Advertisement

European Journal of Wood and Wood Products

, Volume 76, Issue 3, pp 925–935 | Cite as

A non-contact method for the determination of fibre direction of European beech wood (Fagus sylvatica L.)

  • Thomas Ehrhart
  • René Steiger
  • Andrea Frangi
Original

Abstract

This paper introduces a non-contact method for the identification, quantification, and documentation of fibre direction of European beech wood (Fagus sylvatica L.). The developed approach is based on an automated visual analysis of the spindle pattern formed by the medullary rays, also termed wood rays. Each spindle is identified by means of image analysis technique, its position and orientation is determined, and the fibre direction of discretised elements is calculated. The individual process steps necessary to obtain an estimate of the fibre direction of a board are explained using the examples of five different failure types. In all examples, the estimated fields of fibre direction are congruent with the actual fibre direction determined by means of (1) the orientation of all present shrinkage cracks, which are established indicators for the fibre direction in wood, and (2) the fracture pattern after tensile testing. Employing the presented approach could open up new possibilities for the characterisation of European beech and other hardwood species with multi-row medullary rays in several fields of application, in particular regarding stress grading.

Notes

Acknowledgements

The authors wish to acknowledge the support of the Swiss Federal Office for the Environment FOEN within the framework of Aktionsplan Holz.

