Skip to main content
SpringerLink
Log in

Prediction of the Bending Strength of a Laminated Veneer Lumber (LVL) Using an Artificial Neural Network

  • Published:
Mechanics of Composite Materials Aims and scope

The application of an artificial neural networks (ANN) to predicting the bending strength of a laminated veneer lumber (LVL) manufactured under different conditions is considered. First, experimental studies were conducted, and then an ANN model was developed based on the experimental data obtained. LVL specimens of walnut wood, glued with urea-formaldehyde resins containing a chemically modified starch and nanocellulose, were obtained by pressing for different times. Experimental results for them showed that the direct effect of the press time, the square effect of the modified starch, and the joint effect of them had the highest statistical significance to the bending strength of LVL. The ANN model developed gave good predictions for the bending strength, well agreeing with experimental data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Similar content being viewed by others

References

  1. G. Nemli, I. Aydın, and E. Zekovic, “Evaluation of some of the properties of particleboard as function of manufacturing parameters,” Mater. Des., 28, No. 4, 1169-1176 (2007).

    CAS  Google Scholar 

  2. J. Petinarakis and P. Kavvouras, “Technological factors affecting the emission of formaldehyde from particleboards,” Wood Res-Slovakia., 51, No. 1, 31- 40 (2006).

    CAS  Google Scholar 

  3. M. Salem, M. Bohm, S. Barcik, and J. Berankova, “Formaldehyde emission from wood-based panels bonded with different formaldehyde-based resins,” Drvna Ind., 62, No. 3, 177-183 (2011).

    Google Scholar 

  4. E. Minopoulou, E. Dessipri, G. D. Chryssikos, V. Gionis, A. Paipetis, and C. Panayiotou, “Use of NIR for structural characterization of urea–formaldehyde resins,” Int. J. Adhes. Adhes., 23, No. 6, 473-484 (2003).

    CAS  Google Scholar 

  5. M. Liu, R. V. K. G. Thirumalai, Y. Wu, and H. Wan, “Characterization of the crystalline regions of cured urea formaldehyde resin,” RSC Adv., 7, No. 78, 49536-49541 (2017).

    CAS  Google Scholar 

  6. W. Lum, S. H. Lee, and P. H’ng, “Effects of formaldehyde catcher on some properties of particleboard with different ratio of surface to core layer,” Asian J. Appl. Sci., 7, No. 1, 22-29 (2014).

    Google Scholar 

  7. B. D. Park and J. W. Kim, “Dynamic mechanical analysis of urea–formaldehyde resin adhesives with different formaldehyde-to-urea molar ratios,” J. Appl. Polym. Sci., 108, No. 3, 2045-2051, (2008).

    CAS  Google Scholar 

  8. Y. Liu, J. Shen, and X. D. Zhu, “Evaluation of mechanical properties and formaldehyde emissions of particleboards with nanomaterial-added melamine-impregnated papers,” Eur. J. Wood Wood Prod., 73, No. 4, 449-55 (2015).

    CAS  Google Scholar 

  9. N. Nikvash, A. Kharazipour, and M. Euring, “Effects of wheat protein as a bio binder in the manufacture of particleboards using a mixture of canola, hemp, bagasse and commercial wood,” Forest Prod. J., 62, No. 1, 49-57 (2012)

    CAS  Google Scholar 

  10. B. Biduski, F. T. d. Silva, W. M. d. Silva, S. L. d. M. E. Halal, V. Z. Pinto, and A. R. G. Dias, “Impact of acid and oxidative modifications, single or dual, of sorghum starch on biodegradable films,” Food Chem., 214, 53-60 (2017).

    CAS  Google Scholar 

  11. J. Liu, B. Wang, L. Lin, J. Zhang, W. Liu, and J. Xie, “Functional, physicochemical properties and structure of crosslinked oxidized maize starch,” Food Hydrocoll., 36, 45-52 (2014).

    CAS  Google Scholar 

  12. B. Wei, H. Li, Y. Tian, X. Xu, and Z. Jin, “Thermal degradation behavior of hypochlorite-oxidized starch nanocrystals under different oxidized levels,” Carbohydr. Polym., 124, 124-130 (2015).

    CAS  Google Scholar 

  13. Y. R. Zhang, X. L. Wang, G. M. Zhao, and Y. Z. Wang, “Preparation and properties of oxidized starch with high degree of oxidation,” Carbohydr. Polym., 87, No. 4, 2554-2562 (2012).

    CAS  Google Scholar 

  14. E. M. Ciannamea, J. F. Martucci, P. M. Stefani, and R. A. Ruseckaite, “Bonding quality of chemically-modified soybean protein concentrate-based adhesives in particleboards from rice husks,” J. Am. Oil. Chem. Soc., 89, No. 9, 1733-1741 (2012).

