Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model

The structural application of plywood boards has increased considerably in recent years. In this context, determining plywood mechanical properties such as bending strength and modulus of elasticity through predictive models using more-easily obtained properties is a very useful tool for in-factory quality control. Artificial neural networks have demonstrated their high capacity for modelling complex relations between variables, considerably improving on results obtained through regression techniques. Four neural networks were developed to obtain these mechanical properties by determining board thickness, moisture content, specific gravity, bending strength and modulus of elasticity of test pieces of small dimensions. The results were compared with those of a regression model and in all cases the results of the present study were better.