用最短路径算法自动区分碳纤维层合板显微图像中的每一层，实现精准微观分析。
Automatic ply-specific analyses of CFRP micrographs using shortest-path-based ply distinction
- 将分割图建模为像素图，用最短路径找层间分界线。
- 可处理多层间隙、叠层顺序和裂纹等复杂结构，精度高。
- 适合材料制造质量评估与力学性能关联研究者使用。
本文提出一种自动化方法，用于区分高分辨率碳纤维增强聚合物（CFRP）显微图像中语义分割掩码内的各层实例。将分割掩码视为以像素为顶点的图，利用最短路径算法识别出层间分界路径，从而结合全局信息弥合语义分割与层实例分割之间的差距。该方法成功应用于包含单层或多层人工间隙、不同叠层顺序及层间贯穿裂纹的多种特征显微图像。基于计算出的路径，将每条纤维像素归入对应层，可实现对局部纤维体积分数、层厚及界面层厚度等微观结构特性的全面定量分析。这些结果有助于揭示制造过程引入的非均质性，推断制造参数影响，并建立力学性能与微观缺陷间的联系。
原文摘要 · Abstract (English)
We present an automated approach to distinguish between ply instances in semantic segmentation masks of high-resolution carbon-fiber reinforced polymer micrographs. Interpreting the segmentation mask as a graph with pixels as vertices, enables us to use a shortest-path algorithm yielding the ply-separating paths. Thereby, we bridge the gap between semantic segmentation and ply instance segmentation using global information. We successfully apply our approach on high-resolution micrographs featuring a broad range of characteristics like artificially added gaps in single or multiple plies, different stacking sequences and ply traversing cracks. Assigning each fiber pixel to a ply based on the calculated paths, allows for a comprehensive, quantitative ply analysis with respect to its microstructural properties like the local fiber volume fraction as well as locally resolved ply and interleaf layer thickness. These insights help to reveal manufacturing-induced inhomogeneities, draw conclusions on manufacturing parameters and link mechanical properties to underlying microstructural imperfections.
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