Prediction of Retention Level and Mechanical Strength of Plywood Treated With Fire Retardant Chemicals by Artificial Neural Networks
Abstract
The treatment with fire retardant chemicals is the most effective process to protect wood and wood based products from fire is. Therefore, use of fire retardant chemicals has been increased. However, the fire retardant chemicals have an effect on other physical, mechanical and some technological properties of the materials treated with them. In this study, firstly, the retention level prediction model was developed with the artificial neural network (ANN) to examine the effects of wood species and concentration aqueous solution on the retention levels of veneers. Then, the effects of wood species, concentration aqueous solution and retention level on the mechanical properties of plywood were investigated with the mechanical strength prediction model developed with ANN. The prediction models with the best performance were determined by statistical and graphical comparisons. It has been observed that ANN models yielded very satisfactory results with acceptable deviations. As a result, the findings of this study could be employed effectively into the forest products industry to reduce time, energy and cost for empirical investigations.
Keywords
Artiical Neural Network, Fire Retardant, Plywood, Concentration, Retention Level, Mechanical Properties
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