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.

中文翻译：

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基于实验室的微计算机断层扫描辅助木材有限元建模及其当前瓶颈的概述

基于显微实验室的 X 射线计算机断层扫描 (XμCT) 辅助有限元 (FE) 建模是一种在材料科学中日益流行的方法，用于预测异质材料的局部材料特性，例如弹性、湿膨胀和扩散。这种方法对于木材来说相对较新，缺乏明确的方法论。旨在优化 XµCT 辅助有限元过程的研究通常侧重于该过程中的特定方面，例如 XµCT 扫描、分割或网格划分，而不是整个过程。各方面之间的兼容性和数据传输尚未得到相同程度的研究，这会产生传播错误并对最终结果产生负面影响。在当前的研究中，提出了一种 XµCT 辅助木材有限元处理的方法，并根据全面的文献综述确定了其瓶颈。尽管木材作为一种材料的复杂性使得 XµCT 辅助 FE 过程的自动化变得困难，但所提出的方法可以帮助更加深思熟虑地设计和执行该过程。确定的主要挑战包括重建纤维方向以及执行分割和网格划分的自动程序。可以建议将深度学习分割方法与基于几何的网格划分相结合。