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.

欧洲山毛榉（Fagus sylvatica）用于本研究。这些样品的原木覆盖了轻微到强曲率的散布，这些样品的板覆盖了很强的纤维偏差。这项研究包括两个独立的部分：（1）木材的重构和切割模式的优化，以及（2）板材的重构和强度的预测。由于激光扫描已用于原木的表面重建，因此本研究中缺少有关原木内部质量的信息。因此，此处执行了两个单独的步骤。（1）分析了切割方式和板尺寸对产量的影响。对于此步骤，检查了50条日志。（2）开发了一种基于有限元（FE）分析的更高级的数值方法，以提高抗拉强度预测的准确性。执行此步骤，因为视觉分级参数对于这些样品的抗张强度预测相对较弱。在此步骤中，总共分析了200个山毛榉板。但是，由于某些结的几何形状，可以对200个板中的194个板进行重构和数值强度预测。通过数值执行拉伸试验，考虑了缺陷附近的平均应力和最大应力，得出了应力集中因子（SCF）。SCF与纵向应力波速度相结合是数字识别参数（IPs），用于非线性回归模型中以预测拉伸强度。分析了已开发的非线性模型中不同数值参数的组合对提高强度预测质量的影响。为此原因，²）在将每个参数添加到多元回归分析之后，进行检查。将开发的数值方法的性能与典型的分级方法[使用打结度和动态MoE（MoE _dyn）]进行了比较，结果表明，使用虚拟方法进行抗拉强度预测时，确定系数更高。