Abstract Macroscopic materials are heterogeneous, multi-elementary, and complex. No material is homogeneous or isotropic at a certain small scale. Parts of the material that differ from one another can be termed “natural chips.” At different spots on the material, the composition, structure, and properties vary slightly, and the combination of these slight differences establishes the overall material performance. This article presents a state-of-the-art review of research and applications of high-throughput statistical spatial-mapping characterization technology based on the intrinsic heterogeneity within materials. High-throughput statistical spatial-mapping uses a series of rapid characterization techniques for analysis from the macroscopic to the microscopic scale. Datasets of composition, structure, and properties at each location are obtained rapidly for practical sample sizes. Accurate positional coordinate information and references to a point-to-point correspondence are used to set up a database that contains spatial-mapping lattices. Based on material research and development design requirements, dataset spatial-mapping within required target intervals is selected from the database. Statistical analysis can be used to select a suitable design that better meets the targeted requirements. After repeated verification, genetic units that reflect the material properties are determined. By optimizing process parameters, the assembly of these genetic unit(s) is verified at the mesoscale, and quantitative correlations are established between the microscale, mesoscale, macroscale, practical sample, across-the-scale span composition, structure, and properties. The high-throughput statistical spatial-mapping characterization technology has been applied to numerous material systems, such as steels, superalloys, galvanization, and ferrosilicon alloys. This approach has guided the composition and the process optimization of various materials.

中文翻译：

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高通量统计空间映射表征技术及其应用的最新进展

摘要 宏观材料是异质的、多元的、复杂的。没有任何材料在某个小尺度上是均质或各向同性的。材料中彼此不同的部分可以称为“天然碎片”。在材料的不同部位，成分、结构和性能略有不同，这些细微差异的组合决定了材料的整体性能。本文基于材料内在的异质性，对高通量统计空间映射表征技术的研究和应用进行了最先进的回顾。高通量统计空间映射使用一系列快速表征技术进行从宏观到微观尺度的分析。组成、结构的数据集，对于实际样本大小，可以快速获得每个位置的属性。准确的位置坐标信息和对点对点对应关系的引用用于建立包含空间映射点阵的数据库。根据材料研发设计要求，从数据库中选择所需目标区间内的数据集空间映射。统计分析可用于选择更符合目标要求的合适设计。经过反复验证，确定了反映材料特性的遗传单位。通过优化工艺参数，在中尺度验证这些遗传单元的组装，并在微观尺度、中尺度、宏观尺度、实际样本、跨尺度跨度组成、结构、和属性。高通量统计空间映射表征技术已应用于众多材料系统，如钢、高温合金、镀锌和硅铁合金。这种方法指导了各种材料的组成和工艺优化。