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Annals of Forest Science

, 75:102 | Cite as

Performance of strength grading methods based on fibre orientation and axial resonance frequency applied to Norway spruce (Picea abies L.), Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) and European oak (Quercus petraea (Matt.) Liebl./Quercus robur L.)

  • Anders Olsson
  • Guillaume Pot
  • Joffrey Viguier
  • Younes Faydi
  • Jan Oscarsson
Research Paper

Abstract

Key message

Machine strength grading of sawn timber is an important value adding process for the sawmilling industry. By utilizing data of local fibre orientation on timber surfaces, obtained from laser scanning, more accurate prediction of bending strength can be obtained compared to if only axial vibratory measurements are performed. However, the degree of improvement depends on wood species and on board dimensions. It is shown that a model based on a combination of fibre orientation scanning and axial vibratory measurement is very effective for Norway spruce ( Picea abies L.) and Douglas fir ( Pseudotsuga menziesii (Mirb.) Franco). For European oak ( Quercus petraea (Matt.) Liebl./ Quercus robur L.) boards of narrow dimensions, axial vibratory measurements are ineffective whereas satisfactory results are achieved using a model based on fibre orientation.

Context

Machine strength grading of sawn timber is an important value adding process for the sawmilling industry.

Aims

The purpose of this paper has been to compare the accuracy of several indicating properties (IPs) to bending strength when applied to Norway spruce (Picea abies L.), Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) and European oak (Quercus petraea (Matt.) Liebl./Quercus robur L.).

Methods

The IPs were determined for a set of data comprising scanned high-resolution information of fibre orientation on board surfaces, axial resonance frequency, mass and board dimensions.

Results

Whereas dynamic axial modulus of elasticity (MoE) gave good prediction of bending strength of Norway spruce (R2 = 0.58) and Douglas fir (R2 = 0.47), it did not for narrow dimension boards of oak (R2 = 0.22). An IP based on fibre orientation gave, however, good prediction of bending strength for all three species and an IP considering both dynamic axial MoE and local fibre orientation for prediction of bending strength gave very good accuracy for all species (Norway spruce R2 = 0.72, Douglas fir R2 = 0.62, oak R2 = 0.59). Comparisons of results also showed that scanning of fibre orientation on all four sides of boards resulted in more accurate grading compared to when only the two wide faces were scanned.

Conclusion

Data of local fibre orientation on wood surfaces give basis for accurate machine strength grading. For structural size timber of Norway spruce and Douglas fir, excellent grading accuracy was achieved combining such data with data from vibratory measurements. The improvements achieved enable substantial increase of yield in high-strength classes.

Keywords

Grain angle Fibre direction Tracheid effect Structural timber Longitudinal vibrations Grade determining property 

Abbreviations

MoE

Modulus of elasticity

MC

Moisture content

IP

Indicating property

CoV

Coefficient of variation

R2

Coefficient of determination

SEE

Standard error of estimate

u

MC determined according to EN 13183-1 at the time of four point quasi-static bending test

up

MC determined using pin-type moisture metre at the time of vibrational test

f

Resonance frequency of board corresponding to first axial mode of vibration

ρ

Average density of board at the time of vibrational test

ρ12%

Average density of board adjusted to 12% MC (adjusted on the basis of up)

Ea

Axial dynamic MoE of board

Ea,12%

Ea adjusted to 12% MC (on the basis of up)

Da,12%

Board property corresponding to Ea,12%, but determined disregarding ρ

Em,g

Global bending MoE, determined by four-point quasi-static bending test

Em,g,12%

Em,g adjusted to 12% MC (on the basis of u)

fm

Bending strength of board, determined by four-point quasi-static bending test

fm,h

fm, adjusted to a reference size, namely board depth, h, of 150 mm

Eb,90,nom

Lowest bending MoE along board, valid for a moving span of 90 mm, determined on the basis of calculation utilizing data of fibre orientation and nominal values of material parameters

Eb,90,nom,2-side

Eb,90,nom based on data of fibre orientation of two wide faces of board

Eb,90,nom,4-side

Eb,90,nom based on data of fibre orientation of four faces of board

IPE2E

IP to fm,h based on linear regression combining Eb,90,nom,2-side and Ea,12% as predictor variables

IPE4E

IP to fm,h based on linear regression combining Eb,90,nom,4-side and Ea,12% as predictor variables

IPE2 ρ

IP to fm,h based on linear regression combining Eb,90,nom,2-side and ρ,12% as predictor variables

IPE2 ρ

IP to fm,h based on linear regression combining Eb,90,nom,4-side and ρ,12% as predictor variables

IPD2 ρ

IP to fm,h based on linear regression combining Eb,90,nom,2-side and D,12% as predictor variables

IPD2 ρ

IP to fm,h based on linear regression combining Eb,90,nom,4-side and D,12% as predictor variables

Notes

Acknowledgements

We thank Robert Collet from Arts et Metiers who was the fundraiser and leader of the projects from which data was collected.

