论文标题
关于材料点云的高通量量化的强量表和开源工具：组成梯度，微结构对象重建和空间相关性
On Strong-Scaling and Open-Source Tools for High-Throughput Quantification of Material Point Cloud Data: Composition Gradients, Microstructural Object Reconstruction, and Spatial Correlations
论文作者
论文摘要
表征微结构 - 物质特性关系要求使用软件工具，这些工具提取微结构对象的点云和基于连续尺度的表示。应用示例包括原子探针，电子和计算显微镜实验。微结构对象的原子和连续尺度表示，通常会导致对参数化敏感的表示；但是，在实践中，评估这种敏感性是一项繁琐的任务。 在这里，我们展示了如何结合计算几何形状，碰撞分析和图形分析的方法，用于对点云数据进行自动分析，以重建三维对象，表征组成曲线以及通过评估基于图形的对象之间的基于图形的关系的多参数相关性的提取。我们针对带有标记数据的点云实施，讨论了原子探针显微镜中的用例，该用例集中在不同合金中观察到的界面，沉淀和重新沉淀现象。这些方法可扩展，用于对晶粒碎片，晶体生长或降水的时空分析。
Characterizing microstructure-material-property relations calls for software tools which extract point-cloud- and continuum-scale-based representations of microstructural objects. Application examples include atom probe, electron, and computational microscopy experiments. Mapping between atomic- and continuum-scale representations of microstructural objects results often in representations which are sensitive to parameterization; however assessing this sensitivity is a tedious task in practice. Here, we show how combining methods from computational geometry, collision analyses, and graph analytics yield software tools for automated analyses of point cloud data for reconstruction of three-dimensional objects, characterization of composition profiles, and extraction of multi-parameter correlations via evaluating graph-based relations between sets of meshed objects. Implemented for point clouds with mark data, we discuss use cases in atom probe microscopy that focus on interfaces, precipitates, and coprecipitation phenomena observed in different alloys. The methods are expandable for spatio-temporal analyses of grain fragmentation, crystal growth, or precipitation.
