Prediction of Withdrawal Strength of Nail of Uludag Fir Wood by Using Artificial Neural Network (ANNs)
Öz
Anahtar Kelimeler
Kaynakça
- BS EN 13446 (2002) Wood-based panels. Determination fasteners. capacity of
- Örs,Y., Efe, H., Kasal, A., (1999) Effect of corner wooden wedge geometry on bending strength in dismountable leg and table joints of furniture, I. International furniture congress and Exhibition, 457-471.
- Eckelman, C., (1990) Fasteners and Their Use in Particleboard and Medium Density Fiberboard. National Particleboard” Association. Purdue University; March 30.
- İmirzi, Ö. H., (2000) Mechanical properties of massive furniture “T” joints with frame construction, UniversityIinstitute of Science and technology, Ankara. Thesis, Gazi
- Örs,Y., Özen, R., Doğanay, S., (1998) Screw holding ability (strength) of wood materials used in furniture manufacture, Turkish J. agriculture and forestry, 22: 29-34.
- Ozçifçi A., Doğanay S., (1999) Withdrawal Strength of Some Screws and Nails in Waferboard and Picea or Oriental Beech” Journal of Agriculture and Foresty Tubitak 23: (5), 1207- 1213.
- Yapıcı, F., Gündüz, G., Özçifçi, A., Likos, E., (2009) Prediction of Screw and Nail Withdrawal Strength on OSB (Oriented Strand Board) Panels With Fuzzy Classifier, Technology, 12 (3):167- 174.
- Vosniakos, G.C., Benardos, P.G., (2007) Optimizing Network Architecture. Eng. Appl. Artif. Intell. 20 (3): 365–382. Artificial Neural
- Tou, J.Y., Lau, P.Y., Tay, Y. H., (2007) Computer System, Proceedings of International Workshop on Advanced Image Technology (IWAIT), 197- 202, Bangkok, Thailand. Wood Recognition
- Marzuki Khalid, M., Lee, E.L.Y., Rubiyah Y., and Miniappan N., (2008), Design of an Intelligent Wood Species Recognition System, International Journal of Simulation: Systems, Science & Technology, Vol. 9, No. 3.
- Zhang, S.Y., Liu, C., and Jiang Z.H., (2006), Modeling product recovery in relation to selected tree characteristics in black spruce using an optimized random sawing simulator, Forest Products Journal, Vol. 56, No. 11-12, 93-99.
- Packianather M. S., and Drake, P. R., (2000) Neural networks for classifying images of wood veneer. Part 2, The International Advanced Manufacturing Technology, 16:424-433.
- Xu, X., Yu, Z.T., Hu, Y. C., Fan, L.W., Tian, T., calculation of wood thermal conductivity using neural Networks, Zhejiang University Press, Vol. 41, Issue. 7, 1201–1204. Nonlinear fitting
- Samarasinghe, S., Kulasiri, D., Jamieson, T., (2007), Neural Networks for predicting fracture toughness of individual wood samples, Silva Fennica, 41(1): 105–122.
- Shawn D. M., Lazaros I., Stavros A., (2007) Avramidis Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.), Holzforschung, Vol. 61, Issue. 6, 2007, 707-716.
- Stavros, A., Hongwei W., (2007) Artificial neural network and mathematical modeling comparative analysis of non-isothermal diffusion of moisture in wood, Holz als Roh- und Werkstoff, 65: 89–93.
- TS EN 323 (1999) Wood–based panels – determination of density. Turkish Standards, TSE, Ankara.
- TS EN 322 (1999) Wood–based panels – determination of moisture content. Turkish Standards, TSE, Ankara.
- TS EN 13446 (2005) Wood–based panels – determination of withdrawal capacity of fasteners, Turkish Standards, TSE, Ankara.
