Ahşap Kompozit Malzemelerin Mekanik ve Fiziksel Özelliklerine göre Tahmininde Radyal Temelli Fonksiyon Sinir Ağının Kullanımı

Öz

Mühendisler ve tasarımcılar açısından bir malzemenin mekanik ve fizikselözelliklerinin bilinmesi malzemenin kullanım amacının belirlenmesinde en önemli kriterlerdendir. Ahşap kompozit malzemelerin mekanik ve fiziksel özelliklere göre tahmini, gelecekteki ahşap kompozit malzeme uygulamalarında önemli bir rol oynayacaktır. Bu çalışmada mobilya endüstrisinde ve inşaat sektöründe yaygın kullanıma sahip olan yonga levha, lif levha, yönlendirilmiş yonga levha ve kontrplak gibi ahşap kompozit malzemelerin mekanik özelliklerine göre tahmin işlemi radyal temelli fonksiyon ağı ile gerçekleştirilmiştir. Ahşap kompozit malzemelerin tahmininde levha yoğunluğu, eğilme direnci, eğilme elastikiyet direnci ve çekme direnci olarak dört fiziksel ve mekanik özellik kullanılmıştır. Bu çalışma, ahşap kompozit malzeme kullanıcılarının herhangi bir konstrüksiyon için önceden belirledikleri mekanik ve fiziksel özellikleri sağlayacak ahşap kompozit malzemenin seçiminde yardımcı olacaktır. Ayrıca, bu çalışma literatürdeki bu boşluğu dolduracaktır. 

Anahtar Kelimeler

• Physico-mechanical properties,Plywood,Fiberboard,Radial basis function neural network,Particleboard,Oriented strand board

Use of Radial Basis Function Neural Network in Estimating Wood Composite Materials According to Mechanical and Physical Properties

Öz

Knowing the mechanical and physical properties of a material is the most important criteria for engineers and designers interested in determining the intended use of the material. The prediction of wood composite materials based on their mechanical and physical properties plays an important role in their future application. In this study, radial basis function network approach was employed for prediction according to mechanical and physical properties of wood composite materials such as particleboard, fiberboard, oriented strand board and plywood, which have widespread use in the furniture industry and construction sector. Four physical and mechanical properties were used as the board density, bending strength, bending elastic modulus and tensile strength in the prediction of the wood composite materials. This study will assist wood composite users in the selection of wood composite materials that will provide the mechanical and physical properties determined in advance for any construction. Moreover, the present study will fill this gap in literature.

Kaynakça

1. Avramidis, S., Iliadis, L. (2005). “Predicting Wood Thermal Conductivity using Artificial Neural Networks”, Wood and Fiber Science, 37(4), 682-690.
2. Behera L. (2018). Lecture Notes. http://home.iitk.ac.in/~lbehera/Files/Lecture5_RBFN.pdf (Accessed 20.04.2018).
3. Cai, Z., Ross, R. J. (2010). “Mechanical properties of wood-based composites materials”, In: Wood Handbook, Wood as an Engineering Material, U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, General Technical Report FPL-GTR-190, Madison, 12-1-12-12.
4. Cook, D. F., Chiu, C. C. (1997). “Predicting the Internal Bond Strength of Particleboard, Utilizing a Radial Basis Function Neural Network”, Engineering Applications of Artificial Intelligence, 10(2), 171-177.
5. Esteban, L. G., de Palacios, P., Fernández, F. G. (2010). “Use of Artificial Neural Networks as a Predictive Method to Determine Moisture Resistance of Particle and Fiber Boards Under Cyclic Testing Conditions (UNE-EN 321)”, Wood and Fiber Science, 42(3), 335-345.
6. Fernandez, F. G., Esteban, L. G., de 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.
7. Fernandez, F. G., de Palacios, P., Esteban, L. G., Iruela, A. G., Rodrigo, B. G., Menasalvas, E. (2012). “Prediction of MOR and MOE of Structural Plywood Board using an Artificial Neural Network and Comparison with a Multivariate Regression Model”, Composites Part B, 43, 3528-3533.
8. Ilkucar, M., Kaya, A. I., Cifci, A. (2018). “Mekanik Özelliklere Göre Ağaç Türlerinin Yapay Sinir Ağları ile Tahmini”, Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 8(1), 75-83.

