| Abstract: | AbstractWood, as a natural material, has favourable properties in both technical and aesthetic aspects. Due to its inherent variability, production of high-quality sawn timber demands adequate control of log conversion, which is feasible with computed tomography (CT) log scanning. Existing appearance grading rules for sawn timber might not fully reflect people's visual perception of wood surfaces, and therefore, an alternative, more perception-oriented appearance classification could be beneficial. An appearance classification of sawn timber based on partial least squares discriminant analysis (PLS-DA) of knot-pattern variables was developed and tested. Knot-pattern variables derived from images of board faces were used in training PLS-DA models against an initial classification of the board faces previously established by aid of cluster analysis. Virtual board faces obtained from simulated breakdown of 57 CT-scanned Norway spruce logs were graded according to the developed classification. Visual assessment of the grading results indicated that the classification was largely consistent with human perception of board appearance. An initial estimation of the potential to optimize log rotation, based on CT data, for the established appearance grades was derived from the simulations. Considerable potential to increase the yield of a desired appearance grade, compared to conventional log positioning, was observed. |
