Modeling of Wood Bonding Strength Based on Soaking Temperature and Soaking Time by means of Artificial Neural Networks

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 153 - 157, 26.12.2016

Sebahattin Tiryaki , Selahattin Bardak , Aytaç Aydın

Öz

Adhesive bonding of wood enables
sufficient strength and durability to hold wood pieces together and thus
produce high quality wood products. However, it is well known that many
variables have an important influence on the strength of an adhesive bonding.
The objective of the present paper is to predict the bonding strength of spruce
(Picea orientalis (L.) Link.) and
beech (Fagus orientalis Lipsky.) wood
joints subjected to soaking by using artificial neural networks. To obtain the
data for modeling, beech and spruce samples were subjected to the soaking at
different temperatures for different periods of time. In the ANN analysis, 70%
of the total experimental data were used to train the network, 15% was used to
test the validation of the network, and remaining 15% was used to test the
performance of the trained and validated network. A three-layer feedforward
back propagation artificial neural network trained by Levenberg–Marquardt
learning algorithm was found as the optimum network architecture for the
prediction of the bonding strength of soaked wood samples. This architecture
could predict wood bonding strength with an acceptable level of the error.
Consequently, modeling results demonstrated that artificial neural networks are
an efficient and useful modeling tool to predict the bonding strength of wood
samples subjected to the soaking for different temperatures and durations.

