Segregation of Eucalyptus dunnii logs using acoustics

Abstract

Wood properties at harvest-age vary enormously both within and among Eucalyptus trees, leading to a wide range in sawlog quality. One key property determining value of sawn boards is wood stiffness, with boards being allocated to different strength classes and priced accordingly. To maximise returns from sawing, it is highly desirable to be able to sort logs, either in the field or log yard, to ensure that only the higher quality logs are sawn. A non-destructive testing method for assessing wood stiffness, sound flight velocity, was evaluated as a direct measure of wood stiffness using two age classes (9- and 25-year-old) of Eucalyptus dunnii. Trees were measured before and after harvest with a non-destructive acoustic device (FAKOPP stress wave timer), sawn, and all boards tested acoustically prior to and after drying and reconditioning. A subset of boards was then put through a stress-grading machine to determine stiffness, and later tested for wood hardness. Wood samples were also collected from the logs to determine basic density, and relationships between stiffness, hardness, wood density and sound flight time in trees and logs were determined. The speed of sound along logs was sufficiently closely correlated with wood stiffness to allow logs to be sorted into stiffness classes. A highly significant and positive relationship was found for acoustic measurements made on logs while a weaker, but still significant, relationship existed for acoustic measurements made on standing trees. For the sawn boards, sound flight velocity in both green and kiln dried boards was strongly correlated with both stiffness and wood hardness. Stiffness was significantly positively correlated with wood hardness at both ages in both logs and boards, indicating that segregation based on increasing stiffness would lead to improvements in hardness. This study identified the need for further work on the methodology and hardware required for the segregation of Eucalyptus logs to optimise value recovery.