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In this study, water absorption and thickness swelling values of medium density fi berboard (MDF) were modelled by artifi cial neural networks (ANN). MDF panels were produced with different rates of paraffi n (0.0-control, 0.5, 1 and 1.5 %) at different press temperatures (170 and 190 °C). After conditioning of MDF, water absorption (WA) and thickness swelling (TS) of samples were carried out at specifi c intervals within 24 hours. Then, the data obtained from these experiment were modelled using ANN. Paraffi n addition rate, press temperature and immersion time in water were used as the input parameters, while WA and TS values of MDF were used as the output parameters. After training of ANN, it was found that correlation coeffi cients (R) were close to 1 for training, validation, test and all data set. Mean absolute percentage error (MAPE) and mean square error (MSE) were determined as 2.94 % and 0.57, respectively, for all data sets. As a result of this study, the use of proposed ANN model may be recommended to predict the water absorption and thickness swelling of panels instead of complex and time-consuming studies such as empirical formulas.

Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF