Innovative methods of non-destructive evaluation of log quality

Beaulieu, J., Dutilleul, P., 2019: Applications of computed tomography (CT) scanning technology in forest research: a timely update and review. Canadian Journal of Forest Research, 49:1173–1188.10.1139/cjfr-2018-0537Search in Google Scholar

Boero, F., Fedeli, A., Lanini, M., Maffongelli, M., Monleone, R., Pastorino, M. et al., 2018: Microwave tomography for the inspection of wood materials: Imaging system and experimental results. IEEE Transactions on Microwave Theory and Techniques, 66:3497–3510.10.1109/TMTT.2018.2804905Search in Google Scholar

Buksnowitz, C., Müller, U., Evans, R., Teischinger, A., Grabner, M., 2008: The potential of SilviScan’s X–ray diffractometry method for the rapid assessment of spiral grain in softwood, evaluated by goniometric measurements. Wood Science and Technology, 42:95–102.10.1007/s00226-007-0153-6Search in Google Scholar

Carter, P., 2017: Acoustic technology – Enhanced tools for research and operations. In: Proceedings of the 20th International Nondestructive Testing and Evaluation of Wood Symposium – 2017, Madison, WIS, USA, p. 12–15.Search in Google Scholar

Cown, D. J., 1978: Comparison of the Pilodyn and torsio-meter methods for the rapid assessment of wood density in living trees. New Zealand Journal of Forestry Science, 8:384–391.Search in Google Scholar

Downes, G. M., Lausberg, M., 2016: Evaluation of the RESI software tool for the prediction of HM200 within pine logs sourced from multiple sites across New Zealand and Australia. NZ Solid Wood Innov, 15 p.Search in Google Scholar

Faria, J., Martins, T., Ferreira, M., Santos, C., 2008: A computer vision system for color grading wood boards using fuzzy logic. In: IEEE International Symposium on Industrial Electronics – 2008, Cambridge,p. 1082–1087.10.1109/ISIE.2008.4677036Search in Google Scholar

Fuller, A. B., 1990: Microwaves: an introduction to microwave theory and techniques. Pergamon Press, 326 p.Search in Google Scholar

Gao, S., Wang, X., Wiemann, M. C., Brashaw, B. K., Ross, R. J., Wang, L., 2017: A critical analysis of methods for rapid and nondestructive determination of wood density in standing trees. Annals of Forest Science, 74:27.10.1007/s13595-017-0623-4Search in Google Scholar

Gergeľ, T., Bucha, T., Gejdoš, M., Vyhnáliková, Z., 2019: Computed tomography log scanning–high technology for forestry and forest based industry. Central European Forestry Journal, 65:51–59.10.2478/forj-2019-0003Search in Google Scholar

Grundberg, S., Grönlund, A., 1997: Simulated grading of logs with an x–ray log scanner-grading accuracy compared with manual grading. Scandinavian Journal of Forest Research, 12:70–76.10.1080/02827589709355386Search in Google Scholar

Gupta, N. K., Hughes, S. H. C., Isaacson, B., 2004: Tangential CT, a computed tomography method developed for industrial inspection. In: 16th World Conference on Non-Destructive Testing – 2004, Montreal,37:379–384.Search in Google Scholar

Gupta, N. K., 1997: U.S. Patent No. 5,648,996. Washington, DC: U.S. Patent and Trademark Office, 13 p.Search in Google Scholar

Halabe, U. B., Agrawal, S., Gopalakrishnan, B., 2009: Nondestructive evaluation of wooden logs using ground penetrating radar. Nondestructive Testing and Evaluation, 24:329–346.10.1080/10589750802474344Search in Google Scholar

Han, W., Birkeland, R., 1992: Ultrasonic scanning of logs. Industrial metrology, 2:253–281.10.1016/0921-5956(92)80007-GSearch in Google Scholar

Hansson, L., Couceiro, J., Fjellner, B. A., 2017: Estimation of shrinkage coefficients in radial and tangential directions from CT images. Wood Material Science & Engineering, 12:251–256.10.1080/17480272.2016.1249405Search in Google Scholar

Hislop, G., Hellicar, A. D., Li, L., Greene, K., Lewis, C., Meder, R., 2009: Microwave radar for detection of resin defects in Pinus elliottii Engelm var elliottii. Holzforschung, 63:571–574.10.1515/HF.2009.114Search in Google Scholar

