GOALI: / DMREF: Multimodal design of revolutionary additive-enabled oxide dispersion strengthened superalloys

这个与工业界学术联络的资助机会（GOALI）设计材料以革新和工程我们的未来（DMREF）项目将为创造用于超高温应用的金属材料开发新的知识和策略。这些应用对于提高航空航天喷气发动机和陆基发电厂涡轮机的效率至关重要，也将导致有害碳排放的减少。要创建和研究的新材料是基于这样一个概念，即金属可以通过在整个金属基体中均匀分布少量陶瓷氧化物相来增强。氧化增强剂的存在可以显著提高强度，并有助于保护材料在恶劣的高温环境下免受攻击。我们将采用NASA格伦研究中心（GRC）的合作伙伴开创的方法，寻求一种新的方法来制造这些氧化物弥散增强（ODS）金属合金。在这种“增材制造”方法中，移动的激光熔化并固化金属和氧化物粉末，一层一层地构建材料。该项目将通过使用额外的策略来加强金属来进一步改进这些材料。关于这种新的加工策略、合金的内部结构和这些新材料的机械行为之间相互作用的新知识将在项目中产生，这些知识将通过新的人工智能框架获得。该框架将使团队能够优化这些添加剂ODS合金，以满足GE航空航天和空军研究实验室合作伙伴感兴趣的设计目标。DMREF团队包括3名女性和1名黑人教师，并将为学生提供多种机会，让他们针对代表性不足的群体和退伍军人进行研究。该项目将开发新的知识和战略，为航空航天和发电领域的各种苛刻应用创造一类新的金属材料。NASA GRC的合作者最近开发了一种用于制造氧化物弥散强化（ODS）金属合金的新型添加剂加工路线，将用于设计具有特殊高温性能的高温合金。这种新的添加剂ODS工艺可以在单一的添加剂加工步骤中合成ODS合金，从而绕过了耗时且与大规模生产不一致的传统机械合金化工艺。该团队还包括GE航空航天和空军研究实验室的合作者，并寻求通过采用沉淀强化来将新的添加剂ODS工艺与高温合金设计原则相融合，以提高在多个温度范围内的强度和抗氧化性。一种新的基于微观结构的机器学习（ML）框架将用于：(A)以全面的方式表示多尺度微观结构，(b)开发性能/加工联系，以及(c)加速新的添加剂ODS合金的迭代设计。ML框架将通过一系列广泛的实验来获得信息，这些实验用于生成多尺度多模态微观结构量化，评估机械和氧化行为，并对这些合金独特性能背后的机制有一个基本的了解。这种方法将使我们的合作伙伴能够在两个设计目标上优化添加剂ODS合金，包括(1)强度和微观结构稳定性至关重要的长寿命应用的中/高温，以及(2)极端温度应用，在短时间尺度上保持快速发展的微观结构的强度和结构完整性。添加剂ODS加工路线为快速评估合金行为打开了大门，首次使用有效的ML方法对新型ODS合金进行合金显微组织性能优化。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。

This Grant Opportunity for Academic Liaison with Industry (GOALI) Designing Materials to Revolutionize and Engineer our Future (DMREF) project will develop new knowledge and strategies for creating metallic materials for ultra-high temperature applications. These applications are critical for improving the efficiency of jet engines in aerospace and turbines for land-based power plants, and will also lead to reduction of harmful carbon emissions. The new materials to be created and studied are based on the concept that metals can be reinforced by uniformly distributing a small amount of ceramic oxide phases throughout the metallic matrix. The presence of the oxide reinforcements can provide significant enhancement in strength and help protect the material against attack under harsh, high temperature environments. A new way to create these oxide-dispersion-strengthened (ODS) metal alloys will be pursued, using an approach pioneered by our collaborators at NASA Glenn Research Center (GRC). In this “additive manufacturing” approach, a moving laser melts and solidifies the metal and oxide powder, building up the material layer-by-layer. The project will further improve these materials by using additional strategies for strengthening the metals. New knowledge about the interaction between this new processing strategy, the resultant internal structure of the alloy, and mechanical behavior of these new materials will be generated in the project, and this knowledge will be made accessible using a new artificial-intelligence framework. This framework will enable the team to the optimize these additive ODS alloys for design objectives of interest to our partners at GE Aerospace and the Air Force Research Laboratory. The DMREF team includes 3 women and 1 black faculty member and will offer multiple opportunities for student research experiences targeting under-represented groups and veterans. This project will develop new knowledge and strategies for creating a new class of metallic materials for a wide range of demanding applications in aerospace and power generation. A novel additive-processing route for creating oxide dispersion strengthened (ODS) metallic alloys, recently developed by collaborators at NASA GRC, will be utilized to design superalloys with exceptional high temperature properties. This new additive ODS process enables the synthesis of ODS alloys in a single, additive processing step, thereby bypassing the conventional mechanical alloying process that is time-intensive and inconsistent with scale-up manufacturing. The team also includes collaborators at GE Aerospace and the Air Force Research Laboratory, and seeks to meld the new additive ODS process with superalloy design principles by employing precipitate strengthening in order to enhance strength and oxidation resistance across multiple temperature regimes. A novel microstructure based machine learning (ML) framework, will be utilized to: (a) represent multiscale microstructure in a comprehensive manner, (b) develop property/processing linkages, and (c) accelerate the iterative design of new additive ODS alloys. The ML framework will be informed by an extensive suite of experiments used to generate multiscale multimodal microstructure quantification, evaluate the mechanical and oxidation behavior, and develop a fundamental understanding of the mechanisms behind the unique properties of these alloys. This approach will enable optimization of the additive ODS alloys across two design objectives of interest to our partners, including (1) intermediate/high temperatures for long lifetime applications where strength and microstructure stability are of utmost importance, and (2) extreme temperature applications where maintaining strength and structural integrity of rapidly evolving microstructures is only required for short time scales. The additive ODS processing route opens the door to rapid assessment of alloy behavior, enabling for the first time the use of effective ML approaches for alloy-microstructure-property optimization of novel ODS alloys.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.