Funding

The data used in this study was obtained thanks to several sources of funding: funding from the regional council of Bourgogne Franche-Comté and Carnot ARTS institute; funding from the French National Research Agency through the ANR CLAMEB project (ANR-11-RMNP-0015). The cooperation between the research teams was funded by Arts et Metiers.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. ALSC (2014) List of approved machines. American Lumber Standard Committee, Germantown http://www.alsc.org/untreated_machinegraded_mod.htm. Accessed 14 March 2018Google Scholar
  2. Bechtel FK (1985) Beam stiffness as a function of point-wise E, with application to machine stress rating. In: Proceedings of International Symposium on Forest Products Research, CSIR, Pretoria, South Africa. p 22–26Google Scholar
  3. Blass HJ, Gard W (1994) Machine strength grading of timber. In: Proceedings of the Pacific Timber Engineering Conference, Gold Coast, Australia, 11˗15 July 1994Google Scholar
  4. Boughton G (1994) Superior sorting of timber using localized stiffness on edge. In: Proceedings of the Pacific Timber Engineering Conference, Gold Coast, Australia, 11–15 July 1994Google Scholar
  5. Brancheriau L, Paradis S, Baillères H (2007) Bing: beam identification by non-destructive grading (Cirad). http://ur-biowooeb.cirad.fr/en/products/bing/what-is-it. Accessed 6 September 2018.  https://doi.org/10.18167/62696E67
  6. Corder SE (1965) Localized deflection related to bending strength of lumber. In: Proceedings of the Second Symposium on Nondestructive Testing of Wood, Spokane, WA, USAGoogle Scholar
  7. 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 23:33529–33539.  https://doi.org/10.1364/OE.23.033529 CrossRefPubMedGoogle Scholar
  8. Dinwoodie JM (2000) Timber: its nature and behaviour. E & FN Spon. New Fetter Lane, LondonCrossRefGoogle Scholar
  9. EN 13183-1 (2003+AC:2004) Moisture content of a piece of sawn timber—part 1: determination by oven dry methodGoogle Scholar
  10. EN 14081-2 (2010+A1:2012) Timber structures—strength graded structural timber with rectangular cross section—part 2: machine grading; additional requirements for initial type testingGoogle Scholar
  11. EN 338 (2016) Structural timber—strength classesGoogle Scholar
  12. EN 384 (2016) Structural timber—determination of characteristic values of mechanical properties and densitiesGoogle Scholar
  13. EN 408 (2010+A1:2012) Timber structures—structural timber and glued laminated timber—determination of some physical and mechanical propertiesGoogle Scholar
  14. Epaud F (2007) De la charpente romane à la charpente gothique en Normandie (from roman to gothic carpentry in Normandy). Publications du CRAHM, CaenGoogle Scholar
  15. Faydi Y (2017) Mechanical grading of oak wood using vibrational and grain angle measurements. Doctoral dissertation, Arts et Metiers ParisTech, Cluny, FranceGoogle Scholar
  16. Faydi Y, Brancheriau L, Pot G, Collet R (2017) Prediction of oak wood mechanical properties based on the statistical exploitation of vibrational response. BioResources 12(3):5913–5927.  https://doi.org/10.15376/biores.12.3.5913-5927 CrossRefGoogle Scholar
  17. Foschi RO (1987) A procedure for the determination of localized modulus of elasticity. Holz Roh Werkst 45:257–260Google Scholar
  18. Galligan WL, McDonald KA (2000) Machine grading of lumber—practical concerns for lumber producers. Forest Products Laboratory, General Technical Report FPL-GTR-7, Madison, WI, USAGoogle Scholar
  19. Görlacher R (1990) Klassifizierung von Brettschichtholzlamellen durch Messung von Longitudinalschwingungen (classification of glulam laminations by measurement of longitudinal vibrations). Dissertation.Versuchsanstalt für Stahl, Holz und Steine der Universität Fridericiana in Karlsruhe, GermanyGoogle Scholar
  20. Guitard D (1987) Mécanique du matériau bois et composites (Mechanics of the wood material and composites). Cepadues, ToulouseGoogle Scholar
  21. Hanhijärvi A, Ranta-Maunus A (2008) Development of strength grading of timber using combined measurement techniques. Report of the Combigrade project—phase 2. VTT Publication 686Google Scholar
  22. Hatayama Y (1984) A new estimation of structural lumber considering the slope of the grain around knots. Bulletin of the Forestry and Forest Products Research Institute, Japan, 326:69–167Google Scholar
  23. Hoffmeyer P (ed.) (1995) Styrkesortering ger mervärde, Del 2 – Tillgængelig teknik (Strength grading adds value, Part 2 – Available technique). Laboratoriet for Byningsmaterialer, Danmarks Tekniske Universitet, Teknisk Rapport 335–1995, ISSN 0908–3871 (in Danish, Norwegian and Swedish)Google Scholar
