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Comparing predictability of board strength between computed tomography, discrete X-ray, and 3D scanning of Norway spruce logs
RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Hållbar Samhällsbyggnad. Luleå University of Technology, Sweden.
Luleå University of Technology, Sweden.
RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Hållbar Samhällsbyggnad.
2016 (English)In: Wood Material Science & Engineering, ISSN 1748-0272, E-ISSN 1748-0280, Vol. 11, no 2, p. 116-125Article in journal (Refereed) Published
Abstract [en]

Strength graded boards of Norway spruce (Picea abies (L.) Karst.) are important products for many Scandinavian sawmills. If the bending strength of the produced boards can be predicted before sawing the logs, the raw material can be used more efficiently. In previous studies it is shown that the bending strength can be predicted to some extent using discrete X-ray scanning of logs. In this study, we have evaluated if it is possible to predict bending strength of Norway spruce boards with higher accuracy using computed tomography (CT) scanning of logs compared to a combination of discrete X-ray and 3D scanning. The method was to construct multivariate models of bending strength for three different board dimensions. Our results showed that CT scanning of logs produces better models of bending strength compared to a combination of discrete X-ray and 3D scanning. The main reason for this difference was the benefit of knowing the position of where the boards were cut from the logs and therefore detailed knot information could be used in the prediction models. Due to the small number of observations in this study, care should be taken when comparing the resulting prediction models to results from other studies.

Place, publisher, year, edition, pages
Taylor & Francis, 2016. Vol. 11, no 2, p. 116-125
Keywords [en]
Bending strength, Multivariate, Partial least squares, Picea abies, Sawn timber, Wood
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-410DOI: 10.1080/17480272.2015.1022875OAI: oai:DiVA.org:ri-410DiVA, id: diva2:941896
Available from: 2016-06-23 Created: 2016-06-23 Last updated: 2019-06-12Bibliographically approved

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Publisher's full texthttp://www.tandfonline.com/doi/pdf/10.1080/17480272.2015.1022875
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CiteExportLink to record
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