Anahtar Kelimeler

Neural network, Bonding strength, Prediction, Wood, Soaking

Kaynakça

• [1] A. Mori, T. Kitayama, M. Takatani, and T. Okamoto (2004). A Honeymoon-Type Adhesive for Wood Products Basedon Acetoacetylated Poly(vinyl alcohol) and Diamines: Effect of Diamines and Degree of Acetoacetylation. Journal of Applied Polymer Science. Vol. 91. Pages. 2966–2972.
• [2] H. Xiao, W. Wang, and Y.H. Chui (2007). Evaluation of Shear Strength and Percent Wood Failure Criteria for Qualifying New Structural Adhesives. Canada: University of New Brunswick; July. (Project No. UNB50).
• [3] A.A. Marra (1992). Technology of Wood Bonding, Principles in Practice. Van Nostrand-Reinhold, New York, USA.
• [4] S. Ozsahin (2013). Optimization of Process Parameters in Oriented Strand Board Manufacturing with Artificial Neural Network Analysis. European Journal of Wood and Wood Products. Vol. 71. Pages. 769-777.
• [5] K. Kumar and G.S.M. Thakur (2012). Advanced Applications of Neural Networks and Artificial Intelligence: A Review. I. J. Information Technology and Computer Science. Vol. 6. Pages. 57-68.
• [6] S. Tiryaki and C. Hamzacebi (2014). Predicting Modulus of Rupture (MOR) and Modulus of Elasticity (MOE) of Heat Treated Woods by Artificial Neural Networks. Measurement. Vol. 49. Pages. 266-274.
• [7] S.N. Londhe and M.C. Deo (2003). Wave Tranquility Studies Using Neural Networks. Marine Structures. Vol. 16. Pages. 419–436.
• [8] M.C. Taskin, U. Aligulu and H. Dikbas (2008). Artificial Neural Network (ANN) Approach to Prediction of Diffusion Bonding Behaviour (Shear Strength) of SiCp Reinforced Aluminium Metal Matrix Composites. Journal of Yasar University. Vol. 3. Pages. 1811–25.
• [9] E. Sancak (2009). Prediction of Bond Strength of Lightweight Concretes by Using Artificial Neural Networks. Scientific Research and Essay. Vol. 4. Pages. 256-266.
• [10] E.M. Golafshani, A. Rahai, M.H. Sebt and H. Akbarpour (2012). Prediction of Bond Strength of Spliced Steel Bars in Concrete Using Artificial Neural Network and Fuzzy Logic. Construction and Building Materials. Vol. 36. Pages. 411–418.
• [11] F. Wang, J. Li, S. Liu and L. Han (2014). Heavy Aluminum Wire Wedge Bonding Strength Prediction Using a Transducer Driven Current Signal and an Artificial Neural Network. IEEE Transactions on Semiconductor Manufacturing. Vol. 27. Pages. 232-237.
• [12] I. Ceylan (2008). Determination of Drying Characteristics of Timber by Using Artificial Neural Networks and Mathematical Models. Drying Technology. Vol. 26. Pages. 1469–1476.
• [13] S. Tiryaki, A. Malkocoglu and S. Ozsahin (2014). Using Artificial Neural Networks for Modeling Surface Roughness of Wood in Machining Process. Construction and Building Materials. Vol. 66. Pages. 329–335.
• [14] H. Yang, W. Cheng and G. Han (2015).Wood Modification at High Temperature and Pressurized Steam: A Relational Model of Mechanical Properties Based on a Neural Network. Bioresources. Vol. 10. Pages. 5758-5776.
• [15] L.G. Esteban, F.G. Fernandez and P. DePalacios (2009). MOE Prediction in Abies pinsapo Boiss. Timber: Application of an Artificial Neural Network Using Non-Destructive Testing. Computers & Structures. Vol. 87. Pages. 1360–1365.
• [16] S. Tiryaki and A. Aydin (2014). An Artificial Neural Network Model for Predicting Compression Strength of Heat Treated Woods and Comparison with a Multiple Linear Regression Model. Construction and Building Materials. Vol. 62. Pages.102–108.
• [17] S. Ozsahin and I. Aydin (2014). Prediction of the Optimum Veneer Drying Temperature for Good Bonding in Plywood Manufacturing by means of Artificial Neural network. Wood Science and Technology. Vol. 48. Pages. 59–70.
• [18] C. Demirkir, S. Ozsahin, I. Aydin and G. Colakoglu (2013). Optimization of Some Panel Manufacturing Parameters for the Best Bonding Strength of Plywood. International Journal of Adhesion and Adhesives. Vol. 46. Pages. 14–20.
• [19] S. Tiryaki, S. Ozsahin and I. Yildirim (2014). Comparison of Artificial Neural Network and Multiple Linear Regression Models to Predict Optimum Bonding Strength of Heat Treated Woods. International Journal of Adhesion and Adhesives. Vol. 55. Pages. 29-36.
• [20] S. Tiryaki, S. Bardak and T. Bardak (2015). Experimental Investigation and Prediction of Bonding Strength of Oriental Beech (Fagus orientalis Lipsky) Bonded with Polyvinyl Acetate Adhesive. Journal of Adhesion Science and Technology. Vol. 29. Pages. 2521–2536.
• [21] S. Bardak, S. Tiryaki, G. Nemli and A. Aydin (2016). Investigation and Neural Network Prediction of Wood Bonding Quality Based on Pressing Conditions. International Journal of Adhesion and Adhesives. Vol. 68. Pages. 115–123.
• [22] D. Gope, P.C. Gope, A. Thakur and A. Yadav (2015). Application of Artificial Neural Network for Predicting Crack Growth Direction in Multiple Cracks Geometry. Applied Soft Computing. Vol. 30. Pages. 514–528.
• [23] A. Bayram, M. Kankal, G. Tayfur and H. Onsoy (2014). Prediction of suspended sediment concentration from water quality variables. Neural Computing & Applications. Vol 24. Pages. 1079–1087.
• [24] S.A. Kalogirou and M. Bojic (2000). Artificial Neural Networks for the Prediction of the Energy Consumption of a Passive Solar Building. Energy. Vol. 25. Pages. 479–491.
• [25] S.A. Kalogirou (2001). Artificial Neural Networks in Renewable Energy Systems Applications: A Review. Renewable and Sustainable Energy Reviews. Vol. 5. Pages. 373–401.
• [26] BS EN 205 (1991). Test Methods for Wood Adhesives for Non-Structural Applications; Determination of Tensile Bonding Strength of Lap Joints. British Standards, England.
• [27] S.S. Chong, A.R.A. Aziz, S.W. Harun, H. Arof and S. Shamshirband (2015). Application of Multiple Linear Regression, Central Composite Design, and ANFIS Models in Dye Concentration Measurement and Prediction Using Plastic Optical Fiber Sensor. Measurement. Vol. 74. Pages. 78–86.
• [28] J.T. Tsai, M.F. Hou, Y.M. Chen, T. T. H. Wan, H.Y. Kao and H. Y. Shi (2013). Predicting Quality of Life after Breast Cancer Surgery Using ANN-Based Models: Performance Comparison with MR. Support Care Cancer. Vol. 21. Pages. 1341–1350.