Flexural behavior of wood in the transverse direction investigated using novel computer vision and machine learning approach
Flexural behavior of wood in the transverse direction investigated using novel computer vision and machine learning approach
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DOI：
10.1515/hf-2022-0096
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发表时间：
2022-09
期刊：
影响因子：
2.4
通讯作者：
Shuoye Chen;T. Awano;A. Yoshinaga;J. Sugiyama
中科院分区：
文献类型：
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作者：
Shuoye Chen;T. Awano;A. Yoshinaga;J. Sugiyama
摘要采用基于深度学习的语义分割方法（U-Net）对扁柏木材在微观三点弯曲试验中的横截面进行解剖特征分割。使用Crocker-Grier连接算法，成功提取了数千个单元，并使用多个参数（面积、偏心率、拟合椭圆纵横比、边界框纵横比）来评估单元变形的强度。由此，构建了变形强度分布的2D图。通过分析平锯、四分锯和纵锯试件，证实了年轮方向影响木材在横向上的弯曲行为。四分锯试样表现出最大的弹性模量（莫伊）和断裂模量（莫尔）。射线组织与载荷对齐可能有助于限制细胞变形。剖切试样的莫伊和莫尔最小，这可能是由于试样在面内离轴方向上加载，从而引起细胞壁的剪切变形。对于所有三种类型的试样，断裂发生在拉伸部分的试样，表现出大的细胞变形的概率高。因此，所提出的方法可以适用于木材试样的断裂预测。对于不同的试验材种，该方法有助于阐明木材解剖特征与力学性能之间的关系，提高木材资源的有效利用。
Abstract A deep-learning-based semantic segmentation approach (U-Net) was used to partition the anatomical features in the cross-section of hinoki (Chamaecyparis obtusa) wood during a micro three-point bending test. Using the Crocker–Grier linking algorithm, thousands of cells were successfully extracted, and several parameters (area, eccentricity, fitted ellipse aspect ratio, bounding box aspect ratio) were used to evaluate the intensity of the cells’ deformation. Thus, the 2D map of the deformation intensity distribution was constructed. By analyzing flat-sawn, quarter-sawn, and rift-sawn specimens, it was confirmed that the annual ring orientation affects the flexural behavior of wood in the transverse direction. The quarter-sawn specimens exhibited the largest modulus of elasticity (MOE) and modulus of rupture (MOR). The ray tissue aligned against the load may have contributed to the restriction of cell deformation. The rift-sawn specimens exhibited the smallest MOE and MOR, possibly owing to the loading of the specimen in the in-plane off-axial direction, which induced the shear deformation of the cell wall. For all three specimen types, the fracture had high occurrence probability in the tension part of the specimen, which exhibited large cell deformation. Therefore, the proposed method can be adapted to the prediction of wood specimen fractures. With different test wood species, this approach can be of great help in elucidating the relationship between the anatomical features and the mechanical behavior of wood to improve the effective utilization of wood resources.