There are many log and wood properties of interest to wood processors. There is also high variability in important attributes between and within growing regions and between and within individual stems which influence financial returns to wood processors. This review summarises recent studies of segregation technologies and techniques which have shown that:
regional or stand level attribute models will facilitate a coarse level of segregation but not account well for the between and within stem variation;
many tools and techniques are available for segregating wood based on internal properties but few have been implemented commercially. Some are better suited for application in mills than in forests;
the benefits of segregating stands, stems and logs based on wood properties are not clear due to high variability in wood properties, poor market signals (in terms of price) for wood with superior properties, and poor understanding of the costs across the value chain; and
most of the existing economic models tend to look at the economics of segregation from the perspective of a single participant in the value chain, e.g. a structural mill or a central processing yard. Only a few models look across the value chain and these have limitations often poorly representing some participants in the value chain.
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. 相似文献
