DEFECT DETECTION AND QUALITY ASSESSMENT OF HARDWOOD LOGS:PART 2—COMBINED ACOUSTIC AND LASER SCANNING SYSTEM
Keywords:Acoustic impact testing, laser scanning, board grades, log defects, log segregation, yellow-poplar.
AbstractThe 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.
Bhandarkar S, Faust T, Tang M (1999) Catalog: a system for detection and rendering of internal log defects using computer tomography. Machine Vision and Applications 11(4):171-190.
Carpenter RD, Sonderman DL, Rast ED (1989) Defects in hardwood timber. Agricultural Handbook, No. 678. Washington, DC: U.S. Department of Agriculture, Forest Service. 88 p.
Carter P, Briggs D, Ross RJ, Wang X (2005) Acoustic testing to enhance western forest values and meet customer wood quality needs. PNW-GTR642. USDA Forest Service, Pacific Northwest Research Station, Portland, OR. p 121-129.
Chang S (1992) External and internal defect detection to optimize cutting of hardwood logs and lumber. Transferring Technologies for Industry No. 3. Beltsville, MD: U.S. Department of Agriculture, National Agriculture Library, Beltsville, MD 24 p.
Guddanti S, Chang S (1998) Replicating sawmill sawing with topsaw using CT images of a full length hardwood log. Forest Prod J 48(1):72-75.
Harris P, Petherick R, Andrews M (2002) Acoustic resonance tools. In: Proceedings of the 13th International Symposium on Nondestructive Testing of Wood, August 19-21, 2002, Berkeley, CA. p 195-201.
Li P, Abbott A, Schmoldt D (1996) Automated analysis of CT images for the inspection of hardwood log. Pages 1744-1749 In: Proceedings IEEE Conference on Neural Networks. June 3-6, 1996, Washington, DC.
NHLA (2015) Rules for the measurement and inspection of hardwood and cypress. Memphis, TN: National Hardwood Lumber Association, Memphis, TN. 104 p.
Pellerin RF, DeGroot RC, Esenther GR (1985) Nondestructive
stress wave measurements of decay and termite
attack in experimental wood units. Pages 319-353 in Proc.
th International Symposium on Nondestructive Testing of
Wood, September 9-11, 1985, Pullman, WA. Washington
State University, Pullman, WA.
Rast ED, Sonderman DL, Gammon GL (1973) A guide to
hardwood log grading. General Technical Report NE-1.
U.S. Department of Agriculture, Forest Service, Northeastern
Forest Experiment Station, Upper Darby, PA. 32 pp.
Thomas L, Mili L, Thomas RE, Shaffer CA (2006) Defect
detection on hardwood logs using laser scanning. Wood
Fiber Sci 38(4):682-695.
Thomas LH, Thomas RE (2011) A graphical automated
detection system to locate hardwood log surface defects
using high-resolution three-dimensional laser scan data.
Pages 92-101 in Fei SL, Lhotka JM, Stringer JW, Gottschalk
KW, Miller GW, eds. Proc. 17th Central Hardwood Forest
Conference, April 5-7, 2010, Lexington, KY. General
Technical Report NRS-P-78. U.S. Department of Agriculture,
Forest Service, Northern Research Station, Newtown
Thomas RE (2008) Predicting internal yellow-poplar log
defect features using surface indicators. Wood Fiber Sci
Thomas RE (2013) RAYSAW: A log sawing simulator for
D laser-scanned hardwood logs. Pages 325-334 in Proc.
th Central Hardwood Forest Conference, March 26-28,
, Morgantown, WV. General Technical Report NRSP-
U.S. Department of Agriculture, Forest Service,
Northern Research Station, Newtown Square, PA.
Thomas RE, Thomas L, Shaffer C (2008) Defect detection on
hardwood logs using high-resolution laser scan data. Pages
-167 in Proc. 15th International Symposium on
Nondestructive Testing of Wood, September 10-12, 2007,
Duluth, MN. Natural Resources Research Institute, University
of Minnesota, Duluth, MN.
Thomas RE, Thomas LH (2013) Using parallel computing
methods to improve log surface defect detection
methods. Pages 196-205 in Ross RJ, Wang X, eds. Proc.
th International Nondestructive Testing and Evaluation
of Wood Symposium, September 24-27, 2013, Madison,
WI. General Technical Report FPL-GTR-226. U.S.Department of Agriculture, Forest Service, Forest Products Laboratory, Madison, WI.
Wagner F, Taylor F, Ladd D, McMillin C, Roder F (1989) Ultrafast CT scanning of an oak log for internal defects. Forest Prod J 39(11/12):62-64.
Wang X (2013) Acoustic measurements on trees and logs: a review and analysis. Wood Sci Technol 47:965-975.
Wang X, Carter P, Ross RJ, Brashaw BK (2007) Acoustic assessment of wood quality of raw forest materials – A path to increased profitability. Forest Prod J 57(5):6-14.
Wang X, Divos F, Pilon C, Brashaw BK, Ross RJ, Pellerin RF (2004) Assessment of decay in standing timber using stress wave timing nondestructive evaluation tools—a guide for use and interpretation. Gen. Tech. Rep. FPL-GTR-147. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, Madison, WI. 12 pp.
Wang X, Verrill S, Lowell E, Ross RJ, Herian VL (2013) Acoustic sorting models for improved log segregation. Wood Sci Technol 45(4):343-452.
Xu F, Wang X, Thomas E, Liu Y, Brashaw BK, Ross RJ
(2018) Defect detection and quality assessment of hardwood
logs: Part 1—Acoustic impact test and wavelet
analysis. Wood Fiber Sci (submitted).
The copyright of an article published in Wood and Fiber Science is transferred to the Society of Wood Science and Technology (for U. S. Government employees: to the extent transferable), effective if and when the article is accepted for publication. This transfer grants the Society of Wood Science and Technology permission to republish all or any part of the article in any form, e.g., reprints for sale, microfiche, proceedings, etc. However, the authors reserve the following as set forth in the Copyright Law:
1. All proprietary rights other than copyright, such as patent rights.
2. The right to grant or refuse permission to third parties to republish all or part of the article or translations thereof. In the case of whole articles, such third parties must obtain Society of Wood Science and Technology written permission as well. However, the Society may grant rights with respect to Journal issues as a whole.
3. The right to use all or part of this article in future works of their own, such as lectures, press releases, reviews, text books, or reprint books.