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

这个与工业(GOALI)设计材料革新和设计我们的未来(DMREF)项目的学术联系机会将为创造用于超高温应用的金属材料开发新的知识和战略。这些应用对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，还将减少有害碳排放。将要创造和研究的新材料是基于这样一个概念，即通过在金属基质中均匀分布少量的陶瓷氧化物相可以增强金属。氧化物增强体的存在可以显著增强强度，并帮助保护材料在恶劣的高温环境下免受侵蚀。我们将寻求一种新的方法来制造这些氧化物弥散强化(Ods)金属合金，使用由我们在NASA格伦研究中心(GRC)的合作者首创的方法。在这种“添加制造”的方法中，移动的激光熔化和凝固金属和氧化物粉末，一层一层地形成材料。该项目将通过使用其他强化金属的战略来进一步改进这些材料。该项目将产生关于这一新的加工策略、由此产生的合金内部结构以及这些新材料的机械行为之间相互作用的新知识，并将使用一个新的人工智能框架来访问这些知识。该框架将使团队能够针对GE航空航天和空军研究实验室的合作伙伴感兴趣的设计目标，优化这些添加的消耗臭氧层物质合金。DMREF团队包括3名女性和1名黑人教职员工，将为学生提供多种机会，针对代表性不足的群体和退伍军人进行研究体验。该项目将开发新的知识和战略，为航空航天和发电中各种要求苛刻的应用创造一种新的金属材料类别。NASA GRC的合作者最近开发了一种用于创建氧化物弥散强化(Ods)金属合金的新的添加剂加工路线，该路线将被用于设计具有特殊高温性能的高温合金。这一新的消耗臭氧层物质添加剂工艺能够在单一的添加剂加工步骤中合成消耗臭氧层物质合金，从而绕过了传统的机械合金化工艺，因为机械合金化工艺是时间密集型的，与扩大生产不相容。该团队还包括通用电气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消耗臭氧层物质工艺与高温合金设计原则相结合，以提高多种温度制度下的强度和抗氧化性。一个新的基于微观结构的机器学习(ML)框架将被用来：(A)以综合的方式表示多尺度的微观结构，(B)开发性能/工艺联系，以及(C)加速新添加的消耗臭氧层物质合金的迭代设计。ML框架将通过一套广泛的实验获得信息，这些实验用于生成多尺度多模式微结构量化，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。这种方法将使我们能够根据我们合作伙伴感兴趣的两个设计目标来优化添加的消耗臭氧层物质合金，包括(1)中/高温，用于强度和微观结构稳定性至关重要的长寿命应用，以及(2)极端温度应用，即仅在短时间尺度下保持快速演变的微结构的强度和结构完整性。添加剂消耗臭氧层物质处理路线开启了快速评估合金行为的大门，首次能够使用有效的ML方法来优化新型消耗臭氧层物质合金的合金-组织-性能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.