EVALUATION OF MECHANICAL AND FLAME RETARDANT PROPERTIES OF MEDIUM DENSITY FIBERBOARD USING ARTIFICIAL NEURAL NETWORK

Abstract

The present study presents the application of artificial neural network (ANN) to predict the modulus of rupture (MOR) and mass loss (ML) of the fiberboard fire retarded by different fire retarding chemicals pressed at different temperatures and bonded with UF resin. In the experimental study, the effect of adding the fire retarding agents including boric acid, borax and ammonium sulfate was evaluated on MOR and ML of fiberboard manufactured at different press temperatures. At first, the experimental design was created based on the Response Surface Methodology, and then evaluated by determining the significance of the independent variables on the responses through ANOVA test. It was determined that the positive effects of increasing press temperatures moderated the negative effects of adding fire retarding agents. However, ML decreases more at the same time. ANN results have shown a good agreement with experimental results. It was shown that the prediction error was in an acceptable range. The results indicated that the developed ANN model can predict the MOR and ML of the fiberboard sufficiently with an acceptable accuracy. Therefore, the desirable outputs of MOR and ML can be obtained by performing fewer experiments, using less time and cost as the proposed model is applied.