9. Marcano-Cedeño, A., Quintanilla-Domínguez, J., Andina, D. (2009). “Wood Defects Classification using Artificial Metaplasticity Neural Network”, 35th Annual Conference of the IEEE Industrial Electronics Society, Porto, Portugal, 3422-3427.
10. Melo, R. R., Miguel, E. P. (2016). “Use of Artificial Neural Networks in Predicting Particleboard Quality Parameters”, Revista Árvore, 40(5), 949-958.
11. Miguel, E. P., de Melo, R. R., Junior, L. S., Del Menezzi, C. H. S. (2018). “Using Artificial Neural Networks in Estimating Wood Resistance”, Maderas. Ciencia y Tecnología, 20 (unassigned).
12. Montazer, G. A., Giveki, D. (2015). “An Improved Radial Basis Function Neural Network for Object Image Retrieval”, Neurocomputing, 168, 221-233.
13. Ozşahin Ş. (2012). “The Use of Artificial Neural Network for Modeling the Moisture Absorption and Thickness Swelling Properties of Oriented Standard Board”, BioResources, 7(1), 1053-1067.
14. Parsaie, A, Haghiabi, A. H. (2015). “Predicting the Longitudinal Dispersion Coefficient by Radial Basis Function Neural Network”, Modeling Earth Systems and Environment, 1(4), 1-8.
15. P´erez-Godoy, M. D., Rivera, A. J., Carmona, C. J., del Jesus M. J. (2014). “Training Algorithms for Radial Basis Function Networks to Tackle Learning Processes with Imbalanced Data-Sets”, Applied Soft Computing, 25, 26-39.
16. Qayyum, R., Kamal, K., Zafar, T., Mathavan, S. (2016). “Wood Defects Classification using GLCM based Features and PSO Trained Neural Network”, 22nd International Conference on Automation and Computing (ICAC), Colchester, UK, 273-277.
17. Shahnorbanun, S., Siti Nurul Huda, S. A., Haslina, A., Nazlia, O., Rosilah, H. (2010). “A Computational Biological Network for Wood Defect Classification”, World Congress on Engineering and Computer Science (WCECS), San Francisco, USA, 1-5.
18. Tatar, A., Naseri, S., Sirach, N., Lee, M., Bahadori, A. (2015). “Prediction of Reservoir Brine Properties using Radial Basis Function (RBF) Neural Network”, Petroleum, 1(4), 349-357.
19. Tchircoff, A. (2018). The Mostly Complete Chart of Neural Networks, Explained. https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464 (Accessed 24.04.2018).
20. Tiryaki, S., Ozşahin, S., Yıldırım, I. (2014). “Comparison of Artificial Neural Network and Multiple Linear Regression Models to Predict Optimum Bonding Strength of Heat Treated Woods”, International Journal of Adhesion & Adhesives, 55, 29-36.
21. Xu, X., Yu, Z. T., Hu, Y. C., Fan, L. W., Tian, T., Cen, K. F. (2007). “Nonlinear Fitting Calculation of Wood Thermal Conductivity using Neural Networks”, Journal of Zhejiang University, 41(7), 1201-1204.
22. Zhang, J., Cao, J., Zhang, D. (2006). “ANN-based Data Fusion for Lumber Moisture Content Sensors”, Transactions of the Institute of Measurement and Control, 28(1), 69-79.
23. Zhao, N., Wen, X., Yang, J., Li, S., Wang, Z. (2015). “Modeling and Prediction of Viscosity of Water-Based Nanofluids by Radial Basis Function Neural Networks”, Powder Technology, 281, 173-183.