Document Type : Research Paper

Authors

1 MS.c candidates of wood technology, department of wood science and technology, University of Tehran, Karaj, IRAN

2 professor, Department of wood science and technology, University of Tehran, Karaj, IRAN

3 assistant professor, Department of wood science and technology, University of Tehran, Karaj, IRAN

4 professor, Department of wood science and technology, University of Tehran, Karaj, IRAN.

Abstract

The purpose of this study was to investigate which equation (Linear, Exponential equation and quadratic) can describe exactly the interaction effect of particle size and adhesive percent and predict mechanical properties of particleboard (modulus of rupture, modulus of elasticity and bending strength). For this work three levels of density including 0.65, 0.7, and 0.75 g/cm3 and also, three levels of adhesive content including 8, 9.5, and 11% and four levels of slenderness ratio of particles including 46.35, 33.7, 21.51 and 12.87 were used. After conducting the experiment and preparing the data, three kind of equation (linear, quadratic and Exponential equation) for each mechanical property based on slenderness of particles, density and adhesive percent obtained. The result indicated there was no correlation between mechanical properties of particleboard and quadratic equation but there were good correlations between linear and Exponential equation. Also the result indicated that Exponential equation can describe efficiently the simultaneous effect of slenderness and adhesive present on the mechanical properties of particleboard, and it can predict better mechanical properties than linear equation.

Keywords

Main Subjects

- دوست حسینی، ک.، 1379، فناوری تولید و کاربرد صفحات فشرده چوبی ، انتشارات دانشگاه تهران، 648.
- کارگرفر، ا.، دوست حسینی، ک.، و نوربخش، ا.، 1378. استفاده از مدل­های رگرسیونی برای پیش­بینی ویژگی­های تخته­ خرده چوب . دو فصلنامه علمی- پژوهشی تحقیقات علوم چوب و کاغذ ایران ،23 (10): 11-1
–Andre, N., Hyun, C., Seung, B., and Young, T, (2008). prediction of internal bond strength in a medium density fiberboard process using  multivariate statistical methods and variable selection, Wood Science Journal,42:521- 534.
 
-Cook, D.E,. and Chiu, C. C., (1997) Predicting the Internal Bond Strength of Particleboard, Utilizing a Radial Basis Function Neural Network. Engng Applic. Artif.  lnteU. Vol. 10, No. 2, pp.  171-177.
 
-Fernández, G.F., Esteban L.G., DE Palacios P., Navarro N., Conde M., 2008b. Prediction of standard particleboard mechanical properties utilizing an arti ficial neural network and subsequent comparison with a multivariate regression model. Invest. Agrar. Sist. Rec. For. 17(2), 178-187.
-Groves, CK .)1998(. A new method for measuring resin distribution in OSB. Technical Report, Forintek Canada Corp. pp 58.
-Hoover, W.L. Hunt, M.O. Lattanzi, R.C. Bateman, J.H. Youngquist, J.A.(1992). Modeling Mechanical Properties of Single-Layer, Aligned, Mixed Hardwood Strand Panels.  For. Prod. J., 42, 12-18.
-Lehmann, W.F.(1974). Properties of structural particleboards. For. Prod. J., 24, 19-26.
-Lin, H, C., and Huang J, C,. (2004) Using Single Image Multi-Processing Analysis Techniques to Estimate the Internal Bond Strength of Particleboard. Taiwan J For Sci 19(2): 109-17, 2004.
-Kate, E. S., and Smith, G. D., (2006) Prediction of internal bond strength in particleboard from screw withdrawal resistance models. Wood and Fiber Science, 38(2), 2006, pp. 256 – 267.
-Esteban, L, G., and  (2009) Artificial neural networks in variable process control: application in particleboard manufacture Investigación Agraria: Sistemas y Recursos Forestales 2009 18(1), 92-100.
-Mackerle,J., (2005).  Finite element analyses in wood research: a bibliography Wood Sci Technol 39: 579–600.
-Maloney, T, M., (1977). Modern particleboard and dry process fiberboard manufacturing, Miller Freeman Publications, San Francisco, CA. 672 pp.
 
-Nirdosha, G, Setung, S, (2006). Formulation and process modeling of particleboard production using Hardwood saw mill wastes using experimental desings . Composite Structures 75: 520 – 525.