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.

非技术描述的非晶态金属添加剂制造是一种潜在的变革性技术，用于打印具有优势强度和韧性的三维零件。由于无定形金属在不采用晶体结构的情况下固化，因此它们不会形成可以限制零件性能的结晶缺陷。尽管与使用激光沉积在表面上的高冷却速率有利于避免结晶，但激光的扫描可以导致随后的结晶和从一个位置到另一个位置的性质变化。这些问题目前将技术限制在小规模和特殊零件上。为了克服这一限制，机器学习方法将从电子纳米式和仿真数据中得出有意义的材料结构量度。在这个基础上，研究团队将构建模拟模型，这些工具将预测处理如何产生所得材料的强度和韧性。这些模型将通过与实验直接比较来测试，为增加无定形金属制造的计算设计工具提供了科学基础。同时，从事这项研究的三所大学将组成一个学习社区，以支持在线交流中的研究生专业发展。该社区将提炼和传播调查人员的经验，为课程开发在线内容，进行公共交流以及为服务不足的社区建立外展计划。开发的模块将教明天的研究人员如何有效吸引各个年龄的潜水员受众。综合起来，拟议的工作支持高级制造技术和劳动力发展的国家优先事项，尤其是在与数学方法和数据科学的交集中。技术描述该DMREF项目将开发潜在的材料科学和计算工具，以设计与所需的机械性能（包括强度和韧性）的额外制造的无晶体金属。无定形金属（也称为金属眼镜）具有增加制造应用的变革性材料。与通过各向异性晶粒生长固化的结晶材料，通常导致晶界和复杂的质地，快速冷却会导致金属玻璃无需晶体结构而凝固。与晶体相比，非晶的金属添加剂制造对于优势结构均匀性和克服铸造较大结构的限制而有望。但是，与逐层处理相关的加热导致具有复杂的热历史记录和空间变化的机械性能的材料。研究团队进行的模拟模拟是迈向同时设计目标材料属性和性能的同时设计方法的第一步。这种方法将通过直接激光沉积与高保真物理模型进行处理。机器学习将用于量化关键顺序参数，以预测纳米分辨率电子纳米式和原子模拟数据的机械性能。解决此数据融合和推理问题将以强大的方式将不同尺度的实验和仿真数据与结构顺序参数相关联。从这些过程中，研究人员将构建模拟模型，连续数值工具，这些工具将捕获处理如何产生所得材料的强度和韧性。验证将通过直接与原位和原位机械测试进行直接比较来实现。这些模型将包括不确定性量化。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是宝贵的支持。