Prediction of Withdrawal Strength of Nail of Uludag Fir Wood by Using Artificial Neural Network (ANNs)
Öz
In this study, the effects of type of nails material and grain angle of wood on the withdrawal strength of nail have been researched. For this purpose specimens were firstly cut in different sections from Uludağ Fir (Abies bornmülleriana M.) wood. The tests of static nail strength were carried out according to the standards of TS EN 13446. Secondly, an artificial neural network system was built by using data obtained in an experimental study for the prediction of withdrawal nail strength. The comparison between the experimental data and predicted data was also carried out
Anahtar Kelimeler
Kaynakça
- BS EN 13446 (2002) Wood-based panels. Determination fasteners. capacity of
- Örs,Y., Efe, H., Kasal, A., (1999) Effect of corner wooden wedge geometry on bending strength in dismountable leg and table joints of furniture, I. International furniture congress and Exhibition, 457-471.
- Eckelman, C., (1990) Fasteners and Their Use in Particleboard and Medium Density Fiberboard. National Particleboard” Association. Purdue University; March 30.
- İmirzi, Ö. H., (2000) Mechanical properties of massive furniture “T” joints with frame construction, UniversityIinstitute of Science and technology, Ankara. Thesis, Gazi
- Örs,Y., Özen, R., Doğanay, S., (1998) Screw holding ability (strength) of wood materials used in furniture manufacture, Turkish J. agriculture and forestry, 22: 29-34.
- Ozçifçi A., Doğanay S., (1999) Withdrawal Strength of Some Screws and Nails in Waferboard and Picea or Oriental Beech” Journal of Agriculture and Foresty Tubitak 23: (5), 1207- 1213.
- Yapıcı, F., Gündüz, G., Özçifçi, A., Likos, E., (2009) Prediction of Screw and Nail Withdrawal Strength on OSB (Oriented Strand Board) Panels With Fuzzy Classifier, Technology, 12 (3):167- 174.
- Vosniakos, G.C., Benardos, P.G., (2007) Optimizing Network Architecture. Eng. Appl. Artif. Intell. 20 (3): 365–382. Artificial Neural
- Tou, J.Y., Lau, P.Y., Tay, Y. H., (2007) Computer System, Proceedings of International Workshop on Advanced Image Technology (IWAIT), 197- 202, Bangkok, Thailand. Wood Recognition
- Marzuki Khalid, M., Lee, E.L.Y., Rubiyah Y., and Miniappan N., (2008), Design of an Intelligent Wood Species Recognition System, International Journal of Simulation: Systems, Science & Technology, Vol. 9, No. 3.
- Zhang, S.Y., Liu, C., and Jiang Z.H., (2006), Modeling product recovery in relation to selected tree characteristics in black spruce using an optimized random sawing simulator, Forest Products Journal, Vol. 56, No. 11-12, 93-99.
- Packianather M. S., and Drake, P. R., (2000) Neural networks for classifying images of wood veneer. Part 2, The International Advanced Manufacturing Technology, 16:424-433.
- Xu, X., Yu, Z.T., Hu, Y. C., Fan, L.W., Tian, T., calculation of wood thermal conductivity using neural Networks, Zhejiang University Press, Vol. 41, Issue. 7, 1201–1204. Nonlinear fitting
- Samarasinghe, S., Kulasiri, D., Jamieson, T., (2007), Neural Networks for predicting fracture toughness of individual wood samples, Silva Fennica, 41(1): 105–122.
- Shawn D. M., Lazaros I., Stavros A., (2007) Avramidis Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.), Holzforschung, Vol. 61, Issue. 6, 2007, 707-716.
- Stavros, A., Hongwei W., (2007) Artificial neural network and mathematical modeling comparative analysis of non-isothermal diffusion of moisture in wood, Holz als Roh- und Werkstoff, 65: 89–93.
- TS EN 323 (1999) Wood–based panels – determination of density. Turkish Standards, TSE, Ankara.
- TS EN 322 (1999) Wood–based panels – determination of moisture content. Turkish Standards, TSE, Ankara.
- TS EN 13446 (2005) Wood–based panels – determination of withdrawal capacity of fasteners, Turkish Standards, TSE, Ankara.
Toplam 19 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Yazarlar | |
| Yayımlanma Tarihi | 1 Eylül 2012 |
| IZ | https://izlik.org/JA37KX34SX |
| Yayımlandığı Sayı | Yıl 2012 Cilt: 12 Sayı: 3 |