Recently developed technology in sawmills such as advanced log scanning and traceability concepts enable new ways of grading logs and boards. When it comes to strength grading, this is often done on sawn boards using automatic scanning systems. However, if board scanners were to be augmented with data from log scanners by using traceability, more information on the wood propertiesis available. In this study, the main objective was to compare the strength prediction capability of board scanning alone, to board scanning augmented with X-ray and 3D data from log scanning, for Norway spruce (Picea abies L. Karst.) and Scots pine (Pinus sylvestris L.). In that case, data from three different scanning systems  was combined, two for logs and one for boards. A further objective was to investigate whether pre-sorting logs for strength grading can be done using either 3D log data alone, or 3D log data augmented with X-ray data. The results show an improved strength prediction when adding log data to board data, and that 3D log data alone is not enough to pre-sort logs for strength, while adding X-ray log data makes it possible. Strength  prediction on Scots pine performed somewhat better than prediction on Norway spruce.

Metrology and characterisation of products' functional surfaces are of key importance in smart and sustainable manufacturing. By proper measures of resulting topography and dimension at the micro-cm level, higher process control can be achieved, leading to more efficient production with products closer to defined targets. Commercial surface metrology systems for lab- and in/on-line applications have increased in the last decades, but the wood sector has not yet benefited from this development. A better understanding of sawn wood topography combined with smart online metrology systems is expected to lead to a substantial reduction of waste in sawmill production, both by transforming waste pieces and sideboards into engineered wood products and by optimising the sawing process (e.g. by using thinner saw blades and reduced tolerances). It would also open new design possibilities and challenge the construction sector to replace today’s materials with renewable raw materials. Additionally, sawmills will be less dependent on incoming timber dimensions. This study is the first step towards a better understanding of sawn wood topography and how relevant surface features can be detected and analysed to enable the next generation of functional wood surfaces for various applications. By identifying the measuring instrument’s capability to capture surface topographical features of sawn wood, this paper discusses the requirements for efficient measurement techniques. It opens for future implementation of machine learning algorithms to in-line monitor and control the machining process. All tested metrology techniques showed promising results. To capture machining marks, the instrumentation needs to have lateral resolutions on the um level and a measurement area covering some cm; thus, the laser scanning system seemed to be a good compromise.

While X-ray computed tomography (CT) is used extensively in sawmills, its primary applications are improving value-yield and process automation. Three-dimensional CT scanners have also been extensively used for log sorting and sawing optimisation. However, there is hardly any resent research utilising CT from a cutting process perspective. This study addresses this gap by adopting CT to investigate the correlation between local wood properties — namely density, knots and annual rings — and cutting forces. Cutting forces for clear-wood and knotty-wood specimens of Scots pine (Pinus sylvestris L.) followed the expected trend corresponding to the density profile: dense regions led to high cutting force while other regions caused low cutting forces. However, it was found that in addition to density, knot orientation relative to cutting direction, annual ring width along the cutting path, and annual ring angle in relation to the cutting direction were critical factors that influence the cutting process. This study illustrates the feasibility of coupling local wood properties obtained from CT data with cutting force, potentially inspiring further research on a variety of wood property/cutting force relationships.

Sawmill managers are interested in carrying out the sawmilling process in an efficient manner with minimal waste of raw material. In primary processing, an increase in productivity can be achieved if the process can be controlled. This can be achieved by developing a macro-mechanic model of the cutting power determination considered from a point of view of modern fracture mechanics has been developed. For the model knowledge about fracture toughness and shear yield stresses is vital. Pine wood (Pinus sylvestris L.) is a popular species which is widely used in wood industry in Poland and Sweden. Test samples with a cross section of 70 × 70 mm were originated from the both countries. Before sawing, all wood samples were scanned on X-ray Computed Tomography in laboratory conditions in Skellefteå (Sweden). These CT scans were helpful in indicating where clear and knotty samples were obtained from beams. Cutting tests were carried out on the custom-made laboratory stand in Skellefteå. The rotating arm held the wood sample, and the cutting speed equaled 15.5 ms−1. A stellite tipped tooth was fed into the wood sample with uncut chip thicknesses for clear wood 0.5; 0.7 and 1.1 mm, and for knotty wood 0.3; 0.5 and 0.7 mm. The tooth data: overall set equal to 2.9 mm, rake angle 27°, and clearance angle 12°. A piezoelectric sensor on the tooth holder measured the cutting forces. On the basis of the experimental findings it is possible to create mathematical models cutting forces for clear and knotty pine wood. For knotty wood the intercept is almost twice than for clear wood.

