Abstract Plywood is one of the most important wood based composites with its more than 81 million m3 production and a market value of approximately 19 billion dollars in exports and imports in 2010. Bonding strength test is crucial for plywood panels, because it classifies boards as suitable/unsuitable for using areas. The most important factors affecting the bonding strength of plywood are wood species, adhesive type, wood density, veneer peeling temperature, veneer drying temperature and relative moisture content. Determining the optimum plywood manufacturing factors without any loss in bonding strength is also very important. However, a lot of values for these manufacturing parameters need to be tested to reach the optimum panel properties that cause losing much time, energy and cost. The aim of this study was to design an ANN capable of predicting the optimum manufacturing parameters without spending much time and loss bonding strength. For this aim; bonding shear strength values of plywood panels manufactured from the scots pine, maritime pine and European black pine veneers peeled at 32 °C and 50 °C and dried at 110 °C, 140 °C and 160 °C temperatures were obtained by experimental study, then it was predicted the intermediate bond strength values based on veneer peeling and drying temperatures by artificial neural network modeling. the optimum peeling and drying temperature ranges giving the highest bonding strength values were determined as 48–50 °C and 154–160 °C, respectively for panels with phenol formaldehyde and 32–50 °C and 110–124 °C for panels with melamine urea formaldehyde adhesive in scots pine. For maritime pine plywood panels, the optimum temperature ranges were 49–50 °C and 138–160 °C for phenol formaldehyde adhesive while the ranges were 38–50 °C and 110–125 °C for panels with melamine urea formaldehyde adhesive. The optimum temperature ranges for European black pine plywood panels were 32 °C and 126–123 °C for panels with PF adhesive and 32–50 °C and 110–112 °C for panels with MUF adhesive.
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