European beech (Fagus sylvatica L.) is the most commonly occurring deciduous tree species in Central Europe. Because of its competitiveness and (morphological) variability, it occurs in different species mixtures. The aim of the present study was to determine whether, and to what extent, the internal wood properties of European beech are affected by species mixture. Logs obtained from 100 beech trees from the low-mountain Spessart region in Germany were sampled and subdivided into five equal groups representing different mixture types: pure beech stands and four beech stands with the following tree species, respectively: Pseudotsuga menziesii, Picea abies, Pinus sylvestris and Quercus petraea. After logging and sawing, the boards were graded according to strength. For this purpose, the dynamic modulus of elasticity (MOE_dyn) was determined. The MOE_dyn, which is a relevant property for wood quality in the construction sector, was regressed against various independent variables, such as cambial age or mixture type. Relevant differences in wood quality were observed between mixture types. Beech trees in monocultures had the highest stiffness. In particular, species mixture with the light-demanding Quercus petraea or Pinus sylvestris yielded a significant reduction in the stiffness of the beech timber (–10% and –14%, respectively). Therefore, beech glued laminated timber of higher strength classes (≥GL 40c) was not viable using beech from these mixture types. The findings of this study also revealed significant effects of axial board position and cambial age on MOE_dyn. The present transformation of forests into mixed forests affects the quality of the wood. Future research should focus on the dependencies between varying growth conditions—also caused by climate change—and wood formation.

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In Central Europe, European beech (Fagus sylvatica L.) is the most frequently occurring hardwood species. An efficient grading method has the potential to promote its utilisation as construction material. Wood density, eigenfrequency and length were measured in 99 European beech logs for calculating the dynamic modulus of elasticity (MOE_dyn) obtained by longitudinal vibration (resonance). In addition, the log taper was measured. Of those logs, 867 boards were cut using a bandsaw. The MOE_dyn in green condition was determined on 505 of the boards and the MOE_dyn in dry conditions was determined on all of them. The r² value between the MOE_dyn of a log and the mean of MOE_dyn of its boards was 0.72 in the wet condition. The MOE_dyn,12% of boards significantly increased by 88 N mm^-2 for each centimetre away from the pith. The negative effect of log taper on MOE_dyn of boards was barely significant (P-value = 0.050). The MOE_dyn,12% was highly dependent on the MOE_dyn,wet (r² = 0.83) and was 17% higher than the MOE_dyn,wet. The mechanical properties of European beech timber exceed those of European softwood species. However, the relationships regarding MOE_dyn between different grading levels in the processing chain appear to be similar to those of softwoods.

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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 (MoE_dyn)], and it was shown that the coefficient of determination is higher, when using the virtual methods for tensile strength predictions.

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Wood is a strongly anisotropic and heterogeneous material with natural defects that are affecting the uniform scatter of the fiber patterns. European beech (Fagus sylvatica) is the species, used for this study. The logs of these species have generally complicated shapes with frequent curvatures, which is in contrast to most of the softwood species with relatively straight log shapes. From structural point of view, these species have fewer knots and natural features, but stronger fiber deviations compared to different softwood species that have complicated knot configurations. This study consists of two parts: 1) log reconstruction and optimization of the cutting pattern, and 2) board reconstruction and strength prediction. Due to the complex structural pattern of hardwoods, the visual grading method is a relatively weak strength predictor for these species. The aim of this study is to develop a numerical method based on the finite element (FE)-analysis to provide a better prediction for the tensile strength of the boards. The analysis covers the scatter of 200 beech boards. By resembling the tensile test setup numerically, the stress concentration factors (SCFs) are calculated, considering the average and maximum stresses around the imperfections. SCFs in combination with the longitudinal stress wave velocity are the numerical parameters, used in the nonlinear regression model for tensile strength prediction. The nonlinear model is checked for different combinations of the numerical parameters to estimate and visualize the potential of the virtual predictions. Performance of the novel criteria is compared to the typical grading criteria (knottiness and the dynamic MoE (MoEdyn)), and is shown that the coefficient of determination is higher, when using the virtual methods for tensile strength predictions.@en