A numerical strength prediction approach for wood using element-wise local fiber directions from laser scanning

A numerical strength prediction approach for wood using element-wise local fiber directions from laser scanning

Seeber, Franziska (Technische Universität München)
Sarnaghi, Ani Khaloian (Technische Universität München)
Rais, Andreas (Technische Universität München)
van de Kuilen, J.W.G. (TU Delft Bio-based Structures & Materials; Technische Universität München)

2023

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.

Laser scanning
FE model
3D stresses
Virtual strength prediction

http://resolver.tudelft.nl/uuid:d2e3c6c3-ddc5-4c93-a5ce-2768a26f7d72

https://doi.org/10.1016/j.matdes.2022.111578

0264-1275

Materials & Design, 226

Institutional Repository

journal article

© 2023 Franziska Seeber, Ani Khaloian Sarnaghi, Andreas Rais, J.W.G. van de Kuilen