ernestina Menasalvas,et al.  Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model , 2012 .

[38]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .