Collaborative Research: DMREF: Simulation-Informed Models for Amorphous Metal Additive Manufacturing

合作研究：DMREF：非晶金属增材制造的仿真模型

• 批准号: 2323718
• 负责人: Michael Falk
• 金额: $ 70万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323718&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Simulation; Informed

Non-technical Description Additive manufacturing of amorphous metals is a potentially transformative technology for printing three-dimensional parts with superior strength and toughness. Since amorphous metals solidify without adopting a crystal structure, they do not form crystalline defects that can limit part performance. While the high cooling rates associated with using a laser to deposit metal on a surface are favorable for avoiding crystallization, the scanning of the laser can lead to subsequent crystallization and variations in properties from one location to another. These issues currently limit the technique to small scale and specialty parts. To overcome this limitation, machine learning approaches will derive meaningful measures of material structure from electron nanodiffraction and simulation data. Upon this foundation, the research team will build simulation-informed models, tools that will predict how processing gives rise to the strength and toughness of the resulting materials. These models will be tested by direct comparison to experiment, laying the scientific groundwork for computational design tools for additive manufacturing of amorphous metals. Concurrently, the three universities engaged in this research will form a learning community to support graduate student professional development in online communication. This community will distill and disseminate the investigators' experiences developing online content for courses, engaging in public communication, and building outreach programs for underserved communities. The modules developed will teach tomorrow’s researchers how to effectively engage diverse audiences of various ages. Taken together, the proposed work supports national priorities in advanced manufacturing technology and workforce development, particularly at the intersection with mathematical methods and data science.Technical Description This DMREF project will develop the underlying materials science and computational tools to enable design of additively manufactured amorphous metals with desired mechanical properties, including strength and toughness. Amorphous metals, also termed metallic glasses, have potential as a transformative material for additive manufacturing applications. Unlike crystalline materials that solidify through the growth of anisotropic grains, typically resulting in grain boundaries and complex textures, rapid cooling causes metallic glasses to solidify without crystal structure. Amorphous metal additive manufacturing is promising both for superior structural homogeneity compared to crystals and for overcoming cooling-rate limitations for casting larger structures. However, reheating associated with layer-by-layer processing results in material with a complex thermal history and spatially varying mechanical properties. The simulation-informed modeling undertaken by the research team is the first step toward a simultaneous design approach for achieving target materials properties and performance. This approach will couple processing by direct laser deposition with high-fidelity physical models. Machine learning will be used to quantify key order parameters suitable for predicting mechanical properties from nanometer-resolution electron nanodiffraction and atomistic simulation data. Solving this data fusion and inference problem will relate experimental and simulation data on differing scales to structural order parameters in robust ways. From these, the researchers will build simulation-informed models, continuum numerical tools that will capture how processing gives rise to the strength and toughness of the resulting materials. Validation will be achieved by direct comparison to ex situ and in situ mechanical testing. Uncertainty quantification will be included in these models a priori.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非晶金属的增材制造是一种潜在的变革性技术，用于打印具有优越强度和韧性的三维部件。由于非晶金属固化时不采用晶体结构，因此不会形成晶体缺陷，从而限制零件的性能。虽然使用激光在表面上沉积金属的高冷却速率有利于避免结晶，但激光的扫描可能导致随后的结晶和从一个位置到另一个位置的性能变化。这些问题目前限制了该技术的小规模和特殊零件。为了克服这一限制，机器学习方法将从电子纳米衍射和模拟数据中获得有意义的材料结构测量。在此基础上，研究团队将建立模拟信息模型，预测加工过程如何提高所得材料的强度和韧性的工具。这些模型将通过与实验的直接比较进行测试，为非晶金属增材制造的计算设计工具奠定科学基础。同时，参与这项研究的三所大学将组成一个学习社区，以支持研究生在网上交流方面的专业发展。这个社区将提炼和传播调查人员的经验，为课程开发在线内容，参与公共交流，并为服务不足的社区建立外展计划。开发的模块将教会未来的研究人员如何有效地吸引不同年龄的不同受众。总的来说，拟议的工作支持国家在先进制造技术和劳动力发展方面的优先事项，特别是在与数学方法和数据科学的交叉方面。该DMREF项目将开发基础材料科学和计算工具，以设计具有所需机械性能（包括强度和韧性）的增材制造非晶态金属。非晶金属，也被称为金属玻璃，有潜力成为增材制造应用的变革性材料。不像结晶材料通过各向异性晶粒的生长而固化，通常会导致晶界和复杂的纹理，快速冷却导致金属玻璃固化而没有晶体结构。与晶体相比，非晶金属增材制造具有优越的结构均匀性，并且克服了铸造较大结构的冷却速度限制。然而，与逐层加工相关的再加热导致材料具有复杂的热历史和空间变化的机械性能。研究团队进行的模拟建模是实现目标材料特性和性能的同步设计方法的第一步。这种方法将直接激光沉积加工与高保真物理模型相结合。机器学习将用于量化关键顺序参数，适用于从纳米分辨率电子纳米衍射和原子模拟数据预测机械性能。解决这一数据融合和推理问题将以稳健的方式将不同尺度的实验和模拟数据与结构顺序参数联系起来。从这些数据中，研究人员将建立模拟信息模型，连续的数值工具，以捕捉加工过程如何提高所得材料的强度和韧性。验证将通过直接与非原位和原位机械测试进行比较来实现。不确定性量化将先验地包含在这些模型中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。