Combining acoustic and laser scanning methods to improve hardwood log segregation

The objective of this research was to examine the technical feasibility of combining acoustic wave data with high-resolution laser scanning data to improve accuracy of hardwood log defect detection and segregation. Using acoustic impact testing and high-resolution laser scanning techniques, 21 yellowpoplar logs 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 National Hardwood Lumber Association grading rules. The response signals of the logs from acoustic impact testing were analyzed through moment analysis and continuous wavelet transform to extract time domain and frequency domain parameters. The results indicated that acoustic velocity, time centroid, damping ratio, as well as the combined time and frequency domain parameters are all effective quality predictors for segregating low-end logs. Acoustic data combined with high-resolution laser scanning data provide a more complete picture of the log in terms of 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 exactly with respect to log quality and potential lumber recovery than either method independently.

[1]  Robert J. Ross,et al.  Proceedings: 19th International Nondestructive Testing and Evaluation of Wood Symposium , 2015 .

[2]  R. Edward. Thomas,et al.  Using parallel computing methods to improve log surface defect detection methods , 2013 .

[3]  Robert J. Ross,et al.  Acoustic testing to enhance western forest values and meet customer wood quality needs , 2005 .

[4]  S. Guddanti,et al.  Replicating sawmill sawing with TOPSAW using CT images of a full-length hardwood log , 1998 .

[5]  R. Edward Thomas.  Predicting Internal Yellow-Poplar Log Defect Features Using Surface Indicators , 2008 .

[6]  Clifford A. Shaffer,et al.  Defect detection in hardwood logs using high resolution laser scan data , 2008 .

[7]  Roy F. Pellerin,et al.  Nondestructive stress wave measurements of decay and termite attack in experimental wood units , 1986 .

[8]  R. Edward. Thomas.  RAYSAW: a log sawing simulator for 3D laser-scanned hardwood logs , 2013 .

[9]  Everette D. Rast,et al.  A Guide to Hardwood Log Grading , 2017 .

[10]  Xiping Wang,et al.  Acoustic measurements on trees and logs: a review and analysis , 2013, Wood Science and Technology.

[11]  R. Schreyer,et al.  THE DEPARTMENT OF AGRICULTURE. , 1901, Science.

[12]  Robert J. Ross,et al.  Acoustic assessment of wood quality of raw forest materials - A path to increased profitability , 2007 .

[13]  Charles W. McMillin,et al.  Ultrafast CT scanning of an oak log for internal defects , 1989 .

[14]  Clifford A. Shaffer,et al.  DEFECT DETECTION ON HARDWOOD LOGS USING LASER SCANNING , 2007 .

[15]  R. Edward. Thomas,et al.  A GRAPHICAL AUTOMATED DETECTION SYSTEM TO LOCATE HARDWOOD LOG SURFACE DEFECTS USING HIGH-RESOLUTION THREE-DIMENSIONAL LASER SCAN DATA , 2011 .

[16]  Robert J. Ross,et al.  Acoustic sorting models for improved log segregation , 2013 .

[17]  Everette D. Rast,et al.  Defects in hardwood timber , 1989 .

[18]  Daniel L. Schmoldt,et al.  Automated analysis of CT images for the inspection of hardwood logs , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[19]  Suchendra M. Bhandarkar,et al.  Machine Vision and Applications c ○ Springer-Verlag 1999 CATALOG: a system for detection and rendering of internal log defects using computer tomography , 1997 .