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Microscopic lab-based X-ray computed tomography (XµCT) aided finite element (FE) modelling is a popular method with increasing nature within material science to predict local material properties of heterogeneous materials, e.g. elastic, hygroexpansion and diffusion. This method is relatively new to wood and lacks a clear methodology. Research intended to optimise the XµCT aided FE process often focuses on specific aspects within this process such as the XµCT scanning, segmentation or meshing, but not the entirety of the process. The compatibility and data transfer between aspects have not been investigated to the same extent, which creates errors that propagate and negatively impact the end results. In the current study, a methodology for the XµCT aided FE process of wood is suggested and its bottlenecks are identified based on a thorough literature review. Although the complexity of wood as a material makes it difficult to automate the XµCT aided FE process, the proposed methodology can assist in a more considered design and execution of this process. The main challenges that were identified include an automatic procedure to reconstruct the fibre orientation and to perform segmentation and meshing. A combined deep-learning segmentation method with geometry-based meshing can be suggested.

An overview of lab-based micro computed tomography aided finite element modelling of wood and its current bottlenecks