ESTIMATION THE PROPERTIES OF PARTICLEBOARDS MANUFACTURED FROM VINE PRUNINGS STALKS USING ARTIFICIAL NEURAL NETWORKS

Year 2015, , 24 - 33, 07.04.2015

Hüseyin Gürüler , Serkan Ballı , Mehmet Yeniocak , Osman Göktaş

https://doi.org/10.22531/muglajsci.209996

Cited By: 5

Abstract

In this study, pruned vine particles and wood particles in five various proportions were used as the raw material for three-layer particleboards. Primarily, small size sample panels (56x56x2 cm) were manufactured. The physical (thickness swelling (TS), water absorption (WA)), and mechanical (modulus of rupture (MOR), modulus of elasticity (MOE), internal bond (IB)) screw holding (SH) properties of particleboards were determined. Although direct measurement is the most reliable method, it is very complex and time consuming. Also every proportion is not applicable. So that, soft computing methods which are the powerful tools for input-output mapping were preferred. Artificial neural networks (ANNs) were used to estimation. The results show that ANN system capable to predict properties of particleboards in a time and cost effective way.

Keywords

Neural networks, Agricultural residue, Particleboard, Prediction of properties

YAPAY SİNİR AĞLARI KULLANILARAK BAĞ BUDAMA ARTIKLARINDAN ÜRETİLEN YONGA LEVHALARIN ÖZELLİKLERİNİN TAHMİN EDİLMESİ

Abstract

Bu çalışmada, yonga levha hammaddesi olarak beş farklı oranda, bağ budama yongaları ve odun yongaları kullanılmıştır. Öncelikle küçük boyutlu numune paneller üç katmanlı olarak (56x56x2 cm) üretilmiştir. Bu levhaların; fiziksel (kalınlığına şişme, su alma) ve mekaniksel (eğilme direnci, elastikiyet modülü, iç yapışma ve vida tutma) özellikleri belirlenmiştir. Direk ölçüm alma metodu en güvenilir metot olmasına rağmen çok karmaşık ve zaman alıcıdır. Üstelik her oran uygulanabilir değildir. Bu nedenle girdi-çıktı haritalamada en güçlü araçlar olan esnek (yazılımsal) hesaplama yöntemleri tercih edilmiştir. Bu aşamada, yapay sinir ağları (YSA) tahmin amaçlı kullanıldı. Sonuçlar yapay sinir ağları sisteminin zamandan ve maliyetten kazanç sağlayarak yonga levhaların özelliklerinin belirlenmesinde yetkin olduğunu göstermektedir