    CAS  Google Scholar 

  15. R. Widyorini, K. Umemura, A. R. Kusumaningtyas, and T. A. Prayitno, “Effect of starch addition on properties of citric acid-bonded particleboard made from bamboo,” BioResources, 12, No. 4, 8068–8077 (2017).

    CAS  Google Scholar 

  16. P. Zheng, Q. Lin, F. Li, Y. Ou, and N. Chen , “Development and characterization of a defatted soy flour-based bioadhesive crosslinked by 1,2,3,4-butanetetracarboxylic acid,” Int. J. Adhes. Adhes., 78, 148-154 (2017).

    CAS  Google Scholar 

  17. R. Gadhave, P. Mahanwar, and P. Gadekar, “Factor affecting gel time/process-ability of urea formaldehyde resin based wood adhesives,” Open J. Polym. Chem., 7, 33-42 (2017).

    CAS  Google Scholar 

  18. P. S. Garcia, M. V. E. Grossmann, M. A. Shirai, M. M. Lazaretti, F. Yamashita, C. M. O. Muller, and S. Mali, “Improving action of citric acid as compatibiliser in starch/polyester blown films,” Ind. Crops Prod., 52, 305-312 (2014).

    CAS  Google Scholar 

  19. E. Olsson, M. S. Hedenqvist, C. Johansson, and L. Järnström, “Influence of citric acid and curing on moisture sorption, diffusion and permeability of starch films,” Carbohydr. Polym., 94, No. 2, 765-772 (2013).

    CAS  Google Scholar 

  20. S. Bardak, S. Tiryaki, G. Nemli, and A. Aydın, “Investigation and neural network prediction of wood bonding quality based on pressing conditions,” Int. J. Adhes. Adhes., 68, 115-123 (2016).

    CAS  Google Scholar 

  21. C. Demirkir, Ş. Özsahin, I. Aydin, and G. Colakoglu, “Optimization of some panel manufacturing parameters for the best bonding strength of plywood,” Int. J. Adhes Adhes., 46, 14-20 (2013).

    CAS  Google Scholar 

  22. F. G. Fernández, P. de Palacios, L. G. Esteban, A. Garcia-Iruela, B. G. Rodrigo, and E. Menasalvas, “Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model,” Compos. B Eng., 43, No. 8, 3528-3533 (2012).

    Google Scholar 

  23. İ. Akyüz, Ş. Özşahin, S. Tiryaki, and A. Aydın, “An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process,” Clean Technol. Envir., 19, No. 5, 1449-1458 (2017).

    Google Scholar 

  24. European Committee for Standardization (CEN). Wood-based panels. Determination of modulus of elasticity in bending and of bending strength. Standard EN 310. Brussels: CEN, (1993).

  25. K. Manonmani, N. Murugan, and G. Buvanasekaran, “Effects of process parameters on the bead geometry of laser beam butt welded stainless steel sheets,” Int. J. Adv. Manuf. Tech., 32, No. 11, 1125-1133 (2007).

    Google Scholar 

  26. S. Tiryaki and C. Hamzaçebi, “Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks,” Measurement, 49, 266-274 (2014).

    Google Scholar 

  27. Y. Erzin and T. Cetin, “The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions,” Comput. Geosci., 51, 305-313 (2013).

    Google Scholar 

  28. P. C. Williams, in: P. Williams and K. Norris (eds.), Near-Infrared Technology in the Agricultural and Food Industries, Ch. 8, Second ed., American Association of Cereal Chemists, USA, (2001), pp. 145-69.

    Google Scholar 

  29. S. Tiryaki, S. Ozsahin and I. Yildirim, “Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat treated woods,” Int. J. Adhes. Adhes., 55, 29-36 (2014).

    CAS  Google Scholar 

  30. C. D. Lewis, International and business forecasting methods. Butterworths Publishing, London (1982).

    Google Scholar 

  31. H. Wang, J. Liang, J. Zhang, X. Zhou and G. Du, “Performance of urea-formaldehyde adhesive with oxidized cassava starch,” BioResources, 12, 7590-7600 (2017).

    CAS  Google Scholar 

  32. J. Luo, J. Zhang, Q. Gao, A. Mao and J. Li, “Toughening and enhancing melamine–urea–formaldehyde resin properties via in situ polymerization of dialdehyde starch and microphase separation,” Polymers, 11, No. 7, 1167 (2019).

