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Modeling beech (Fagus orientalis) particleboard properties based on resin content and board density

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Abstract

The aim of this study was to modeling beech (Fagus orientalis) particleboard properties based on UF resin content and board density. Board density at three levels (520, 620 and 720 kg/m3) and resin content (6, 7 and 8 %) were compared. Stepwise multivariate-linear regression models were used to evaluate the influence of the above parameters on board properties and to determine the most effective factor. Regression equations indicated that both parameters were included in the models of shear strength, and thickness swelling after 24 h immersion based on the degree of importance. The model of modulus of rupture only had one step and board density positively affected it. Board density and resin content had not significant effect on modulus of elasticity. The results obtained from contour plots revealed that manufacturing beech particleboards with density ranging from 570 to 620 kg/m3 and 6 % resin would result in boards with properties within those required by corresponding standards.

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References

  • ANSI (2009) Particleboard. American national standard. ANSI A208.1-2009. National Composite Association, Lesburg, VA

  • Arabi M, Faezipour M, Layeghi M, Enayati AA (2011) Interaction analysis between slenderness ratio and resin content on mechanical properties of particleboard. J For Res 22(3):461–464

    Article  Google Scholar 

  • Ashori A, Nourbakhsh A (2008) Effect of press cycle and particleboard made from the underutilized low-quality raw materials. Ind Crop Prod 28(2):225–230

    Article  CAS  Google Scholar 

  • ASTM 1037 (1996) Standard method for the preparation of extractive free wood. Designation D1105-84. Annual book of ASTM standards, Vol 04-01 wood

  • Baharoğlu M, Nemli G, Sarı B, Bardak S, Ayrılmıs N (2012) The influence of moisture content of raw material on the physical and mechanical properties, surface roughness, wettability, and formaldehyde emission of particleboard composite. Compos Part B 42(3):2448–2451

    Article  Google Scholar 

  • Bowyer JL, Shmulsky R, Haygreen JG (2007) Forest products and wood science: an introduction. Wiley, London

    Google Scholar 

  • Cook DF, Chiu CC (1997) Predicting the internal bond strength of particleboard utilizing a radial basis function neural network. Eng Appl Artif Intell 10(2):171–177

    Article  Google Scholar 

  • Dias FM, Nascimento MF, Martinez-Espinos M, Lah FAR, Domenico Valarelli IDD (2005) Relation between the compaction rate and physical and mechanical properties of particleboard. J. Mater Res 8(3):329–333

    Article  Google Scholar 

  • Doosthoseini K (2008) Wood composite materials, manufacturing, applications. Tehran University Press, Iran, 1:647. (in Persian)

  • EN 310 (1993) Wood based panel. Department of modulus of elasticity in bending and bending strength. European Committee for Standardization, Brussels

    Google Scholar 

  • EN 312 (2003) Particleboards-specifications. European Committee for Standardization, Brussels

    Google Scholar 

  • EN 317 (1993) Particleboard and fiberboards. Determination of swelling in thickness after immersion in water. European Committee for Standardization, Brussels

    Google Scholar 

  • EN 323 (1999) Wood based panels. Determination of the density. European Committee for Standardization, Brussels

  • EN 326 (1993) Wood based panels. Sampling, cutting and inspection. Sampling and cutting of test pieces and expression of test results. European Committee for Standardization, Brussels

    Google Scholar 

  • Enayati A, Eslah F, Farhid E (2013) Evaluation of particleboard properties using multivariate regression equations based on structural factors. J Agr Sci Technol 15:1405–1413

    Google Scholar 

  • Eslah F, Enayaty AA, Tajvidi M, Faezipour MM (2012) Regression models for the prediction of poplar particleboard properties based on urea formaldehyde resin content and board density. J Agr Sci Technol 14(6):1321–1329

    CAS  Google Scholar 

  • Garcia Fernandez F, García Esteban L, De Palacios P, Navarro M, Conde M (2008) Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model. For Syst 17(2):178–187

    Google Scholar 

  • Hayashi K, Ohmi M, Tominaga H, Fukada K (2003) Effect of board density on bending properties and dimensional stabilities of MDF-reinforced corrugated particleboard. J Wood Sci Technol 49:398–404

    Article  Google Scholar 

  • Hesch R (1993) Correlations among density, resin content and quality criteria in homogeneous boards of bagasse. Euro J Wood Wood Prod 51(5):312–318

    Article  CAS  Google Scholar 

  • Hiziroglu S, Jarusombuti S, Fuengvivat V (2004) Surface characteristics of wood composites manufactured in Thailand. Build Environ 39(11):1359–1364

    Article  Google Scholar 

  • Kalaycioglu H, Deniz I, Hiziroglu S (2005) Some of the properties of particleboard from paulownia. Wood Sci Technol 51(4):410–414

    Article  CAS  Google Scholar 

  • Kalogirou S, Eftekhari M, Marjanovic L (2003) Predicting the pressure coefficients in a naturally ventilated test room using artificial neural network. J. Build and Environ 38:399–407

    Article  Google Scholar 

  • Kim S (2009) Environment-friendly adhesives for surface bonding of wood-based flooring using natural tannin to reduce formaldehyde and TVOC emission. J Bio-Resour Technol 100:744–748

    Article  CAS  Google Scholar 

  • Malinov S, Sha W, Meckeown JJ (2001) Modeling the correlation between processing parameters and properties in titanium alloys using artificial neural network. J Comput Mater Sci 21:375–394

    Article  CAS  Google Scholar 

  • Maloney TM (1977) Modern particleboard and dry-process fiberboard manufacturing. Calif (USA), Miller Freeman Pub, San Francisco, p 672

    Google Scholar 

  • Maloney TM (1993) Modern particleboard and dry-process fiberboard manufacturing. Miller Freeman Publications, California

  • Nemli G, Ozturk I, Aydin I (2005) Some of the parameters influencing surface roughness of particleboard. Build Environ 40(10):1337–1340

    Article  Google Scholar 

  • Wang SY, Chen TY, Fann JD (1999) Comparison of internal bond strength and compression shear strength of wood-based materials. J Wood Sci 45:396–401

    Article  Google Scholar 

  • Zhou D (1990) A study of oriented strand board made from hybrid poplar. Holz als Roh-und Werkstoff 48:293–296

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Authors and Affiliations

  1. Department of Wood and Paper Science and Technology, Faculty of Natural Resources, University of Tehran, Karaj, Islamic Republic of Iran

    A. A. Enayati & F. Eslah

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  1. A. A. Enayati
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  2. F. Eslah
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Correspondence to F. Eslah.

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Enayati, A.A., Eslah, F. Modeling beech (Fagus orientalis) particleboard properties based on resin content and board density. J Indian Acad Wood Sci 11, 45–49 (2014). https://doi.org/10.1007/s13196-014-0116-0

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  • DOI: https://doi.org/10.1007/s13196-014-0116-0

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