References

  1. Aicher S, Höfflin L, Behrens W (2001) A study on tension strength of finger joints in beech wood laminations. Otto Graf J 12:169–186Google Scholar
  2. Ashby MF et al (1985) The fracture and toughness of woods. In: Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, pp 261–280Google Scholar
  3. Baradit E, Aedo R, Correa J (2006) Knots detection in wood using microwaves. Wood Sci Technol 40(2):.118–123CrossRefGoogle Scholar
  4. Belkacemi M et al. (2016) Wood fiber orientation assessment based on punctual laser beam excitation: a preliminary study. In: Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography (July)Google Scholar
  5. Blaß HJ et al (2005) Biegefestigkeit von Brettschichtholz aus Buche (Bending strength of glued laminated timber made from beech) (In German). In: Karlsruher Berichte zum Ingenieurholzbau—Volume 1. Universitätsverlag Karlsruhe, KarlsruheGoogle Scholar
  6. Brännström M, Manninen J, Oja J (2008) Predicting the strength of sawn wood by tracheid laser scattering. BioResources 3(2):437–451Google Scholar
  7. Briggert A, Olsson A, Oscarsson J (2016) Three-dimensional modelling of knots and pith location in Norway spruce boards using tracheid-effect scanning. Eur J Wood Prod 74(5):725–739CrossRefGoogle Scholar
  8. Clerc G, Volkmer T (2017) Brettschichtholz aus Laubholz - Technische Grundlagen zur Marktimplementierung als Bauprodukt in der Schweiz (Glued laminated timber made from hardwoods - Technical bases for the market implementation as a building product in Switzerland) (In German). Technical report of module 3, Biel, SwitzerlandGoogle Scholar
  9. Cramer SM, McDonald KA (1989) Predicting lumber tensile stiffness and strength with local grain angle measurements and failure analysis. Wood Fiber Sci 21(1):393–410Google Scholar
  10. Daval V, Pot G, Belkacemi M, Meriaudeau F, Collet R (2015) Automatic measurement of wood fiber orientation and knot detection using an optical system based on heating conduction. Opt Express 23(26):33529–33539CrossRefPubMedGoogle Scholar
  11. Denzler JK, Weidenhiller A (2015) Microwave scanning as an additional grading principle for sawn timber. Eur J Wood Prod 73(4):423–431CrossRefGoogle Scholar
  12. DIN 4074–5 (2008) Strength grading of wood—Part 5: sawn hard wood (in German). DIN German Institute for StandardizationGoogle Scholar
  13. Ehrhart T, Fink G, Steiger R, Frangu A (2016a) Experimental investigation of tensile strength and stiffness indicators regarding European beech timber. In: Proceedings of WCTE 2016, Vienna, AustriaGoogle Scholar
  14. Ehrhart T, Fink G, Steiger R, Frangi A (2016b) Strength grading of European beech lamellas for the production of GLT and CLT. In: Proceedings of INTER—Meeting Forty-Nine, Graz, Austria, pp 29–43Google Scholar
  15. EN 1310 (1997) Round and sawn timber—method of measurement of features. CEN European Committee for StandardizationGoogle Scholar
  16. EN 408 (2012) Timber structures—structural timber and glued laminated timber—determination of some phyiscal and mechanical properties. CEN European Committee for StandardizationGoogle Scholar
  17. Federal Office for the Environment FOEN (2014) Swiss Statistical Yearbook of Forestry [Jahrbuch Wald und Holz—Annuaire La forêt et le bois]. UZ Nr. 1420, FOEN, Bern (in German and French)Google Scholar
  18. Foley C (2001) A three-dimensional paradigm of fiber orientation in timber. Wood Sci Technol 35(5):453–465CrossRefGoogle Scholar
  19. Frühmann K, Burgert I, Stanzl-Tschegg SE, Tschegg EK (2003) Mode I fracture behaviour on the growth ring scale and cellular level of spruce (Picea abies [L.] Karst.) and beech (Fagus sylvatica L.) loaded in the TR crack propagation system. Holzforschung 57(6):653–660CrossRefGoogle Scholar
  20. Frühwald K (2004) Laubholz im Bauwesen und seine Festigkeitssortierung (Strength grading of hardwoods for the building sector) (In German). In: VI. Sympozium DREVO V STAVEBNYCH KONSTRUKCIACH, Kocovce, Slovakia, pp 105–118Google Scholar
  21. Frühwald K, Schickhofer G (2005) Strength grading of hardwoods. In: Proceedings of the 14th international symposium on nondestructive testing of wood, Hanover, GermanyGoogle Scholar
  22. Glos P, Denzler JK (2004) Strength and stiffness behaviour of beech laminations for high strength glulam. In: Görlacher R (ed) CIB—meeting thirty-seven. Edinburgh, Scotland, p CIB-W18/37-6-3Google Scholar
  23. Krapez J-C, Gardette G, Balageas DL (1996) Thermal ellipsometry in steady-state and by lock-in thermography: application to anisotropic materials characterization. In: Proceedings of quantitative infrared thermography QIRT ‘96 Eurotherm Seminar 50, pp. 257–262Google Scholar
  24. Lukacevic M, Füssl J (2014) Numerical simulation tool for wooden boards with a physically based approach to identify structural failure. Eur J Wood Prod 72(4):497–508CrossRefGoogle Scholar
  25. Matthews PC, Beech BH (1976) US Patent 3,976,384: method and apparatus for detecting timber defectsGoogle Scholar
  26. Matthews PC, Soest JF (1986) US Patent 4,606,64: method for determining localized fiber angle in a three dimensional fibrous materialGoogle Scholar
  27. Metcalfe L, Dashner B (2002) US Patent 2002/0025061A1: high speed and reliable determination of lumber quality using grain influenced distortion effectsGoogle Scholar
  28. Norimoto M, Yamada T (1972) The dielectric properties of wood VI: on the dielectric properties of the chemical constituents of wood and the dielectric anisotropy of wood. Wood Res Bull Wood Res Inst Kyoto Univ 52:31–43Google Scholar
  29. Norton JAP, McLaughlan TA, Kusec DJ (1974) US Patent 3,805,156: wood slope of grain indicatorGoogle Scholar
  30. Nyström J (2003) Automatic measurement of fiber orientation in softwoods by using the tracheid effect. Comput Electron Agric 41(1–3):91–99CrossRefGoogle Scholar
  31. Olsson A, Oscarsson J (2017) Strength grading based on high resolution laser scanning and dynamic exication: a full scale investigation of performance. Eur J Wood Prod 75(1):17–31CrossRefGoogle Scholar
  32. Olsson A, Oscarsson J, Serrano E, Källsner B, Johansson M, Enquist B (2013) Prediction of timber bending strength and in-member cross-sectional stiffness variation on the basis of local wood fibre orientation. Eur J Wood Prod 71(3):319–333CrossRefGoogle Scholar
  33. Sarén MP, Serimaa R, Tolonen Y (2006) Determination of fiber orientation in Norway spruce using X-ray diffraction and laser scattering. Eur J Wood Prod 64(3):183–188CrossRefGoogle Scholar
  34. Sauter UH, Breinig L (2016) European Hardwoods for the Building Sector Reality of today—possibilities for tomorrow: WP 1: Hardwood resources in Europe Standing stock and resource forecasts. Workshop Garmisch-PartenkirchenGoogle Scholar
  35. Schlotzhauer P et al. (2016) Machine grain angle determination on spruce, beech, and oak lumber for construction use. In: Proceedings of WCTE 2016, Vienna, AustriaGoogle Scholar
  36. Simonaho SP, Palviainen J, Tolonen Y, Silvennoinen R (2004) Determination of wood grain direction from laser light scattering pattern. Opt Lasers Eng 41(1):.95–103CrossRefGoogle Scholar
  37. Soest JF (1997) US Patent 5,703,960: lumber defect scanning including multi-dimensional pattern recognitionGoogle Scholar
  38. Steele PH, Neal SC, McDonald SM (1991) The slope-of-grain indicator for defect detection in unplaned hardwood lumber. For Prod J 41(1):15–20Google Scholar
  39. Viguier J, Jehl A, Collet R, Bleron L, Meriaudeau F (2015) Improving strength grading of timber by grain angle measurement and mechanical modeling. Wood Mater Sci Eng 10(1):145–156CrossRefGoogle Scholar
  40. Viguier J, Bourreau D, Bocquet J-F, Pot G, Bleron L, Lanvin J-D (2017) Modelling mechanical properties of spruce and Douglas fir timber by means of X-ray and grain angle measurements for strength grading purpose. Eur J Wood Prod 75(4):527–541CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Institute of Structural EngineeringETH ZürichZurichSwitzerland
  2. 2.Structural Engineering Research LaboratoryEmpa-Materials Science and TechnologyDubendorfSwitzerland

Personalised recommendations