The market share of European beech (Fagus sylvatica L.) wood in the construction sector is low despite an increase in beech stock in Central European Forests in recent years. More efficient sawing techniques, higher lamella grading yields and solving of adhesion challenges may increase the competitiveness of beech glulam and promote its use. The aim of this paper is to revise the lamella grading system in the current German technical approval for beech glulam Z-9.1-679:2019 (DIBt (2019). BS-Holz aus Buche und BS-Holz Buche Hybridträger und zugehörige Bauarten. Allgemeine bauaufsichtliche Zulassung Z-9.1-679:2019. Deutsches Institut für Bautechnik) and to suggest modifications in the lamella grading rules for glulam production allowing higher yields and reliable tensile strength values at the same time. The unique dataset in this study combined different origins of lamellas and covered a wide range of visual, physical and mechanical wood characteristics including a high amount of low quality material. Indicating properties (IPs) for tensile strength, such as knot parameters and dynamic modulus of elasticity, were contrasted with tensile strength and static modulus of elasticity. Beech lamellas, graded by means of Z-9.1-679:2019 (DIBt (2019). BS-Holz aus Buche und BS-Holz Buche Hybridträger und zugehörige Bauarten. Allgemeine bauaufsichtliche Zulassung Z-9.1-679:2019. Deutsches Institut für Bautechnik), did not achieve the tensile strengths required for glulam production in many grading classes and the yield was low. A machine grading approach with dynamic modulus of elasticity as a single grading criterion gave higher yields than the current grading procedure and high reliability for tensile strength prediction with a prediction accuracy of R2 = 0.67.

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.