A possible application for an industrial computed tomography scanner in a sawmill is finding an optimal rotational position of logs with respect to knots and outer shape. Since a computed tomography scanner is a great investment, it is important to investigate potential profitability of such an investment for different production strategies. The objective of this study was to investigate the potential value increase of the sawn timber of Norway spruce (Picea abies (L.) Karst.) by rotating logs to their optimum position prior to sawing compared with sawing all logs in horns down position. The production strategy evaluated by log breakdown simulation in this case study was to produce strength graded timber of the center boards, while the side boards were appearance graded. This case study showed an average value increase with respect to the value of center boards, side boards and chips of 11 %.

With the development of scanning technology in sawmills, it is possible to optimise log rotational position when sawing. However, choosing a different rotational position than horns down might be detrimental for the board shape after drying, especially for curved logs. Thus, there is a need to investigate at what level of log curve it is possible to freely rotate logs without causing board warp. This study was carried out through a test sawing that was conducted at a sawmill situated in the middle of Sweden. The tests were made on 177 Norway spruce logs, with varying amount of curve. Half of the logs were sawn in the horns-down position, half were sawn rotated perpendicular to horns down. Log shape and warp of the dried boards were measured. The results indicated a relationship between board spring, log curve and choice of rotational position. Furthermore, board bow was related to log curve but not rotational position. It can be concluded that for straight logs, with a bow height of less than 15 mm, an unconventional rotational position does not cause excess spring in the boards. Bow and twist are not affected by the rotational position at all.

In this paper, a method for utilizing knot information from computed tomography (CT) scanning of Scots pine (Pinus sylvestris L.) logs was evaluated. A high speed industrial CT scanner is being developed, which will enable scanning of logs in sawmills at production speed. This development calls for the ability to optimize breakdown parameters in a quick manner because there are many decisions to be made and the timeframe for these production decisions is short. One of the important breakdown parameters is in which rotational position to saw a log. The presented method used CT data to create a two-dimensional projection of knot information from a log, in order to minimize the amount of data to analyze. The center of mass of the knot projection relative to the center of the sawing pattern was chosen as the rotational position of the log. The aim was to put large knots on the flat surfaces of the boards, as knots on edge surfaces have a more negative effect on board quality in the sorting rules used in this study. The method was tested by sawing simulation and was compared with the industrial praxis of sawing logs horns down. The results show an increase in board quality and value, albeit for a selected group of Scots pine logs. The method is very sensitive to positioning errors, but it has some potential if sawlog positioning accuracy is improved.

High speed industrial computed tomography (CT) scanning of sawlogs is new to the sawmill industry and therefore there are no properly evaluated algorithms for detecting knots in such images. This article presents an algorithm that detects knots in CT images of logs by segmenting the knots with variable thresholds on cylindrical shells of the CT images. The knots are fitted to ellipses and matched between several cylindrical shells. Parameterized knots are constructed using regression models from the matched knot ellipses. The algorithm was tested on a variety of Scandinavian Scots pine (. Pinus sylvestris L.) and Norway spruce (. Picea abies (L.) Karst.) with a knot detection rate of 88-94% and generating about 1% falsely detected knots.

This paper describes how the image processing technique known as template matching performs when used to recognize boards of Scots pine (Pinus sylvestris L.). Recognition of boards enables tracking of individual boards through an industrial process, which is vital for process optimization.

A dataset of 886 Scots pine board images were used as a database to match against. The proposed board recognition method was evaluated by rescanning 44 of the boards and matching these to the larger dataset. Three different template matching algorithms have been investigated while reducing the pixel densities of the board images (downsampling the images). Furthermore, the effect of variations in board length has been tested and the computational speed of the recognition with respect to the database size has been measured. Tests were conducted using the open source software package OpenCV due to its highly optimized code which is essential for applications with high production speed.

The conducted tests resulted in recognition rates above 99% for board lengths down to 1 m and pixel densities down to 0.06 pixels/mm. This study concluded that template matching is a good choice for recognition of wooden board surfaces.

In the wood industry, there is a wish to recognize and track wood products through production chains. Traceability would facilitate improved process control and extraction of quality measures of various production steps. In this paper, a novel wood surface recognition system that uses scale and rotationally invariant feature descriptors called K-plets is described and evaluated. The idea behind these descriptors is to use information of how knots are positioned in relation to each other. The performance and robustness of the proposed system were tested on 212 wood panel images with varying levels of normally distributed errors applied to the knot positions. The results showed that the proposed method is able to successfully identify 99–100 % of all panel images with knot positional error levels that can be expected in practical applications.

This study reports experimental data of airflow distribution as a function of fan speed in an industrial batch kiln. Measurements were conducted with 20 hot-film anemometers distributed throughout the load at two occasions. The main result was that airflow distribution did not change significantly as the fan speed was reduced, and no positions where the air movement stopped were found. It was also found that relatively more air ran in the bolster spaces in comparison to the adjacent packages as the air ran through the load.