The objective of this study was to determine the technical feasibility of combining acoustic wave data with high-resolution laser scanning data to improve the accuracy of defect detection and quality assessment in hardwood logs. Using acoustic impact testing and high-resolution laser scanning techniques, 21 yellow poplar logs (Liriodendron tulipifera) obtained from the central Appalachian region were evaluated for internal and external defects. These logs were then sawn into boards and the boards were visually graded based on the National Hardwood Lumber Association grading rules. The response signals of the logs from acoustic impact testing were analyzed to extract time-domain and frequency-domain parameters. The laser scan data of each log was processed by a defect detection system. The results indicated that acoustic velocity, time centroid, damping ratio, and the combined time- and frequency-domain parameters are all effective quality predictors of the hardwood logs in terms of internal soundness. High-resolution laser scanning is complementary to acoustic impact testing. Acoustic parameters combined with laser scanning results provide a more complete data picture of the log: size, shape, surface defects, and degree of soundness. Indications of soundness in a particular log allow the internal prediction system to flag suspicious defects as potentially unsound. Thus, a combined system would be able to discriminate much more precisely with respect to log quality and potential board grade yields than would either method independently.
Bibliographical noteFunding Information:
This project was conducted under the cooperative research agreement (14-JV-11111133-089) between the Natural Resources Research Institute of the University of Minnesota Duluth and the USDA Forest Service, Forest Products Laboratory. Mr. Feng Xu’s participation in this project was supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions and the Nanjing Forestry University Innovation Grant for Outstanding PhD Dissertations (grant no. 163070682). We thank Neal Bennett and Deborah Conner for their technical assistance during the project.
- Acoustic impact testing
- Board grades
- Laser scanning
- Log defects
- Log segregation
- Yellow poplar