Hu, C., Tanaka, C., Ohtani, T., 2004: Locating and identifying sound knots and dead knots on sugi by the rule-based color vision system. Journal of Wood Science, 50:115–122.10.1007/s10086-003-0549-3Search in Google Scholar

Ilic, J., 2001: Relationship among the dynamic and static elastic properties of air-dry Eucalyptus delegatensis R. Baker. Holz als Roh – und Werkstoff, 59:169–175.10.1007/s001070100198Search in Google Scholar

Jacquin, P., Mothe, F., Longuetaud, F., Billard, A., Kerfriden, B., Leban, J. M., 2019: CarDen: a software for fast measurement of wood density on increment cores by CT scanning. Computers and Electronics in Agriculture, 156:606–617.10.1016/j.compag.2018.12.008Search in Google Scholar

Jones, P. D., Schimleck, L. R., Peter, G. F., Daniels, R. F., Clark, A., 2006: Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy. Wood Science and Technology, 40:709–720.10.1007/s00226-006-0085-6Search in Google Scholar

Jol, H. M., 2008: Ground penetrating radar theory and applications. Elsevier, 544 p.Search in Google Scholar

Kasal, B., Drdacky, M., Jirovsky, I., 2003: Semi-destructive methods for evaluation of timber structures. In: WIT Transactions on the Built Environment – 2003, p. 835–842.Search in Google Scholar

Kloiber, M., Drdácký, M., Machado, J. S., Piazza, M., Yamaguchi, N., 2015: Prediction of mechanical properties by means of semi-destructive methods: A review. Construction and Building Materials, 101:1215–1234.10.1016/j.conbuildmat.2015.05.134Search in Google Scholar

Kloppenburg, A., 2018: Density determination of tropical hardwoods with the Resistograph–Diss. Master’s Thesis, Delft University of Technology, Delft, Netherlands, 80 p.Search in Google Scholar

Kowal, J., Karwat, B., Sioma, A., 2012: Using three-dimensional images in the description of environment and biological structures. Polish Journal of Environmental Studies, 21:227–232.Search in Google Scholar

Ku, G., Wang, L. V., 2001: Scanning microwave-induced thermoacoustic tomography: Signal, resolution, and contrast. Medical Physics, 28:4–10.10.1118/1.133340911213921Search in Google Scholar

Lenz, P., Auty, D., Achim, A., Beaulieu, J., Mackay, J., 2013: Genetic improvement of white spruce mechanical wood traits – early screening by means of acoustic velocity. Forests, 4:575–594.10.3390/f4030575Search in Google Scholar

Lindström, H., Reale, M., Grekin, M., 2009: Using nondestructive testing to assess modulus of elasticity of Pinus sylvestris trees. Scandinavian Journal of Forest Research, 24:247–257.10.1080/02827580902758869Search in Google Scholar

Liptai, R. G., Harris, D. O., Tatro, C. A., 1972: An introduction to acoustic emission. In: Acoustic Emission. ASTM International, p. 3–10.10.1520/STP505-EBSearch in Google Scholar

Llana, D. F., Hermoso, E., Bobadilla, I., Iñiguez-Gonzalez, G., 2018: Influence of moisture content on the results of penetration and withdrawal resistance measurements on softwoods. Holzforschung, 72:549–555.10.1515/hf-2017-0133Search in Google Scholar

Longuetaud, F., Mothe, F., Santenoise, P., Diop, N., Dlouha, J., Fournier, M. et al., 2017: Patterns of within-stem variations in wood specific gravity and water content for five temperate tree species. Annals of Forest Science, 74: 64.10.1007/s13595-017-0657-7Search in Google Scholar

McDonald, K. A., 1978: Lumber Defect Detection by Ultrasonics. Department of Agriculture, Forest Service, Forest products laboratory, Medison, WIS., 23 p.Search in Google Scholar

Meaney, P. M., Goodwin, D., Golnabi, A. H., Zhou, T., Pallone, M., Geimer, S. D. et al., 2012: Clinical microwave tomographic imaging of the calcaneus: A first-in-human case study of two subjects. IEEE Transactions on Biomedical Engineering, 59:3304–3313.10.1109/TBME.2012.2209202375925222829363Search in Google Scholar

