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

为了实现降低现代车辆能耗和排放的宏伟目标，最广泛采用的策略之一是使用轻质结构材料。镁的密度不到钢的四分之一，是取代汽车和其他车辆部件中传统材料的天然竞争者。然而，尽管镁具有理想的物理特性，但它在一辆典型汽车的构成中只占很小的比例。究其原因，很大程度上是因为将相对易碎的镁合金制成汽车部件所需的复杂形状非常困难。该奖项支持镁合金在成形和其他变形操作过程中微观行为的科学研究。通过揭示金属的微观结构与其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部件。这将辅以面向stem的外联活动和外部合作。镁组分对施加变形的响应受两种原子水平现象的支配：滑移和孪晶活度。由于滑移的困难，孪生对室温和接近室温的变形尤其重要。该项目将建立在新开发的显微镜技术（高分辨率电子背散射衍射）上，以生成形成过程中纳米和微观水平结构活动的快照。数据挖掘知识提取技术将应用于由此产生的孪晶和滑移活动的庞大数据集，以加速发现微观结构与镁变形力学之间的相互关系。数据挖掘将采用决策树分析和神经网络方法。由此产生的知识将嵌入到孪生/变形活动的中尺度模型中，作为评估和设计用于轻质结构应用的改进合金的基础。新的见解将出现在双胞胎的发展，跨越不同的晶粒尺寸范围和临界温度水平。关键结构参数将建模，包括详细的晶界特征、精确的局部（相对）应变水平、位错活度/滑移转变温度、晶格熵测量和其他局部非均质性指标。此外，作为研究的一部分，高通量显微镜和数据挖掘的进步将作为加速发现晶体材料变形本构模型的新框架。

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.