Jan Nyström

Automatic measurement of fiber orientation in softwoods by using the tracheid effect

Abstract Spiraled grain commonly occurs in softwood trees. Instead of running parallel to the pith, the grain runs spirally around the trunk like a helix. Since wood is an orthotropic material with higher shrinkage perpendicular to than parallel to the fibers, the log will twist when dried, and so will a plank or board cut from it. Several investigations have shown that the magnitude of twist in sawn wood is highly correlated to the fiber orientation, so by measuring the fiber orientation on green lumber, the risk of warp after drying can be indicated. Fiber orientation is also interesting for other purposes, for example stress grading and research applications. This paper concerns the tracheid effect, which utilizes the light conducting properties of the softwood tracheids to measure fiber orientation. A small circular laser beam was projected onto the wood surface. The light was transmitted in the wood and scattered back to form an elliptical shape extended in the direction of the fibers. The ellipse of light was registered with a CMOS camera, and the orientation of the ellipses major axis was calculated. This method has a correlation coefficient of 0.99996 to manually aligned fiber orientation, and repeated measurements show a standard deviation of 0.2°. Calculation time for one 64×64 pixel image was 67 μs, which must be regarded as useful for industrial applications.

Real-time spectral classification of compression wood in Picea abies

Compression wood is formed by the living tree to compensate for external loads. It creates wood fibers with properties undesirable in sawn products. Automatic detection of compression wood can lead to production advantages. A wood surface was scanned with a spectrometer, and compression wood was detected by analyzing the spectral composition of light reflected from the wood surface within the visible spectrum. Linear prediction models for compression wood in Norway spruce (Picea abies) were produced using multivariate analysis and regression methods. The resulting prediction coefficients were implemented in a scanning system using the MAPP2200 smart image sensor combined with an imaging spectrograph. This scanning system is capable of making a pixelwise classification of a wood surface in real time. Classification of one spruce plank was compared with analysis by scanning electron microscopy, showing that the automatic classification was correct in 11 of 14 cases.

Prediction of longitudinal shrinkage and bow in Norway spruce studs using scanning techniques

Straightness is one of the most important properties for making timber an attractive material for modern mechanized building. Several studies have shown that a lack of straightness is one of the main reasons for choosing materials other than timber in the construction industry. This paper presents a way to model moisture-induced bow from longitudinal shrinkage data predicted from an analysis of images of the surface of Norway spruce studs. For this study, eight studs (45 × 95 × 2500 mm and 45 × 120 × 3000 mm) of Norway spruce timber were selected. Bow in these studs was measured at two moisture contents below the fiber saturation point. The studs were then split into three slices 11 mm thick, and the surfaces of these slices were scanned to obtain color information and images of the tracheid effect. The slices were cut into sticks with dimensions of 10 × 10 × 200 mm. The longitudinal shrinkage coefficient of these sticks was measured. A multivariate model was created to model the longitudinal shrinkage coefficient data from the information in the images. The predicted longitudinal shrinkage data was used to model bow. The mean value of the measured longitudinal shrinkage was 0.0121 (SD 0.0123). The root mean square error of prediction (RMSEP) for the multivariate model was 0.0079, which is regarded as good. Thus, it was possible to model moisture-induced bow with good accuracy using the predicted longitudinal shrinkage data.

Measurement of green plank shape for prediction and elimination of compression wood

The objective of this study was to predict the amount and the distribution of compression wood (CW) within a Norway spruce [ Picea abies (L.) Karst.] plank based on green plank curvature. The findings indicated a possibility of predicting the longitudinal distribution of CW from the green plank curvature. Areas free from CW showed a typical concave shape in relation to the centre of the log, while CW was present when a convex shape was shown. The larger the magnitude of convex curvature, the higher the concentrations of CW that could be found, and a larger fraction of dried planks was rejected due to excessive warp. This study also determined what information can be used to eliminate areas of high concentrations of CW by cutting and how cutting affects the grading results with respect to warp. Over 50% of the plank length showing a high concentration of CW (>30% of the cross-cut volume) was successfully cut off. Cutting strategies based on predicted CW concentrations resulted in a 10-40% increase in accepted plank length.

Modelling of adequate pretwist for obtaining straight timber

Abstract Wood in general and wooden studs in particular are often distorted owing to uneven shrinkage during the drying process in the sawmill. Twist is often the most detrimental of all types of distortion, and it is caused by spiral grain in combination with variations in moisture content. For sawmills, the objective is to produce dried, straight boards, and one method of dealing with boards with excessive spiral grain is to sort them out and then dry them in a pretwisted position to obtain straight boards after drying. A model using the finite element (FE) method for the simulation of drying twist distortions was first calibrated against laboratory experiments in which boards were dried with and without restraints and pretwists. After the calibration, the FE results were compared with industrial test results for boards that were dried without restraints or with restraints with zero pretwist, i.e. straight restraints. The FE model used an elastic–ideally plastic material model to obtain permanent deformations. The calibration was to set the yield stresses so that there was a good match between FE results and results from the laboratory experiments. The comparison between the industrial test results and the FE results showed that the FE model is capable of realistic simulations of drying boards with and without restraints and presumably also pretwists.

Methods for detecting compression wood in green and dry conditions

The living softwood tree forms compression wood to compensate for external loads during growth, which creates wood fibers with higher longitudinal shrinking and swelling than normal wood at moisture content changes. This is often the cause of undesirable warping of sawn wood products after drying. An automatic detection of severe compression wood is thus useful to reject unwanted pieces. Detection in green condition is often preferred in a sawmill while detection in dry condition is needed in other applications. Three different non- destructive scanning methods were evaluated on both green and dry wood surfaces. The methods used were RGB (red, green, blue) color scanning, tracheid-effect scanning and x-ray scanning. The color and x-ray methods were evaluated on Southern yellow pine lumber, while the tracheid-effect scanning was tested on Norway spruce. For scanning in green condition detection of compression wood was good using the tracheid-effect and color scanning. X-ray scanning was not useful because of the uneven moisture distribution in green lumber. After drying the result changes, tracheid-effect and x-ray scanning have good detection ability while RGB color does not provide sufficient information for reliable detection.