Measurement of spiral grain with computed tomography

Spiral grain is a feature of wood that affects the shape of the sawn timber. Boards sawn from logs with a large spiral grain have a tendency to twist when the moisture content changes. In sawmills the spiral grain in logs is judged manually. For research purposes the spiral grain in stems and logs is normally measured by destructive methods. In this study the spiral grain of the stems was measured nondestructively with a computed tomography (CT) scanner. Twelve Norway spruce (Picea abies) stems from two stands in Sweden were scanned with a CT scanner with one cross-sectional scan every 10mm along the stem. Concentric surfaces at various distances from the pith were reconstructed from the stack of CT images. In these concentric-surface images, which show various internal features of the log. the spiral grain angle was measured at different distances from the pith and at different heights in the stem. The destructive measurements of the spiral grain were carried out on disks from the top ends of the logs. On these disks the spiral grain was measured at different distances from the pith with a protractor. Finally, the results from the destructive method were compared with the results from analysis of the CT images. The nondestructive and destructive measurements were compared in pairs with the same radial and approximately the same height position in each pair. The correlations (r) between the two methods were 0.81 and 0.71. respectively, for the two stands. It was concluded that it is possible to measure the spiral grain angle nondestructively with a CT scanner.An outline of this study was presented at the 50th Annual Meeting of the Japan Wood Research Society, Kyoto     

Twist of wood studs: dependence on spiral grain gradient

Distortions due to moisture changes during drying or in service are a major problem for construction timber. Twist, caused mainly by the cylindrical geometry, the orthotropic nature of the wood material, and the tendency of the wood fibers to grow in a spiral around the stem, is often regarded as the most detrimental distortion of sawn timber. There is a need for a basic mechanical understanding of how the twist distortion arises and also a need for a simple formula to predict the amount of twist distortion. In this article such a formula is proposed, and theory and experimental data that indicate the validity of the formula are shown. The first term in the formula is a modification of a traditional expression which is proportional to the mean value of the spiral grain angle in the cross section in question. The second term in the formula is new and is proportional to the gradient of the spiral grain angle, and this term normally counteracts the first term so that a stud with a left-handed spiral grain might achieve a right-handed twist. Linear elastic finite element method (FEM) results and comparisons with experimental data show that the formula works well and that linear FEM calculations exaggerate the twist, which is probably partly due to nonlinear effects. The formula could be used to predict the twist of sawn timber from measured spiral grain angles on the log surface.     

Identification of selected internal wood characteristics in computed tomography images of black spruce: a comparison study

The feasibility of identifying internal wood characteristics in computed tomography (CT) images of black spruce was investigated using two promising classifiers: the maximum likelihood classifier (MLC) and the back propagation (BP) artificial neural network (ANN) classifier. Nine image features including one spectral feature (gray level values), a distance feature, and seven textural features were employed to develop the classifiers. The selected internal wood characteristics to be identified included heartwood, sapwood, bark, and knots. Twenty cross-sectional CT images of a black spruce log were randomly selected to develop the two classifiers. The results suggest that both classifiers produced high classification accuracy. Compared with the MLC classifier (80.9% overall accuracy), the BP ANN classifier had better classification performance (97.6% overall accuracy). Moreover, statistical analysis reveals that the heartwood of the black spruce log used in this study is the easiest to identify by either classifier compared with the other three log features. The results also suggest that the separability of one wood characteristic from the other wood characteristics in black spruce CT images is mainly related to moisture content.     

Age trends of genetic parameters of spiral grain in hybrid larch F1 and implications for efficiency of early selection

Age trends in variance components and heritability were estimated from the spiral grain angle of rings 2–25, as counted from the pith in hybrid larch (Larix gmelinii var. japonica × Larix kaempferi) F₁. Age-age genetic and phenotypic correlations and the optimum selection age for spiral grain were also calculated. Wood samples were collected from 95 29-year-old trees belonging to 19 full-sib families in a progeny test plantation in Hokkaido, northern Japan. Spiral grain angle data were obtained by the splitting method. Mean grain angles at growth rings used for the analyses were calculated as the arithmetic mean of angles up to respective rings. Generally, the additive genetic variance for mean grain angle decreased with increasing ring number. Although dominance variance was comparatively high near the pith, it decreased to zero in subsequent growth rings. Highest heritability estimates of mean grain angle occurred at ring 4 and then declined with age, ranging from 0.45 down to 0.20. Age-age phenotypic correlations were higher than genetic correlations, especially those involving early growth rings. Optimum selection ages for spiral grain based on genetic and phenotypic correlations were estimated as 3 and 4 years (cambial age), respectively, in which maximum gain efficiency per year were obtained.     