We present a procedural framework for modeling the annual ring pattern of solid wood with knots. Although wood texturing is a well-studied topic, there have been few previous attempts at modeling knots inside the wood texture. Our method takes the skeletal structure of a tree log as input and produces a three-dimensional scalar field representing the time of added growth, which defines the volumetric annual ring pattern. First, separate fields are computed around each strand of the skeleton, i.e., the stem and each knot. The strands are then merged into a single field using smooth minimums. We further suggest techniques for controlling the smooth minimum to adjust the balance of smoothness and reproduce the distortion effects observed around dead knots. Our method is implemented as a shader program running on a GPU with computation times of approximately 0.5 s per image and an input data size of 600 KB. We present rendered images of solid wood from pine and spruce as well as plywood and cross-laminated timber (CLT). Our results were evaluated by wood experts, who confirmed the plausibility of the rendered annual ring patterns.

Timber utilisation and wood construction are fundamental components of Europe's strategy on progressing towards a sustainable bio-based economy. Both components require optimised strength grading procedures in Europe's sawmills. To further improve the current procedures, quality assessment and segregation of the raw material need to start already at the roundwood stage. Furthermore, the quality information should be made accessible throughout the production process to leverage the full potential for optimisation. This paper discusses the current state of research on combined log and board strength grading with a focus on the situation in Europe. It highlights limitations due to technology and current standardisation and identifies knowledge gaps and research opportunities. The European research project READiStrength (resource-efficient and data-driven integrated log and board strength grading) has been initiated to address these issues.

Using thinner sawblades in sawmills has a potential for both increasing volume yield and decreasing energy usage in the sawing process; possible secondary effects of this are reduced outtake from forests or at least a better use of what is harvested, lower greenhouse gas emissions and higher productivity. How much the sawing kerf can be reduced depends on several factors, such as maintaining the stability of the sawblade, feeding accuracy and so on. This study deals with the possible volume yield increases when reducing the sawing kerf, based on different possible scenarios of which some are more realistic to achieve in the near future than others. The study is based on simulated sawing of 1464 Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H. Karst.) logs. Tests were made for both circular sawblades as well as for band saws, deployed in a cant sawing process with a primary and a secondary saw. Both variable sawing patterns as well as fixed sawing patterns was investigated. In the first case, the choice of sawing pattern was optimized based on volume yield, while in the latter case, logs were presorted on top diameter into log classes that were each assigned a specific sawing pattern. The results show an increased yield of about 1.3 to 1.5 percent units per mm reduced sawblade thickness, and a maximum theoretical yield of 57-59 % for a 0 mm saw kerf. The latter is of course not possible to achieve in practice, but it hints at the maximum potential of reducing the sawblade thickness. In the near future, it is expected that a kerf reduction of around 1 mm is possible. This would thus result in a possible yield increase of 1.3-1.5 percent units, with further increases depending on improvements in sawblade design, sawblade stabilization, improved materials, and machines that are more accurate. Possible future studies include quantification of secondary effects from the yield increase; effects that can help mitigate deforestation and climate change, with a retained level of production volumes.

X-ray computed tomography (CT) scanning of sawmill logs is associated with costly and complex machines. An alternative scanning solution was developed, but its data have not been evaluated regarding detection of internal features. In this exploratory study, a knot detection algorithm was applied to images of four logs to evaluate its performance in terms of knot position and size. The results were a detection rate of 67 percent, accurate position, and inaccurate size. Although the sample size was small, it was concluded that automatic knot detection in coarse resolution CT images of softwoods is feasible, albeit for knots of sufficient size.