ALBERT

Description: Samples of European beech (Fagus sylvatica) were used for this study. Logs of these samples covered a scatter of mild-to-strong curvatures and the boards of these samples covered strong fiber deviations. This study consists of two separate parts: (1) log reconstruction and optimization of the cutting pattern, and (2) board reconstruction and strength prediction. Information about the internal quality of the logs is missing in this study, as laser scanning has been used for surface reconstruction of logs. Therefore, two separate steps were implemented here. (1) Influence of cutting pattern and board-dimensions on yield were analyzed. For this step, 50 logs were checked. (2) A more advanced numerical method based on the finite element (FE) analysis was developed to improve the accuracy of tensile strength predictions. This step was performed, because visual grading parameters were relatively weak predictors for tensile strength of these samples. In total, 200 beech boards were analyzed in this step. However, due to the geometrical configuration of some knots, the reconstruction and numerical strength prediction of 194 boards out of 200 boards were possible. By performing tensile tests numerically, stress concentration factors (SCFs) were derived, considering the average and maximum stresses around the imperfections. SCFs in combination with the longitudinal stress wave velocity were the numerical identifying parameters (IPs), used in the nonlinear regression model for tensile strength prediction. The influence of the combination of different numerical parameters in the developed non-linear model on improving the quality of the strength prediction was analyzed. For this reason, improvement of coefficient of determination (R2) after adding each parameter to the multiple regression analysis was checked. Performance of the developed numerical method was compared to the typical grading approaches [using knottiness and the dynamic MoE (MoEdyn)], and it was shown that the coefficient of determination is higher, when using the virtual methods for tensile strength predictions.

Description: Detection of local wood inhomogeneities is important for accurate strength and stiffness prediction. In hardwood specimens, visual characteristics (e.g. knots or fibre deviation) are difficult to detect, either with a visual surface inspection or by the machine. Transversal ultrasound scan (TUS) is a non-destructive evaluation method with high potential for hardwoods. The method relies on differences in ultrasound wave propagation in perpendicular to the grain direction. The aim of this study is to estimate and analyse the capabilities of TUS for defect detection in hardwoods and prediction of mechanical property values. In the current paper, the TUS was applied to the hardwood species European ash (Fraxinus excelsior L.), Norway maple (Acer platanoides L.) and sycamore maple (Acer pseudoplatanus L.). In total, 16 boards of both specimens were completely scanned perpendicular to the grain using a laboratory scanner with dry-coupled transducers. The measurements were processed to 2D scan images of the boards, and image processing routines were applied to further feature extraction, defect detection and grading criteria calculation. In addition, as a reference for each board, all relevant visual characteristics and mechanical properties from the tensile test were measured. Using the TUS global fibre deviation, the size and the position of the knots can be detected. Knottiness correlates to the strength properties similarly or even better compared to the manual knottiness measurement. Between the global fibre angle measured using TUS and measured on the failure pattern, no correlation could be found. The ultrasound modulus of elasticity perpendicular to the grain does not show any meaningful correlation to the elastic properties parallel to the grain. In overall, TUS shows high potential for the strength grading of hardwoods.