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


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.


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


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.).


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.


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.


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.


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



Modulus of elasticity


Moisture content


Indicating property


Coefficient of variation


Coefficient of determination


Standard error of estimate


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


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


Resonance frequency of board corresponding to first axial mode of vibration


Average density of board at the time of vibrational test


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


Axial dynamic MoE of board


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


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


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


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


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


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


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 based on data of fibre orientation of two wide faces of board


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


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


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



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


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.


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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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