Optimal Modified Starch Content in UF Resin for Glulam Based on Bonding Strength Using Artificial Neural Network

Abstract

The purpose of this study was to present an application of the artificial neural network (ANN) that predicts the bonding strength of glulam manufactured from plane tree (Platanus orientalis L.) wood layers adhered with a combination of modified starch adhesive and UF resin. Bonding strength was measured at different weight ratios containing different values of nano-zinc oxide as an additive under different conditions of press temperature and press time. As a part of the research, an experimental design was determined. According to that, the glulam specimens were fabricated, the bonding strength of specimens was measured, and the results were statistically analyzed. Then, a model was developed to predict bonding strength using the artificial neural network (ANN) technique. To describe the results, FTIR and TGA tests were also conducted. The experimental results show that the maximum bonding strength values were obtained when the WR was at the middle level (50%), nano-zinc oxide content was at a maximum (4%), and press temperature and press time were fixed at 200 °C and 22 min, respectively. The ANN results agreed well with the experimental results. It became clear that the prediction errors were in an acceptable range. The results indicate that the developed ANN model could predict the bonding strength well with an acceptable error.