For new structural applications and greater efficiency in the use of lumber in traditional applications, we’re required to predict the strength of lumber to assign grades more accurately. The presence of growth characteristics such as knots, shakes and the slope of gain, influence that strength. Sawmills can with modern scanning technology assess lumber online at the rate of one every few seconds and so in principle can predict the strength of each piece. Despite that technology, potential modern machine grading is still done based on models developed long ago for human graders. An important advance was made recently by Seong-Hwan Jun, a former UBC Statistics PhD student working with engineers and wood scientists at FPInnovations, a local industrial laboratory. Until his graduation, Seong was a member of the Forest Products Stochastic Modelling Group (FPSMG) jointly funded by NSERC and FPInnovations. The outcome of that work was a methodology for detecting knots from lumber scans. In particular, Seong and his collaborators at UBC and FPInnovations developed an elaborate library of computational tools for implementing that methodology. However, due to time constraints, that library of programs could not be documented for the community of potential users; so, the goal of the project to be described by the speaker, is the first steps toward the documentation of that library, a project which is being done in consultation with Dr. Jun.
The talk will provide a background for the work being done by the Grading Enhancement Research Subgroup of the FPSMG along with illustrative examples. Work on the project started with a lengthy review of Canada’s forest products industry, lumber manufacturing, and the methods used to classify lumber into grades. Next, we had to learn about image processing and the associated software for doing so. The ultimate goal involved processing a laser light scattered image, consisting of a succession of spots running across a board at high resolution. We first explored the images of those tracheid laser dots by using java+imageJ software. Then we fitted an ellipse to each of these dots and extracted out their shape (namely, minor and major axes) and the rotation angle. These dots were in turn clustered to form larger ellipses that, roughly speaking, provides on each surface an outline of the knot face as it expresses itself on that surface of the board. After fitting the ellipses, we get a .csv file labelled tracheids.csv that can be used for knot identification and matching. By using the eccentricities and sines of rotation of the angle plots of the individual spots of laser dot images, it is possible to give a conclusion about whether a knot is present or absent of the board. A 3-dimensional plot then stitches the four surfaces of board together so that their position in the original (real) coordinate is preserved. Current work is underway by another research subgroup to use these images to predict lumber strength.