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

Year 2016, Volume: 4 Issue: Special Issue-1, 153 - 157, 26.12.2016

Sebahattin Tiryaki , Selahattin Bardak , Aytaç Aydın

Abstract

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.

Keywords

Neural network, Bonding strength, Prediction, Wood, Soaking

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