Predictive Models for Modulus of Rupture and Modulus of Elasticity of Particleboard Manufactured in Different Pressing Conditions

Abstract

Determining the mechanical properties of particleboard has gained a great importance due to its increasing usage as a building material in recent years. This study aims to develop artificial neural network (ANN) and multiple linear regression (MLR) models for predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of particleboard depending on different pressing temperature, pressing time, pressing pressure and resin type. Experimental results indicated that the increased pressing temperature, time and pressure in manufacturing process generally improved the mechanical properties of particleboard. It was also seen that ANN and MLR models were highly successful in predicting the MOR and MOE of particleboard under given conditions. On the other hand, a comparison between ANN and MLR revealed that the ANN was superior compared to the MLR in predicting the MOR and MOE. Finally, the findings of this study are expected to provide beneficial insights for practitioners to better understand usability of such composite materials for engineering applications and to better assess the effects of pressing conditions on the MOR and MOE of particleboard.

Keywords

• model comparison
• modulus of elasticity
• modulus of rupture
• particleboard
• prediction
• wood