Mechanical properties of wood such as stiffness and strength vary locally especially due to heterogeneities and anisotropy. Analytical models and numerical simulations of wooden boards are able to represent varying material orientation e.g. with local fiber directions from laser scanning as input for the prediction of strength. Current Finite Element Models reconstructed the grain orientation by means of computationally demanding fluid analysis around obstacles like knots; whereas the available fiber pattern, captured by means of laser scanning, was passed solely into the detection of knots, but not directly processed for the inclusion of material fiber orientation. Therefore, the goal of this paper was the development of a numerical approach to directly include locally varying measured fiber orientation with orthotropic material properties and to predict the tensile strength of boards with reduced computational effort. Therefore, the stiffness was transformed element-wise according to the measured fiber deviations and the local fiber stress components were computed for the specific tensile load case. For the virtual strength prediction, numerical maximum stress values were compared to experimental tensile strength. Good agreements were observed with reduced computational effort compared to existing approaches between numerical and experimental results.@en

In central Europe forests, the share of European beech (Fagus sylvatica L.) trees has been increased in the last decades. Machine strength grading of hardwood is challenging due to a lack of knowledge about strength predictors. However, high strength classes are needed for the utilization as glued and cross laminated timber. We used the information of an industrial scanner on fiber orientations, developed a 3D cluster value (SOG_3D,150,max) for strength assessment and combined the parameter with the dynamic modulus of elasticity (MOE_dyn) to compute an indicating property (IP). A sample of 407 European beech boards passed a multi-sensor scanner to detect wood density, eigenfrequency and slope of grain (SOG). Fiber angle on the surfaces of four board sides was measured using the tracheid effect. The spatial fiber orientation inside the board was modeled for a total of approximately 150,000 points per board meaning 12 points per cm³. Finally, the board section with the largest average local fiber orientation in a window of 150 mm defined the grading parameter SOG_3D,150,max. The prediction of tensile strength via SOG_3D,150,max reached r² between 0.466 and 0.605 depending on the type of data transformation. A combination with the MOE_dyn, the probably most common IP, increased the r² to 0.722 at best. Local grain deviation is a suitable wood parameter for hardwood strength grading. By detecting local defects, the causality between wood strength and tree functioning as well as silvicultural steering may be further understood in future.

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Timber joints with their simultaneous ductile and brittle failure modes still pose a major challenge when it comes to modelling. Wood is heterogeneous and highly anisotropic. It shows ductile behaviour in compression and brittle behaviour in tension and shear. A 3D constitutive model for wood based on continuum damage mechanics was developed and implemented via a subroutine into a standard FE framework. Embedment and joint tests using three different wood species (spruce, beech and azobé) were carried out, and the results were compared with modelling outcomes. The failure modes could be identified, and the general shape of the load–displacement curves agreed with the experimental outcomes.

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Structural heterogeneities in wood are changing the complexity of the numerical modelling of this material. Knots are the main strength governing parameters, which are changing the uniformity of the stress distributions through the boards. These weak locations may result in failure in the material. Comprehensive reconstruction of the boards, based on the knot data on the surfaces of the boards makes it possible to access the virtual model of the element and predict new material behaviors. The aim of the current study is to develop a more technologically advanced numerical method for grading of the wooden boards. Simulations are run using ABAQUS and PYTHON. By programming the structural model of the boards, covering the total quality range, the mechanical behavior is predicted under a uniform tension. Simulations are run for low-medium quality Douglas fir and mediumhigh quality spruce boards. Based on the extracted numerical parameters, the strength of the boards are predicted virtually. The results of the simulations are benchmarked to the currently available visual and machine grading methods. By getting a promising correlation between the tensile and the predicted strength results, a more advanced numerical method is provided for virtual strength grading of the boards. The method is later applied for the reconstruction of the glulam model based on the information of the single lamellas.@en