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Customer adapted grading of Scots pine sawn timber using a multivariate method
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
2015 (English)In: Scandinavian Journal of Forest Research, ISSN 0282-7581, E-ISSN 1651-1891, Vol. 30, no 1, p. 87-97Article in journal (Refereed) Published
Abstract [en]

To define new grading rules, or to customize the ones in use in a rule-based automatic grading system of boards, is a time-consuming job for a sawmill engineer. This has the effect that changes are rarely made. The objective of this study was to continue the development of a method that replaces the calibration of grading rule settings by a holistic-subjective automatic grading, using multivariate models. The objective was also to investigate if this approach can improve sawmill profitability and at the same time have a satisfied customer. For the study, 323 Scots pine (Pinus sylvestris L.) boards were manually graded according to preferences of an important customer. That is, a customer that regularly purchases significant volumes of sawn timber. This manual grading was seen as reference grading in this work. The same boards were also scanned and graded by a rule-based automatic grading system, calibrated for the same customer. Multivariate models for prediction of board grade based on aggregated knot variables, obtained from the scanning, were calibrated using partial least squares regression. The results show that prediction of board grades by the multivariate models were more correct, with respect to the manual grading, than the grading by the rule-based automatic grading system. The prediction of board grades based on multivariate models resulted in 76-87% of the boards graded correctly, according to the manual grading, while the corresponding number was 63% for the rule-based automatic grading system.

Place, publisher, year, edition, pages
2015. Vol. 30, no 1, p. 87-97
National Category
Other Mechanical Engineering
Research subject
Wood Science and Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-12739DOI: 10.1080/02827581.2014.968359ISI: 000349118400008Scopus ID: 2-s2.0-84926255283Local ID: be6cca49-b5fa-4769-b182-a4dfe5d69400OAI: oai:DiVA.org:ltu-12739DiVA, id: diva2:985690
Note

Validerad; 2015; Nivå 2; 20140804 (bendar)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Berglund, AndersBroman, OlofOja, JohanGrönlund, Anders

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