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


Acoustic velocity, board grade, damping ratio, impact test, log defects, dynamic modulus of elasticity, time centroid, 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. This article (Part 1) focused on exploring the potential of an acoustic impact testing method coupled with advanced waveform analysis to detect internal decay of hardwood logs and classify logs in terms of log quality and potential board grade yields. Twenty-one yellow-poplar (Liriodendron tulipifera) logs obtained from the Central Appalachian region were evaluated for internal soundness using an acoustic impact testing technique. These logs were then sawn into boards, and the boards were visually graded based on National Hardwood Lumber Association grading rules. The response signals of the logs from acoustic impact tests were analyzed through moment analysis and continuous wavelet transform to extract time-domain and frequency-domain parameters. The results indicated that the acoustic impact test coupled with wavelet analysis is a viablemethod to evaluate the internal soundness of hardwood logs. Log acoustic velocity alone was able to identify the very low-end logs that have the most severe internal rot or other unsound defects but failed to identify the logs with poor geometry that resulted in very low recovery. Time centroid, damping ratio, and combined time- and frequency-domain parameters were found effective in predicting log quality in terms of board grade yields. Log segregation based on time-domain (time centroid and ρ/Tc**2) and frequency-domain (damping ratio and Ed/ζ**2) parameters showed a positive correlation with the board grade yields.



Author Biographies

Feng Xu, Nanjing Forestry University

College of Information Science and Technology, PhD Student

Xiping Wang, USDA Forest Service

Forest Products Laboratory, Research Forest Products Technologist

Ed Thomas, USDA Forest Service

Northern Research Station, Research Computer Scientist

Yunfei Liu, Nanjing Forestry University

College of Information Science and Technology, Professor

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