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.

非技术性描述非晶金属增材制造是一种潜在的变革性技术，可用于打印具有卓越强度和韧性的三维零件。由于非晶态金属在不采用晶体结构的情况下凝固，因此它们不会形成限制零件性能的晶体缺陷。虽然与使用激光在表面上沉积金属相关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以及从一个位置到另一位置的特性变化。这些问题目前将该技术限制在小规模和特种零件上。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得出有意义的材料结构测量结果。在此基础上，研究团队将构建基于仿真的模型和工具，以预测加工如何提高所得材料的强度和韧性。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建一个学习社区，支持研究生在线交流的专业发展。该社区将提炼和传播调查人员开发在线课程内容、参与公共交流以及为服务不足的社区建立外展计划的经验。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段的不同受众。总而言之，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，特别是在数学方法和数据科学的交叉领域。技术描述该 DMREF 项目将开发基础材料科学和计算工具，以实现增材制造非晶态金属的设计，使其具有所需的机械性能，包括强度和韧性。非晶金属，也称为金属玻璃，具有作为增材制造应用的变革材料的潜力。与通过各向异性晶粒生长固化的晶体材料不同，通常会产生晶界和复杂的纹理，快速冷却会导致金属玻璃在没有晶体结构的情况下固化。 非晶金属增材制造不仅具有比晶体更优异的结构均匀性，而且能够克服铸造较大结构的冷却速率限制，因此前景广阔。然而，与逐层加工相关的再加热会导致材料具有复杂的热历史和空间变化的机械性能。研究团队进行的基于仿真的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结合起来。机器学习将用于量化适合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的关键有序参数。解决这个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。研究人员将据此构建基于模拟的模型和连续数值工具，以捕捉加工过程如何提高所得材料的强度和韧性。验证将通过与非原位和原位机械测试的直接比较来实现。不确定性量化将预先包含在这些模型中。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。