The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board

Abstract

In this study, an artificial neural network (ANN) approach was employed for modeling the moisture absorption (MA) and thickness swelling (TS) properties of oriented strand board (OSB) in various applications. A series of ANN models were developed for the analysis and prediction of correlations between processing parameters and MA and TS of OSB. An ANN model was found for modeling the effects of OSB treatment variables on the MA and TS. The required data for training and testing of the model were obtained from the experimental results of Salay (2010). In designing this model, the MA and TS of the OSB were determined using OSB treatment variables, including board layup type, resin type, application rate of resin, and wax content. When experimental data and results obtained from the ANN were compared by regression analysis using Matlab, it was determined that both groups of data (test and train) were consistent. It was demonstrated that the well-trained feed forward and back propagation multilayer ANN model is a powerful and sufficient tool for the prediction of MA and TS; therefore, by using ANN outputs, satisfactory results can be estimated, rather than measured and hence time and cost are reduced in all the required experimental activities.