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Journal of Materials Science

, Volume 48, Issue 6, pp 2370–2375 | Cite as

Hardness and density profile of surface densified and thermally modified Scots pine in relation to degree of densification

  • Lauri RautkariEmail author
  • Kristiina Laine
  • Andreja Kutnar
  • Sergej Medved
  • Mark Hughes
Article

Abstract

Solid Scots pine (Pinus sylvestris L.) wood was surface densified to three different degrees of compression and hydrothermal post-treatment after surface densification process was performed. The study determined the significance of degree of compression and hydrothermal post-treatment on the density profile generated and the resulting improved Brinell hardness. It was found that different degree of compression resulted in only slightly different density profiles, when compared on the absolute thickness scale. Although the hydrothermal post-treatment lead to an average mass loss of 3.8 % in surface densified specimens regardless of the degree of compression, density profile of surface densified specimens was not significantly affected by hydrothermal post-treatment. Furthermore, the surface densification increased the Brinell hardness more than 90 %. Degree of compression had limited effect, which means that it did not matter if the specimens were compressed from 22 to 15 mm or 18 to 15 mm, the Brinell hardness was approximately the same. The hydrothermal post-treatment did not reduce the Brinell hardness of control and surface densified specimens. Furthermore, the surface densification significantly increased the elastic recovery of surface densified wood, which was measured during hardness measurements and was affected by degree of compression. Additionally, the hydrothermal post-treatment reduced the elastic recovery, in control specimens and surface densified specimens.

Keywords

Density Profile Densified Wood Equilibrium Moisture Content Initial Thickness Elastic Recovery 
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.

Notes

Acknowledgements

The authors would like to thank the COST Action FP0904 for financial support within the frame of Short Term Scientific Mission and Miss Cara Leitch for technical support.

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Lauri Rautkari
    • 1
    • 2
    Email author
  • Kristiina Laine
    • 1
  • Andreja Kutnar
    • 3
    • 4
  • Sergej Medved
    • 5
  • Mark Hughes
    • 1
  1. 1.Department of Forest Products Technology, School of Chemical TechnologyAalto UniversityAaltoFinland
  2. 2.Forest Products Research Institute, Joint Research Institute for Civil and Environmental Engineering, School of Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK
  3. 3.Andrej Marušič InstituteUniversity of PrimorskaKoperSlovenia
  4. 4.ILTRA d.o.o.LjubljanaSlovenia
  5. 5.Department of Wood Science and Technology, Biotechnical FacultyUniversity of LjubljanaLjubljanaSlovenia

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