Application of the Artificial Neural Network to Predict the Bending Strength of the Engineered Laminated Wood Produced Using the Hydrolyzed Soy Protein-Melamine Urea Formaldehyde Copolymer Adhesive

Abstract

The artificial neural network (ANN) was used to predict the modulus of rupture (MOR) of the laminated wood products adhered by melamine/urea formaldehyde (MUF) resin with different formaldehyde to melamine/urea molar ratios combined with different weight ratios of the protein adhesive resulting from the alkaline treatment (NaOH) of the soybean oil meal to MUF resin pressed at different temperatures according to the central composite design (CCD). After making the boards and performing the mechanical test to measure the MOR, based on experimental data, different statistics such as determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and sum of squares error (SSE) were determined, and then the suitable algorithm was selected to determine the estimated values. After comparing estimated values with the experimental values, the direct and interactive effects of the independent variables on MOR were determined. The results indicated that using suitable algorithms to train the ANN well, a very good estimate of the bending strength of the laminated wood products can be offered with the least error. In addition, based on the estimated and measured strengths and FTIR and TGA diagnostic analyses, it was found that the replacement of the MUF resin by the protein bio-based adhesive when using low F to M/U molar ratios, the MOR is maximized if a high range of temperature is used during the press.