  24. Hu M (2018) Studies on the fibre direction and local bending stiffness of Norway spruce timber. Doctoral dissertation, Linnaeus University, Växjö, SwedenGoogle Scholar
  25. IGN (2017) French National Forest Inventory. http://inventaire-forestier.ign.fr/. Accessed 20 Oct 2017
  26. Jehl A (2012) Modeling of lumber’s mechanical behavior using x-rays densitometry and laser imaging. Doctoral dissertation, Arts et Metiers ParisTech, Cluny, FranceGoogle Scholar
  27. Kass AJ (1975) Middle ordinate method measures stiffness variation within pieces of lumber. For Prod J 25:33–41Google Scholar
  28. Kollman FFP, Côté WA (1968) Principles of wood science and technology. Springer Verlag, Berlin HeidelbergCrossRefGoogle Scholar
  29. Lukacevic M, Füssl J, Eberhardsteiner J (2015) Discussion of common and new indicating properties for the strength grading of wooden boards. Wood Sci Technol 49:551–576.  https://doi.org/10.1007/s00226-015-0712-1 CrossRefGoogle Scholar
  30. Matthews PC, Beech BH (1976) Method and apparatus for detecting timber defects. U.S. Patent 3976384Google Scholar
  31. NF EN 975-1 (2009) Sawn timber—appearance grading of hardwoods—part 1: Oak and beech. AFNOR, ParisGoogle Scholar
  32. Oh JK, Shim K, Kim KM, Lee JJ (2009) Quantification of knots in dimension lumber using a single-pass X-ray radiation. J Wood Sci 55:264–272.  https://doi.org/10.1007/s10086-009-1031-7 CrossRefGoogle Scholar
  33. Ohlsson S, Perstorper M (1992) Elastic wood properties from dynamic tests and computer modeling. J Struct Eng 118:2677–2690.  https://doi.org/10.1061/(ASCE)0733-9445(1992)118:10(2677) CrossRefGoogle Scholar
  34. Olsson A (2016) Determination of sawn timber properties using laser scanning—development potentials and industrial applications. In: Proceedings of WCTE 2016, World Conference on Timber Engineering, Vienna, Austria, 22–25 August 2016Google Scholar
  35. Olsson A, Oscarsson J (2017) Strength grading on the basis of high resolution laser scanning and dynamic excitation: a full scale investigation of performance. Eur J Wood Wood Prod 75:17–31.  https://doi.org/10.1007/s00107-016-1102-6 CrossRefGoogle Scholar
  36. 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 Wood Prod 71:319–333.  https://doi.org/10.1007/s00107-013-0684-5 CrossRefGoogle Scholar
  37. Oscarsson J, Olsson A, Enquist B (2014) Localized modulus of elasticity in timber and its significance for the accuracy of machine strength grading. Wood Fiber Sci 46:489–501Google Scholar
  38. Rellstab C, Bühler A, Graf R, Folly C, Gugerli F (2016) Using joint multivariate analyses of leaf morphology and molecular-genetic markers for taxon identification in three hybridizing European white oak species (Quercus spp.). Ann For Sci 73:669–679.  https://doi.org/10.1007/s13595-016-0552-7 CrossRefGoogle Scholar
  39. Schlotzhauer P, Wilhelms F, Lux C, Bollmus S (2018) Comparisons of three systems for automatic grain angle determination on European hardwood for construction use. Eur J Wood Wood Prod 76:911–923.  https://doi.org/10.1007/s00107-018-1286-z CrossRefGoogle Scholar
  40. Schoch W, Heller I, Schweingruber FH, Kienast F (2004) Wood anatomy of central European species. http://www.woodanatomy.ch. Accessed 10 March 2018
  41. Stängle SM, Brüchert F, Heikkilä A, Usenius T, Usenius A, Sauter UA (2015) Potentially increased sawmill yield from hardwoods using X-ray computed tomography for knot detection. Ann For Sci 72:57–65.  https://doi.org/10.1007/s13595-014-0385-1 CrossRefGoogle Scholar
  42. Viguier J (2015) Classement mécanique des bois de structure. Prise en compte des singularités dans la modélisation du comportement mécanique (Timber grading. Modeling of the mechanical behavior using defects). Doctoral dissertation, Université de Lorraine, FranceGoogle Scholar
  43. 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 10:145–156.  https://doi.org/10.1080/17480272.2014.951071 CrossRefGoogle Scholar
  44. Viguier J, Bourreau D, Bocquet JF, Pot G, Bléron L, Lanvin JD (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 Wood Prod 75:527–541.  https://doi.org/10.1007/s00107-016-1149-4 CrossRefGoogle Scholar
  45. WoodEye (2016) A method and device for evaluating a wooden board (Förfarande och anordning för utvärdering av en träplanka). European Patent EP 2 823 298 B1, 28 December 2016Google Scholar
  46. Zhou J, Shen J (2003) Ellipse detection and phase demodulation for wood grain orientation measurement based on the tracheid effect. Opt Lasers Eng 39:73–89.  https://doi.org/10.1016/S0143-8166(02)00041-6 CrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Building TechnologyLinnaeus UniversityVäxjöSweden
  2. 2.LaBoMaPArts & MétiersClunyFrance

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