A1 Journal article (refereed), original research

Modelling internal knot distribution using external log features


Open Access hybrid publication

Publication Details
Authors: Zolotarev Fedor, Eerola Tuomas, Lensu Lasse, Kälviäinen Heikki, Helin Tapio, Haario Heikki, Kauppi Tomi, Heikkinen Jere
Publisher: Elsevier
Publication year: 2020
Language: English
Related Journal or Series Information: Computers and Electronics in Agriculture
Volume number: 179
ISSN: 0168-1699
JUFO-Level of this publication: 2
Open Access: Open Access hybrid publication

Abstract

The quality of the end product in the sawmill industry is highly dependent on the distribution of knots. If the internal structure of the logs used as raw material were known it would be possible to optimize the sawing process by controlling the locations of individual knots in the resulting boards. Methods such as Computer Tomography or Magnetic Resonance Imaging can be used to gain reliable information about the internal structure of the logs prior to sawing. Such scanners are, however, expensive or slow, which renders the use of these methods ill-suited to online integration in sawmill operations. Laser range scanners, on the other hand, are very fast but provide only external information. A method to estimate the internal log structure using only external information provided by a laser range scanner is proposed in this paper. The method comprises five major steps: point cloud filtering and centreline estimation, log surface heightmap generation, knot segmentation, volumetric reconstruction of knots and virtual sawing. The end result is the generation of images of virtual boards for the given sawing parameters. The method was evaluated on debarked softwood logs. The experimental part of the work demonstrates that the pixel intensities of the virtual boards generated using the proposed method correlate well with the probabilities of knots appearing in the corresponding locations in the final product. Virtual sawing allows exhaustive evaluation of different sawing parameters, thus making it possible to optimize sawing parameters prior to sawing.


Last updated on 2020-30-11 at 10:45