Muller, W., 2003: Timber girder inspection using ground penetrating radar. Insight-Non-Destructive Testing and Condition Monitoring, 45:809–812.10.1784/insi.45.12.809.52990Search in Google Scholar

Nicolotti, G., Socco, L. V., Martinis, R., Godio, A., Sambuelli, L., 2003: Application and comparison of three tomographic techniques for detection of decay in trees. Journal of Arboriculture, 29:66–78.10.48044/jauf.2003.009Search in Google Scholar

Oja, J., 1997: A comparison between three different methods of measuring knot parameters in Picea abies. Scandinavian Journal of Forest Research, 12:311–315.10.1080/02827589709355415Search in Google Scholar

Österberg, P., 2009: Wood quality and geometry measurements based on cross section images - Diss. Thesis. Tampere University of Technology, Tampere, 192 p.Search in Google Scholar

Pastorino, M., Randazzo, A., Fedeli, A., Salvadè, A., Poretti, S., Maffongelli, M. et al., 2015: A microwave tomographic system for wood characterization in the forest products industry. Wood Material Science & Engineering, 10:75–85.10.1080/17480272.2014.898696Search in Google Scholar

Piazza, M., Riggio, M., 2008: Visual strength-grading and NDT of timber in traditional structures. Journal of Building Appraisal, 3:267–296.10.1057/jba.2008.4Search in Google Scholar

Pirouz, Z., 2015: Defect Detection Technology for Hard-wood Manufacturing, FP Innovations, 85 p.Search in Google Scholar

Rais, A., Ursella, E., Vicario, E., Giudiceandrea, F., 2017: The use of the first industrial X–ray CT scanner increases the lumber recovery value: case study on visually strength-graded Douglas-fir timber. Annals of Forest Science, 74:28.10.1007/s13595-017-0630-5Search in Google Scholar

Riggio, M., Anthony, R. W., Augelli, F., Kasal, B., Lechner, T., Muller, W. et al., 2014: In situ assessment of structural timber using non-destructive techniques. Materials and Structures, 47:749–766.10.1617/s11527-013-0093-6Search in Google Scholar

Rinn, F., Schweingruber, F. H., Schär, E., 1996: Resistograph and X–ray density charts of wood. Comparative evaluation of drill resistance profiles and X–ray density charts of different wood species. Holzforschung, 50:303–311.10.1515/hfsg.1996.50.4.303Search in Google Scholar

Rosenthal, A., Jetzfellner, T., Razansky, D., Ntziachristos, V., 2012: Efficient framework for model-based tomographic image reconstruction using wavelet packets. IEEE Transactions on Medical Imaging, 31:1346–135710.1109/TMI.2012.218791722345528Search in Google Scholar

Ross, R. J., Brashew, B. K., Pellerin, R. F., 1998: Nondestructive evaluation of wood. Forest Products Journal, 48:14.Search in Google Scholar

Ross, R. J., 2015: Nondestructive evaluation of wood: second edition. General Technical Report, Madison, WI: U.S. Forest Service, 169 p.10.2737/FPL-GTR-238Search in Google Scholar

Sandak, J., Tanaka, C., 2005: Evaluation of surface smoothness using a light-sectioning shadow scanner. Journal of Wood Science, 51:270–273.10.1007/s10086-004-0637-zSearch in Google Scholar

Sauter, U. H., Bruechert, F., Straudenmaier, J., 2017: Nondestructive Assessment of Wood Quality throughout Wood Supply Chain and Manufacturing Process In: Wang, X.; Senalik, C. A.; Ross, R. J., (eds.): 20th international nondestructive testing and evaluation of wood symposium. Madison, WI: US Department of Agriculture, Forest Service, Forest Products Laboratory, p. 8–13.Search in Google Scholar

Senalik, C. A., Wacker, J. P., Wang, X., Jalinoos, F., 2016: Assessing the ability of ground-penetrating radar to detect fungal decay in Douglas-fir beams. In: 25th ASNT Research Symposium – 2016, New Orleans, p. 110–116.Search in Google Scholar

Simic, K., Gendvilas, V., O’Reilly, C., Harte, A. M., 2019: Predicting structural timber grade-determining properties using acoustic and density measurements on young Sitka spruce trees and logs. Holzforschung, 73:139–149.10.1515/hf-2018-0073Search in Google Scholar

Sioma, A., 2015: Assessment of wood surface defects based on 3D image analysis. Wood Research, 60:339–350.Search in Google Scholar

