Thirty full-length sample trees from the B.C. Interior were selected for a study to determine whether external log characteristics can predict internal log quality. The sample trees were also used to create 3-dimensional log images for sawmill simulation purposes. "LogSaw", a simulation tool with internal log defect detection capabilities, was used to explore the extent to which internal and external log quality information can improve log breakdown optimization. A model of a hypothetical sawmill producing lumber for the standard North American dimension market was created to study how lumber value recovery depends on different sawing optimization scenarios.
Three sawing optimization scenarios using different levels of knowledge of internal log defects were compared to currently used sawing optimization technique:
Ideal sawing optimization - all defects within log interior are known.
Sawing optimization using only the knowledge of surface knots.
Sawing optimization using log rotation instructions based on zones of least external knot density.
Simulation results have shown that it is worthwhile to “look into the log”. When compared with the current optimization technique, the sawing optimization, including the full knowledge of log interior, has increased the value recovery by 6.2%. When only the surface knots were projected into the log interior and included in the optimization, the value recovery had increased by 4.3%. Even this 4.3% increase is still a big improvement because this sawing optimization could be implemented using currently available scanning technologies and optimization software enhanced to include log surface knots. The scenario of using log rotation instructions based on predicted zones of least internal knot density did not show value recovery improvement.
Including surface knots in the log breakdown optimization has considerably increased sawmill revenue; the hypothetical sawmill considered in this study, processing 400,000 m3 of log per year, has increased its revenue by $2.2 million.