Holz als Roh- und Werkstoff

, Volume 64, Issue 1, pp 30–36 | Cite as

Simulated and realised industrial yields in sawing of maritime pine (Pinus pinaster Ait.)

  • Isabel PintoEmail author
  • Sofia Knapic
  • Helena Pereira
  • Arto Usenius


A sawing simulation software was evaluated by comparison with a real situation in the industrial sawing of maritime pine (Pinus pinaster Ait.) stems. At an operational sawmill sawing yields were measured for the conversion of logs with two sawing patterns: production of boards and production of boards and lumber. The simulation used the WoodCIM® optimisation software with similar sawing set-ups and dimensionally matching virtual logs obtained from cross cutting of 3D mathematical reconstructions of maritime pine stems. The virtual maritime pine stems and the sawing simulation software showed potential to evaluate the impact of raw material and process characteristics on the production performance. The simulated sawing yields corresponded closely to the industrial yields for the production of boards (57% volume yield). For production of lumber and boards, the simulation allowed to obtain a higher volume (45% vs. 53%). The negative impact of resin production on the sawing yields was estimated by comparing the industrial yields of resin tapped trees with matching virtual logs and showed a loss of 11% sawn wood volume, increasing with log diameter.


Industrial Yield Forest Prod Resin Production Resin Pocket Sawing Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Simulierte und tatsächliche Schnittholzausbeute beim industriellen Einschnitt von Seekiefern (Pinus pinaster Ait.)


Eine Simulationssoftware zur Einschnittoptimierung wurde durch den Vergleich mit einem realen Einschnitt von Seekiefernstämmen (Pinus pinaster Ait.) bewertet. Die mit zwei verschiedenen Schnittbildern – Einschnitt von Brettern sowie Einschnitt von Brettern und Bauholz – in einem Sägewerk erzielten Schnittholzausbeuten wurden gemessen.

Für die Simulation wurde die WoodCIM® Optimierungssoftware benutzt. Dabei wurden ähnliche Schnittbilder und Abschnitte mit gleichen Durchmessern verwendet. Diese wurden aus virtuell modellierten Seekiefernstämmen erzeugt.

Die virtuellen Stämme sowie die Simulationssoftware zeigten die Möglichkeit, den Einfluss des Rohmaterials sowie der Prozessbedingungen auf die Schnittholzproduktion zu bewerten.

Die Ausbeuten für den reinen Bretteinschnitt aus der Simulation stimmen genau mit den in der Praxis ermittelten Ausbeuten überein (57% Volumenausbeute). Bei der gleichzeitigen Produktion von Bauholz und Brettern ergibt sich aus der Simulation eine höhere Ausbeute (53% statt 45%).