    Google Scholar 

  33. P. Li, Y. Wu, W. Liu, Y. Zuo, J. Lü, and R. Tu, “Preparation of resorcinol-dialdehyde starch-formaldehyde copolycondensation resin adhesive,” Gaofenzi Cailiao Kexue Yu Gongcheng/ Polym. Mater. Sci. Eng., 35, 122-129 (2019).

    CAS  Google Scholar 

  34. N. Ayrilmis, J. H. Kwon, S. H. Lee, T. H. Han, and C. W. Park, “ Microfibrillated-cellulose-modified urea-formaldehyde adhesives with different U:F molar ratios for wood-based composites,” J. Adhes. Sci. Technol., 30, No. 18, 2032-2043 (2016).

    CAS  Google Scholar 

  35. D. J. Gardner, G. S. Oporto, R. Mills and M. A. S. A. Samir, “Adhesion and surface issues in cellulose and nanocellulose,” J. Adhes. Sci. Technol., 22, No. (5-6), 545-567 (2008).

    CAS  Google Scholar 

  36. S. Veigel, U. Müller, J. Keckes, M. Obersriebnig, and W. Gindl-Altmutter, “Cellulose nanofibrils as filler for adhesives: Effect on specific fracture energy of solid wood-adhesive bonds,” Cellulose., 18, No. 1, 1227-1237 (2011).

    CAS  Google Scholar 

  37. E. Mahrdt, S. Pinkl, C. Schmidberger, H. W. G. van Herwijnen, S. Veigel, and W. Gindl-Altmutter, “Effect of addition of microfibrillated cellulose to urea-formaldehyde on selected adhesive characteristics and distribution in particle board,” Cellulose, 23, No. 1, 571-580 (2016).

    CAS  Google Scholar 

  38. C. Loxton, A. Thumm, W. Grigsby, T. A. Adams, and R. M. Ede, “Resin distribution in medium density fiberboard. Quantification of UF resin distribution on blowline- and dry-blended MDF fiber and panels,” Wood fiber Sci., 35, No. 3, 370-380 (2003).

    CAS  Google Scholar 

  39. H. Zhang, J. Zhang, S. Song, G. Wu, and J. Pu, “Modified nanocrystalline cellulose from two kinds of modifiers used for improving formaldehyde emission and bonding strength of ureaformaldehyde resin adhesive,” Bioresources, 6, No. 4, 4430-4438 (2011).

    CAS  Google Scholar 

  40. X.-f. Zhao, L.-q. Peng, H.-l. Wang, Y.-b. Wang, and H. Zhang, “Environment-friendly urea-oxidized starch adhesive with zero formaldehyde-emission,” Carbohydr. Polym., 181, 1112-1118 (2018).

    CAS  Google Scholar 

  41. X. Jiang, C. Li, Y. Chi, and J. Yan, “TG-FTIR study on urea-formaldehyde resin residue during pyrolysis and combustion,” J. Hazard. Mater., 173, No. 1, 205-210 (2010).

    CAS  Google Scholar 

  42. N. S. Sulaiman, R. Hashim, O. Sulaiman, M. Nasir, M. H. M. Amini, and S. Hiziroglu, “Partial replacement of ureaformaldehyde with modified oil palm starch based adhesive to fabricate particleboard,” Int. J. Adhes Adhes., 84, 1-8 (2018).

    CAS  Google Scholar 

  43. G. Hamdi and G. Ponchel, “Enzymatic degradation of epichlorohydrin crosslinked starch microspheres by α-amylase,” Pharm. Res., 16, No. 6, 867-875 (1999).

    CAS  Google Scholar 

  44. M. Dunky, in: A. Pizzi, L. M. Kashmiri (eds.), Handbook of Adhesive Technology, Ch. 47, Second ed., Taylor & Francis Group, LLC (2003).

    Google Scholar 

Download references

Acknowledgment

Authors gratefully acknowledge the Bio-systems Laboratory of Shahid Beheshti University for the technical support.

Author information

Authors and Affiliations

  1. Faculty of New Technologies Engineering, Shahid Beheshti University, Tehran, Iran

    M. Nazerian, S. A. Razavi, A. Partovinia, E. Vatankhah & Z. Razmpour

Authors
  1. M. Nazerian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. A. Razavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Partovinia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. Vatankhah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Z. Razmpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Nazerian.

Additional information

Russian translation published in Mekhanika Kompozitnykh Materialov, Vol. 56, No. 5, pp. 945-966, September-October, 2020.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nazerian, M., Razavi, S.A., Partovinia, A. et al. Prediction of the Bending Strength of a Laminated Veneer Lumber (LVL) Using an Artificial Neural Network. Mech Compos Mater 56, 649–664 (2020). https://doi.org/10.1007/s11029-020-09911-4

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11029-020-09911-4

Keywords

Search

Navigation