The results indicate that the developed ANN model could predict the bonding strength well with an acceptable error.1 EndNote Introduction for Referencing and Citing EndNote Online Practical exercises Libraries, Research and Learning Resources SeptemberĢ Table of Contents Section Page no. It became clear that the prediction errors were in an acceptable range. The ANN results agreed well with the experimental results. 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 ☌ and 22 min, respectively. To describe the results, FTIR and TGA tests were also conducted.
Then, a model was developed to predict bonding strength using the artificial neural network (ANN) technique. According to that, the glulam specimens were fabricated, the bonding strength of specimens was measured, and the results were statistically analyzed. As a part of the research, an experimental design was determined. 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. 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.
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