Der negative Einfluss der Harzgewinnung auf die Schnittholzausbeute wurde durch den Vergleich der in der Praxis erzielten Schnittholzausbeute von zur Harzgewinnung genutzten Bäumen mit der von entsprechenden virtuellen Bäumen geschätzt. Dabei zeigte sich ein Ausbeuteverlust von 11%, der sich mit steigendem Stammdurchmesser vergrösserte.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Björklund L (1999) Identifying heartwood-rich stands or stems of Pinus sylvestris by using inventory data. Silva Fenn 33(2):119–129Google Scholar
  2. 2.
    Chiorescu S, Grönlund A (2000) Validation of a CT-based simulator against a sawmill yield. Forest Prod J 50(6):69–76Google Scholar
  3. 3.
    Grundberg S (1999) An X-ray LogScanner – a tool for control of the sawmill process. Division of Wood Technology, Luleå University of Technology, Doctoral thesis, Skellefteå, 30 pagesGoogle Scholar
  4. 4.
    Hallock H, Stern AR, Lewis DW (1978) Is there a “best” sawing method? Res Pap FPL-280. USDA Forest Serv, Forest Prod Lab, 11 pagesGoogle Scholar
  5. 5.
    Ikonen V-P, Kellomäki S, Peltola H (2003) Linking tree stem properties of Scots pine (Pinus sylvestris L) to sawn timber properties through simulated sawing. Forest Ecol Manag 174:251–263CrossRefGoogle Scholar
  6. 6.
    Knapic S, Gloria A, Pereira H (2004) Analysis of production yields in the sawmill industry. A Inst Sup Agron 49:223–241 (In Portuguese)Google Scholar
  7. 7.
    Leban JM, Duchanois G (1990) SIMQUA: A simulation software for wood quality. Ann Sci Forest 47:483–493CrossRefGoogle Scholar
  8. 8.
    Maness TC, Lin Y (1995) The influence of sawkerf and target size reductions on sawmill revenue and volume recovery. Forest Prod J 45(11/12):43–50Google Scholar
  9. 9.
    Oja J (1997) Measuring knots and resin pockets in CT-images of Norway spruce. Division of Wood Technology, University of Technology, Luleå, Licenciate thesis, Skellefteå, 6 pagesGoogle Scholar
  10. 10.
    Pinto I, Pereira H, Usenius A (2002) Sawing simulation of Pinus pinaster Ait. In: Nepveu G (ed) Proc of 4th workshop in “Connection between Silviculture and wood quality through modelling approaches and simulation softwares”. British Columbia, INRA, NancyGoogle Scholar
  11. 11.
    Pinto I, Pereira H, Usenius A (2003) Analysis of log shape and internal knots in twenty maritime pine (Pinus pinaster Ait) stems based on visual scanning and computer aided reconstruction. Ann For Sci 60:137–144CrossRefGoogle Scholar
  12. 12.
    Pinto I, Pereira H, Usenius A (2004) Heartwood and Sapwood development in maritime pine (Pinus pinaster Ait) stems. Trees 18(3):284–294Google Scholar
  13. 13.
    Pinto I, Usenius A, Song T, Pereira H (2005) Sawing simulation of maritime pine (Pinus pinaster Ait) stems for production of heartwood containing components. Forest Prod J 55(4):88–96Google Scholar
  14. 14.
    Richards DB (1973) Hardwood lumber yield by various simulated sawing methods. Forest Prod J 23(10):50–58Google Scholar
  15. 15.
    Schmoldt D, Li P, Araman P (1996) Interactive simulation of hardwood log veneer slicing using CT images. Forest Prod J 46(4):41–47Google Scholar
  16. 16.
    Song T (1987) Optimization of sawing decision making through computer simulation. Laboratory of mechanical wood technology, Helsinki University of Technology, Licenciate thesis, Espoo, 109 pagesGoogle Scholar
  17. 17.
    Song T (1998) Tree stem construction model for “Improved spruce timber utilisation” project. VTTs Building Technology internal report. Helsinki, 20 pagesGoogle Scholar
  18. 18.
    Todoroki CL (1994) Effect of edging and docking methods on volume and grade recoveries in the simulated production of flitches. Ann Sci Forest 51:241–248CrossRefGoogle Scholar
  19. 19.
    Todoroki CL (1996) Developments of the sawing simulation software AUTOSAW – linking wood properties, sawing and lumber end-use. In: Nepveu G (ed) Proc of the 2nd Workshop on Connection BeTween Silviculture and Wood Quality through Modelling Approaches and Simulation Softwares. South Africa, INRA, Nancy, pp 241–247Google Scholar
  20. 20.
    Usenius A (1980) Model for simulation of sawing setup. Doctoral thesis, VTT, Technical centre of Finland, 178 pages (in Finnish)Google Scholar
  21. 21.
    Usenius A (1999) Wood conversion chain optimisation. In: Nepveu G (ed) Proc of 3rd workshop on Connection between Silviculture and Wood Quality through Modelling Approaches and Simulation Softwares. La Londe-Les-Maures. INRA, Nancy, 542–548 pagesGoogle Scholar
  22. 22.
    Usenius A (2000) WoodCIM® – Integrated planning and optimizing system for sawmilling industry. VTTs Building Technology internal report, 8 pagesGoogle Scholar
  23. 23.
    Usenius A, K.O. Sommardahl KO, Song T (1989) Project in optimal use of wood raw material. Final internal report, VTT, Technical Centre of Finland, 209 pages (in Finnish)Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Isabel Pinto
    • 1
    Email author
  • Sofia Knapic
    • 2
  • Helena Pereira
    • 2
  • Arto Usenius
    • 1
  1. 1.VTT Building and TransportVTTFinland
  2. 2.Centro de Estudos FlorestaisInstituto Superior de AgronomiaLisboaPortugal

Personalised recommendations