A supervised iterative approach to 3D microstructure reconstruction from acquired tomographic data of heterogeneous fibrous systems
A supervised iterative approach to 3D microstructure reconstruction from acquired tomographic data of heterogeneous fibrous systems
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DOI：
10.1016/j.compstruct.2018.08.029
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发表时间：
2018-12
影响因子：
6.3
通讯作者：
Ronald F. Agyei;M. Sangid
中科院分区：
文献类型：
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作者：
Ronald F. Agyei;M. Sangid
近年来，短纤维增强复合材料（SFRCs）广泛应用于需要形状复杂、重量轻且具有良好强度性能的应用中，这需要深入研究，以了解SFRCs对载荷和损伤响应背后的基础物理。由于此类研究的准确性取决于成功的子体积表征，因此SFRCs复杂的子体积结构需要认真的方法来解决复杂的形态，如纤维交叉，这绝不是一项微不足道的努力。本文提出了一种新的框架，该框架依赖于鲁棒二维分割和三维体重建技术之间的协同作用，以忠实地重建SFRCs三维x射线层析图的纤维结构。该框架的含义不仅包括一个充分表征复杂子体积的平台，而且还提供了一种方便的方法，可以将跟踪算法整合到重建纤维的原位表征中，如果需要的话。
The recent ubiquitous utilization of short fiber reinforced composites (SFRCs) in applications that require complex shape conforming, light-weight materials with good strength properties, calls for in-depth studies to understand the underpinning physics behind SFRCs response to load and consequently damage. Since the accuracy of such studies is contingent on successful sub-volume characterization, the intricate sub-volume architecture of SFRCs requires conscientious methodology that addresses complex morphologies like fiber cross-overs, which is by no means a trivial endeavor. This paper proposes a novel framework that hinges on the synergy between robust 2D segmentation and 3D volume reconstruction techniques to faithfully reconstruct the fiber architecture of 3D X-ray tomograms of SFRCs. The implications of this framework not only include a platform that fully characterizes the complex sub-volume, but also provides a convenient means of incorporating tracking algorithms necessary for the in-situ characterization of the reconstructed fibers, if desired.