• Xiping Wang Forest Products Laboratory
  • Ed Thomas USDA Forest Service
  • Feng Xu Nanjing Forestry University
  • Yunfei Liu Nanjing Forestry University
  • Brian K Brashaw USDA Forest Service
  • Robert J Ross USDA Forest Service


Acoustic impact testing, laser scanning, board grades, log defects, log segregation, yellow-poplar.


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.

Author Biographies

Ed Thomas, USDA Forest Service

Northern Research Station, Research Computer Scientist

Feng Xu, Nanjing Forestry University

College of Information Science and Technology, PhD Student

Yunfei Liu, Nanjing Forestry University

College of INformation Science and Technology

Brian K Brashaw, USDA Forest Service

Forest Products Laboratory, Program Manager

Robert J Ross, USDA Forest Service

Forest Products Laboratory, Project Leader


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Research Contributions