Microstructural Foundations of Magnesium Performance: A Data Mining Approach to High-throughput Electron Microscopy

镁性能的微观结构基础：高通量电子显微镜的数据挖掘方法

• 批准号: 1404771
• 负责人: David Fullwood
• 金额: $ 34.69万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404771&HistoricalAwards=false
• 关键词: Microstructural; Foundations; Magnesium; Performance; Data

In order to meet ambitious targets for reduced energy consumption and emissions in modern vehicles, one of the most widely adopted strategies involves the deployment of lightweight structural materials. At less than a quarter of the density of steel, magnesium is a natural contender to replace legacy materials in automotive and other vehicle components. However, despite its desirable physical properties, magnesium accounts for only a small proportion of the make-up of a typical automobile. The reason, in large part, stems from the difficulty with forming the relatively brittle magnesium alloys into complex shapes required for vehicle components. This award supports research into the science underlying micro-scale behavior of magnesium alloys during forming and other deformation operations. By uncovering the relationships between the metal's microstructure and its response to deformation, modified manufacturing processes and improved alloys can be designed to enable widespread use of lightweight magnesium components in vehicular and other weight-sensitive applications. This will be complemented by STEM-oriented outreach activities and external collaborations.The response of a magnesium component to applied deformation is governed by two atomic-level phenomena: slip and twin activity. Twinning is especially vital to deformation at and near room temperature, due to the difficulty of slip. This project will build upon newly developed microscopy techniques (high-resolution electron backscatter diffraction) to generate snapshots of nano and micro-level structural activity during forming activities. Data mining knowledge extraction techniques will be applied to the resultant huge data sets of twin and slip activity in order to accelerate the discovery of interrelations between microstructure and magnesium deformation mechanics. The data mining will employ a decision-tree type analysis and a neural net approach. The resultant knowledge will be embedded in a meso-scale model of twin / deformation activity as the basis for assessment and design of improved alloys for light-weight structural applications. New insights will emerge into twin development that span various grain-size ranges and critical temperature levels. Key structure parameters will be modeled, including detailed grain-boundary character, accurate local (relative) strain levels, dislocation activity / slip transition temperatures, measures of crystal lattice entropy and other metrics of local heterogeneity. Furthermore, the high-throughput microscopy and data mining advances developed as part of the study will serve as a new framework for accelerated knowledge discovery for constitutive models of deformation in crystalline materials.

为了实现雄心勃勃的目标，以减少现代车辆的能源消耗和排放，最广泛采用的策略之一是部署轻量级的结构材料。镁在不到钢密度的四分之一不到四分之一的位置，是替代汽车和其他车辆组件中遗留材料的天然竞争者。但是，尽管具有理想的物理特性，但镁的占典型汽车组成的一小部分。原因很大程度上源于将相对脆弱的镁合金形成媒介物所需的复杂形状的困难。该奖项支持对形成和其他变形操作过程中镁合金的科学基础微尺度行为的研究。通过揭示金属的微结构与对变形的响应之间的关系，可以设计改进的制造工艺和改进的合金，以便在车辆和其他重量敏感的应用中广泛使用轻质镁成分。这将与面向茎的外展活动和外部协作相辅相成。镁成分对应用变形的响应受两个原子级现象的控制：滑移和双活性。由于滑动的难度，双对在室温下和接近室温尤其重要。该项目将基于新开发的显微镜技术（高分辨率电子反向衍射），以在成型活动期间生成纳米和微级结构活动的快照。数据挖掘知识提取技术将应用于产生的双胞胎和滑移活动的巨大数据集，以加速微观结构和镁变形力学之间的相互关系。数据挖掘将采用决策树类型分析和神经网络方法。所得的知识将嵌入双胞胎 /变形活性的中尺度模型中，作为评估和设计改进合金的基础，用于轻重量结构应用。新的见解将出现在跨越各种晶粒大小范围和临界温度水平的双胞胎发展中。将建模关键结构参数，包括详细的晶粒特征，准确的局部（相对）应变水平，错位活性 /滑动过渡温度，晶体晶格熵的测量以及其他局部异质性的指标。此外，作为研究的一部分而开发的高通量显微镜和数据挖掘进展将是一个新的框架，用于加速晶体材料变形模型的知识发现。