Abstract
In this study, pruned vine particles and wood particles in five various proportions were used as the raw material for three-layer particleboards. Primarily, small size sample panels (56x56x2 cm) were manufactured. The physical (thickness swelling (TS), water absorption (WA)), and mechanical (modulus of rupture (MOR), modulus of elasticity (MOE), internal bond (IB)) screw holding (SH) properties of particleboards were determined. Although direct measurement is the most reliable method, it is very complex and time consuming. Also every proportion is not applicable. So that, soft computing methods which are the powerful tools for input-output mapping were preferred. Artificial neural networks (ANNs) were used to estimation. The results show that ANN system capable to predict properties of particleboards in a time and cost effective way.
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