State-of-the-Art Review of High-Throughput Statistical Spatial-Mapping Characterization Technology and Its Applications. (June 2020)

• Record Type: Journal Article
• Authors: Wang, Haizhou; Zhao, Lei; Jia, Yunhai; Li, Dongling; Yang, Lixia; Lu, Yuhua; Feng, Guang; Wan, Weihao
• Is Part Of: Engineering. Volume 6:Number 6(2020)
• Page Start: 621
• Page End: 636
• Publication Date: 2020-06
• Subjects: Material heterogeneity -- High-throughput characterization -- Statistical spatial-mapping -- Original-position statistical-distribution analysis; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2020.05.005
• Languages: English
• ISSNs: 2095-8099

Abstract

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.