Keywords

Sinir ağları, Tarımsal atıklar, Yonga levha, Özelliklerin tahmini

References

• Ntalos, G.A., Grigoriou, A.H., “Characterization and utilisation of vine prunings as a wood substitute for particleboard production”, Ind. Crops Prod., 16, 59–68, 2002.
• Nemli, G., Kirci, H., Serdar, B., Ay, N., “Suitability of kiwi (Actinidia sinensis Planch.) prunings for particleboard manufacturing”, Ind. Crops Prod., 17, 39–46, 2003.
• Papadopoulos, A.N., Hague, J.R.., “The potential for using flax (Linum usitatissimum L.) shiv as a lignocellulosic raw material for particleboard”, Ind. Crops Prod., 17, 143–147, 2003.
• Copur, Y., Guler, C., Akgul, M., Tasiıoglu, C., “Some chemical properties of hazelnut husk and its suitability for particleboard production”, Build. Environ., 42, 2568–2572, 2007.
• Bektas, I., Guler, C., Kalaycioglu, H., “Manufacturing of Particleboard from Sunflower Stalks (Helianthus annuus L.) Using Urea-Formaldehyde Resin”, Kahramanmaras Sutcu Imam University, J. Sci. Eng, 5, 49–56, 2002.
• Kalaycioglu, H., Nemli, G., “Producing composite particleboard from kenaf (Hibiscus cannabinus L.) stalks”, Ind. Crops Prod., 2006.
• Guru, M.S., Tekeli, B., Bilici, I., “Manufacturing of urea–formaldehyde-based composite particleboard from almond shell”, Mater Des., 27, 1148–1151, 2006.
• Wang, D., Sun, X.S., “Low density particleboard from wheat straw and corn pith”, Ind. Crops Prod., 2002.
• Alma, M.H., Kalaycıoğlu, H., Bektaş, I., Tutuş, A., “Properties of cotton carpel-based particleboards”, Ind. Crop. Prod., 22, 141–149, 2005.
• Almeida, R.R., Del, M.C.H., Teixeira, D.E., “Utilization of the coconut shell of babachu (Orbignya sp.) to produce cement-bonded particleboard”, Bioresour. Tech., 85, 159–163, 2002.
• Hashimoto, Y., “Applications of artificial neural networks and genetic algorithms to agricultural systems”, Comput. Electron. Agric., 18, 71–72, 1997.
• Huang, Y., Lan, Y., Thomson, S.J., Fang, A., Hoffmann, W.C., Lacey, R.E., “Development of soft computing and applications in agricultural and biological engineering”, Comput. Electron. Agric., 71, 107–127, 2010.
• Kaul, M., Hill, R.L., Walthall, C., “Artificial neural networks for corn and soybean yield prediction”, Agric. Syst., 85, 1–18, 2005.
• Broner, I., Comstock, C.R., “Combining expert systems and neural networks for learning site-specific conditions”, Comput. Electron. Agric., 19, 37–53, 1997.
• Cartwright, H., “Using artificial intelligence in chemistry and biology: A practical guide”, CRC Press., 2008.
• Mendas, A., Delali, A., “Integration of MultiCriteria Decision Analysis in GIS to develop land suitability for agriculture: Application to durum wheat cultivation in the region of Mleta in Algeria”, Comput. Electron. Agric., 83, 117–126, 2012.
• Jia, J., Davalos, J.F., “An artificial neural network for the fatigue study of bonded FRP–wood interfaces”, Compos. Struct., 74, 106–114, 2006.
• Yapici, F., Ozcifci, A., Akbulut, T., Bayir, R., “Determination of modulus of rupture and modulus of elasticity on flakeboard with fuzzy logic classifier”, Mater. Des., 30, 2269–2273, 2009.
• Riegler, M., Spangl, B., Weigl, M., Wimmer, R., Müller, U., “Simulation of a real-time process adaptation in the manufacture of high-density fibreboards using multivariate regression analysis and feedforward control”, Wood Sci. Technol., 47, 1243–1259, 2013.
• Demirkir, C., Özsahin, Ş., Aydin, I., Colakoglu, G., “Optimization of some panel manufacturing parameters for the best bonding strength of plywood”, Int. J. Adhes. Adhes., 46, 14–20, 2013.
• Young, T.M., “Parametric and non-parametric regression tree models of the strength properties of engineered wood panels using real-time industrial data”, 2007.
• André, N., Cho, H.-W., Baek, S.H., Jeong, M.-K., Young, T.M., “Prediction of internal bond strength in a medium density fiberboard process using multivariate statistical methods and variable selection”, Wood Sci. Technol., 42, 521–534, 2008.
• Zhang, G., Patuwo, B.E., Hu, M.Y., “Forecasting with Artificial Neural Network: The State of the Art”, Int. J. Forecast., 14, 35–62, 1998.
• Kaastra, I., Boyd, M., “Designing a neural network for forecasting financial and economic time series”, Neurocomputing, 10, 215–236, 1996.
• Jain, L.C., Martin, N.M., “Fusion of neural networks, fuzzy sets, and genetic algorithms: industrial applications”, CRC press., 1998.
• McCulloch, W.S., Pitts, W., “A logical calculus of the ideas immanent in nervous activity”, Bull. Math. Biophys., 5, 115–133, 1943.
• Rumelhart, D.E., McClelland, J.L., the PDP Research Group 1986, “Parallel Distributed Processing: Explorations in the Microstructure of Cognition”, Found. MIT Press., Cambridge MA, 1986.
• Hu, Y.H., Hwang, J.-N., “Handbook of neural network signal processing”, CRC press, 2001.
• Russell, S.J., Norvig, P., “Artificial Intelligence: A Modern Approach”, Second Ed., Prentice Hall,Upper Saddle River, New Jersey, 2003.
• Karasulu, B., Balli, S., “Image segmentation using fuzzy logic, neural networks and genetic algorithms: Survey and trends”, Mach. Graph. Vis., 19, 367–409, 2010.
• Graupe, D., “Principles of Artificial Neural Networks”, 3rd Editio. ed. World Scientific Press, Singapore, 2013.
• Haykin, S., “Neural Networks: A Comprehensive Foundation”, The Knowledge Engineering Review, Prentice Hall, 1999.
• Kohonen, T., “Self-organizing maps” Springer, 2001.
• Mellit, A., Kalogirou, S.A., “Artificial intelligence techniques for photovoltaic applications: A review”, Prog. Energy Combust. Sci., 2008.
• Ballı, S., Tarımer, İ., “An Application of Artificial Neural Networks for Prediction and Comparison with Statistical Methods”, Electron. Electr. Eng., 19, 101–105, 2013.
• Nemli, G., Kalaycioglu, H., Alp, T., “Suitability of date palm branches for particleboard production”, Holz als Roh und Werkstoff, 59,411-412, 200