Schimleck, L., Dahlen, J., Apiolaza, L. A., Downes, G., Emms, G., Evans, R. et al., 2019: Non-destructive evaluation techniques and what they tell us about wood property variation. Forests, 10:728.10.3390/f10090728Search in Google Scholar

Schimleck, L. R., Evans, R., Matheson, A. C., 2002: Estimation of Pinus radiata D. Don clear wood properties by near-infrared spectroscopy. Journal of Wood Science, 48:132–137.10.1007/BF00767290Search in Google Scholar

Schmoldt, D. L., Occeña, L. G., Lynn Abbott, A., Gupta, N. K., 1998: Nondestructive evaluation of hard-wood logs: CT scanning, machine vision and data utilization. Nondestructive Testing and Evaluation, 15:279–309.10.1080/10589759908952876Search in Google Scholar

Sonka, M., Hlavac, V., Boyle, R., 2014: Image processing, analysis, and machine vision. Cengage Learning, 920 p.Search in Google Scholar

Stängle, S. M., Brüchert, F., Heikkila, A., Usenius, T., Usenius, A., Sauter, U. H., 2015: Potentially increased sawmill yield from hardwoods using X–ray computed tomography for knot detection. Annals of Forest Science, 72:57–65.10.1007/s13595-014-0385-1Search in Google Scholar

Thomas, L., Mili, L., Thomas, E., Shaffer, C. A., 2007: Defect detection on hardwood logs using laser scanning. Wood and Fiber Science, 38:682–695.Search in Google Scholar

Thumm, A., Riddell, M., Nanayakkara, B., Harrington, J., Meder, R., 2010: Near infrared hyperspectral imaging applied to mapping chemical composition in wood samples. Journal of Near Infrared Spectroscopy, 18:507–515.10.1255/jnirs.909Search in Google Scholar

Tiuri, M., Heikkilä, S., 1979: Microwave Instrument for Accurate Moisture Measurement of Timber. In: 9th European Microwave Conference – 1979, Brighton, p. 702–70510.1109/EUMA.1979.332663Search in Google Scholar

Wang, X., Ross, R. J., 2002: Non-destructive Evaluation of Green Materials – Recent Research and Development Activities. In: Nondestructive evaluation of wood. Forest Products Society, Madison, p. 149–171.Search in Google Scholar

Wang, X., Divos, F., Pilon, C., Brashaw, B. K., Ross, R. J., Pellerin, R. F., 2004: Assessment of decay in standing timber using stress wave timing nondestructive evaluation tools. US Department of Agriculture, Forest Products Laboratory, Technical Report, 147 p.10.2737/FPL-GTR-147Search in Google Scholar

Wang, J., Zhao, Z., Song, J., Nie, Z. P., Liu, Q. H., 2013: Reconstruction of microwave absorption of multiple tumors in heterogeneous tissue for microwave-induced thermo-acoustic tomography. Progress, Electromagnetics Research, 32:57–72.10.2528/PIERM13051903Search in Google Scholar

Wen, J., Gao, L., Xiao, X., Xiao, Z., Li, C., 2016: Detection and measurement of internal defects for treetrunk by GPR. International Journal of Simulation: Systems, Science and Technology, 17:9–1.Search in Google Scholar

Zhang, D., He, H., Zong, C., Liu, Y., 2019: Microwave-induced thermoacoustic imaging of wood: a first demonstration. Wood Science and Technology, 53:1223–1234.10.1007/s00226-019-01131-xSearch in Google Scholar

FAKOPP., 2020: Manual for the ArborSonic3D acoustic tomograph. [Brochure], Available at: https://fakopp.com/en/product/arborsonic/Search in Google Scholar

Microtec, 2019: CT Log Computed Tomography for the sawmill of the future. [Brochure], Available at: https://microtec.eu/assets/products/ctlog/MT-CT-Log2.pdfSearch in Google Scholar

Sensors & Software. 2016: pulseEKKO - For the GPR Professional [Brochure], Available at: https://www.sensoft.ca/wp-content/uploads/2016/02/pulseEKKO-Brochure.pdfSearch in Google Scholar

Ward, C., 2014: SilviScan™ rapid wood analysis, CSIROpedia. Available at: https://csiropedia.csiro.au/SilviScan-rapid-wood-analysis/rySearch in Google Scholar