Development and modeling of a novel particle mixture for the core configuration of particulate wood composites

Particleboard (PB) is the most utilized reconstituted wood composite panel for furniture production. However, edge smoothness and screw withdrawal resistance (SWR) have been limiting factors in some of its applications. In view of mounting wood and resin costs, this study focused on benchmarking the mechanical properties of Canadian-made furniture grade Ready-To-Assemble MS and M2 particleboard. Of the five plants surveyed, over 80% of the 30 furniture grade particleboards tested were below the ANSI A208.1 standard for edge SWR of 900N. Particle geometry was examined by hydrolyzing samples from the commercial panels to disintegrate them into individual particles which were then characterized in terms of particle length, aspect ratio (AR), and slenderness ratio (SR). Based on maximum likelihood estimation a 2-parameter lognormal distribution function was found to be the best fit for particle length, AR, and SR followed by the 2-parameter Gamma distribution function. AR was found to be the best indicator of edge SWR. A novel particle mixture was formulated by repartitioning the core particles of commercial, as-received PB furnish into core-fine, medium, and coarse particles, which were then remixed in different proportions for the core furnish to fabricate low density panels: the IB strength was 40% higher than the control panels and the edge SWR 18% higher. A response surface model based on a mixture design was developed for macro-voids in the core of simulated particle mats and the macro-voids ratio in pressed panels was also found to increase exponentially with the void fraction in randomly packed loose particle mats. The study concluded that there are too many fine particulates and dust in the core of commercial PB. Increasing the amount of fines in the panel surface by decreasing fine and dust in the panel core through repartitioning the particles into fine, medium, and coarse has the potential of increasing surface smoothness, IB strength, SWR, and consequently reducing density and raw material cost. Keywords: aspect ratio, gamma distribution, lognormal distribution, mixture design, particle, particle distribution, particle mixture, particleboard, random packing, response surface methodology, slenderness ratio, X-ray CT.Forestry, Faculty ofGraduat