Araştırma Makalesi

Prediction of Retention Level and Mechanical Strength of Plywood Treated With Fire Retardant Chemicals by Artificial Neural Networks

Cilt: 5 Sayı: 5 31 Aralık 2020
Şükrü Özşahin , Aydın Demir *, İsmail Aydın
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Journal of Anatolian Environmental and Animal Sciences Kapak Journal of Anatolian Environmental and Animal Sciences
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Prediction of Retention Level and Mechanical Strength of Plywood Treated With Fire Retardant Chemicals by Artificial Neural Networks

Abstract

The treatment with fire retardant chemicals is the most effective process to protect wood and wood based products from fire is. Therefore, use of fire retardant chemicals has been increased. However, the fire retardant chemicals have an effect on other physical, mechanical and some technological properties of the materials treated with them. In this study, firstly, the retention level prediction model was developed with the artificial neural network (ANN) to examine the effects of wood species and concentration aqueous solution on the retention levels of veneers. Then, the effects of wood species, concentration aqueous solution and retention level on the mechanical properties of plywood were investigated with the mechanical strength prediction model developed with ANN. The prediction models with the best performance were determined by statistical and graphical comparisons. It has been observed that ANN models yielded very satisfactory results with acceptable deviations. As a result, the findings of this study could be employed effectively into the forest products industry to reduce time, energy and cost for empirical investigations.

Keywords

Artiical Neural Network , Fire Retardant , Plywood , Concentration , Retention Level , Mechanical Properties

Kaynakça

  1. Aydin, I., (2004). Effects of Some Manufacturing Conditions on Wettability and Bonding of Veneers Obtained from Various Wood Species, PhD Thesis, KTU Natural Science Institute, Trabzon.
  2. Ceylan, I., (2008). Determination of Drying Characteristics of Timber by Using Artificial Neural Networks and Mathematical Models, Drying Technology, 26(12), 1469-1476.
  3. Cheng, R. X. & Wang, Q. W., (2011). The influence of FRW-1 fire retardant treatment on the bonding of plywood, Journal of Adhesion Science and Technology, 25, 1715–1724.
  4. Demir, A., Aydin, I. & Colak, S., (2016). Effect of various fire retardant chemicals in different concentrations on mechanical properties of plywood. In Proc. 2nd International Furniture Congress, Muğla, Turkey, 13-15 October, pp. 411.
  5. Demirkir, C., Özsahin, Ş., Aydin, I. & Colakoglu, G., (2013). Optimization of some panel manufacturing parameters for the best bonding strength of plywood, International Journal of Adhesion and Adhesives, 46, 14-20.
  6. EN 310, (1993). Wood based panels. Determination of modulus of elasticity in bending and of bending strength. European Standard.
  7. EN 314-1, (1998). Plywood–bonding quality–Part1: test methods, European Standard.
  8. Esteban, L.G., Fernandez, F.G. & Palacios, P., (2011). Prediction of plywood bonding quality using an artificial neural network, Holzforschung, 65(2), 209–214.
  9. Fateh, T., Rogaume, T., Luche, J., Richard, F. & Jabouille, F., (2013). Kinetic and mechanism of the thermal degradation of a plywood by using thermogravimetry and Fourier-transformed infrared spectroscopy analysis in nitrogen and air atmosphere, Fire Safety Journal, 58, 25 - 37.
  10. Fernández, F.G., Esteban, L.G., Palacios, P., Navarro, N. & Conde, M., (2008). Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model, Investigación agraria: Sistemas y recursos forestales, 17(2), 178–187.

Ayrıntılar

Birincil Dil

İngilizce

Konular

-

Bölüm

Araştırma Makalesi

Yazarlar

Yayımlanma Tarihi

31 Aralık 2020

Gönderilme Tarihi

13 Kasım 2020

Kabul Tarihi

25 Kasım 2020

Yayımlandığı Sayı

Yıl 2020 Cilt: 5 Sayı: 5

DOI

https://doi.org/10.35229/jaes.825435

IZ

https://izlik.org/JA45MW55AN

Kaynak Göster

RIS / Bibtex
APA
Özşahin, Ş., Demir, A., & Aydın, İ. (2020). Prediction of Retention Level and Mechanical Strength of Plywood Treated With Fire Retardant Chemicals by Artificial Neural Networks. Journal of Anatolian Environmental and Animal Sciences, 5(5), 785-792. https://doi.org/10.35229/jaes.825435

   JAES, 2016- 11.yıl