Comparing predictability of board strength between computed tomography,discrete X-ray,and 3D scanning of Norway spruce logs

Strength graded boards of Norway spruce (Picea abies (L.) Karst.) are important products for many Scandinavian sawmills. If the bending strength of the produced boards can be predicted before sawing the logs, the raw material can be used more efficiently. In previous studies it is shown that the bending strength can be predicted to some extent using discrete X-ray scanning of logs. In this study, we have evaluated if it is possible to predict bending strength of Norway spruce boards with higher accuracy using computed tomography (CT) scanning of logs compared to a combination of discrete X-ray and 3D scanning. The method was to construct multivariate models of bending strength for three different board dimensions. Our results showed that CT scanning of logs produces better models of bending strength compared to a combination of discrete X-ray and 3D scanning. The main reason for this difference was the benefit of knowing the position of where the boards were cut from the logs and therefore detailed knot information could be used in the prediction models. Due to the small number of observations in this study, care should be taken when comparing the resulting prediction models to results from other studies.     

Optimization potential for perception-oriented appearance classification by simulated sawing of computed tomography-scanned logs of Norway spruce

Abstract

Wood, as a natural material, has favourable properties in both technical and aesthetic aspects. Due to its inherent variability, production of high-quality sawn timber demands adequate control of log conversion, which is feasible with computed tomography (CT) log scanning. Existing appearance grading rules for sawn timber might not fully reflect people's visual perception of wood surfaces, and therefore, an alternative, more perception-oriented appearance classification could be beneficial. An appearance classification of sawn timber based on partial least squares discriminant analysis (PLS-DA) of knot-pattern variables was developed and tested. Knot-pattern variables derived from images of board faces were used in training PLS-DA models against an initial classification of the board faces previously established by aid of cluster analysis. Virtual board faces obtained from simulated breakdown of 57 CT-scanned Norway spruce logs were graded according to the developed classification. Visual assessment of the grading results indicated that the classification was largely consistent with human perception of board appearance. An initial estimation of the potential to optimize log rotation, based on CT data, for the established appearance grades was derived from the simulations. Considerable potential to increase the yield of a desired appearance grade, compared to conventional log positioning, was observed.     

Predicting moisture content and density distribution of Scots pine by microwave scanning of sawn timber

This study was carried out to investigate the possibility of calibrating a prediction model for the moisture content and density distribution of Scots pine (Pinus sylvestris) using microwave sensors. The material was initially of green moisture content and was thereafter dried in several steps to zero moisture content. At each step, all the pieces were weighed, scanned with a microwave sensor (Satimo 9,4GHz), and computed tomography (CT)-scanned with a medical CT scanner (Siemens Somatom AR.T.). The output variables from the microwave sensor were used as predictors, and CT images that correlated with known moisture content were used as response variables. Multivariate models to predict average moisture content and density were calibrated using the partial least squares (PLS) regression. The models for average moisture content and density were applied at the pixel level, and the distribution was visualized. The results show that it is possible to predict both moisture content distribution and density distribution with high accuracy using microwave sensors.     

Predicting dynamic modulus of elasticity of Norway spruce structural timber by forest inventory,airborne laser scanning and harvester-derived data

Norway spruce structural timber is one of the most important products of the Norwegian sawmilling industry, and a high grade-yield of structural timber is therefore important for the economic yield. Presorting of logs suited for production of structural timber might be one option to increase the grade yield. In this study, dynamic modulus of elasticity (E_dyn) of structural timber was predicted based on forest inventory data at site level and single-tree data from airborne laser scanning (ALS) and harvester. The models were based on 611 boards from 4 sites in southeastern Norway. Important variables at site level were elevation, site index (SI), and mean stand age. However, when combining data from all information sources, mean stand age and site index were the only significant variables at site level. Tree height and variables describing the crown, like crown length and crown volume, were important vaiables extracted from ALS data. Stem diameter measures and tapering were important variables measured by the harvester. The combined model with variables from all three information sources reduced the variance the most, especially when using individual tree age instead of average stand age. However, combining all these data requires accurate positioning of the trees by the harvester.