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

这个赠款机会学术联络与工业（GOALI）设计材料革命和工程我们的未来（DMREF）项目将开发新的知识和战略，为超高温应用创造金属材料。 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，也将减少有害的碳排放。 要创建和研究的新材料是基于这样的概念，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。 氧化物增强物的存在可以提供显著的强度增强，并有助于保护材料在苛刻的高温环境下免受攻击。 将采用我们在NASA格伦研究中心（GRC）的合作者开创的方法，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。 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，逐层构建材料。 该项目将通过使用其他强化金属的策略来进一步改进这些材料。 该项目将产生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，这些知识将使用新的人工智能框架进行访问。 该框架将使团队能够优化这些添加剂ODS合金，以实现我们在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。 DMREF团队包括3名女性和1名黑人教师，并将为针对代表性不足的群体和退伍军人的学生研究经验提供多种机会。该项目将开发新的知识和策略，为航空航天和发电领域的广泛应用创造新的金属材料类别。最近由NASA GRC的合作者开发的一种用于创建氧化物弥散强化（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工艺能够在单一的添加剂加工步骤中合成ODS合金，从而绕过了传统的机械合金化工艺，该工艺耗时且与大规模制造不一致。 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将新的添加剂ODS工艺与高温合金设计原则相结合，以提高多个温度体系的强度和抗氧化性。一种新的基于微结构的机器学习（ML）框架，将被用来：（a）代表多尺度的微观结构，在一个全面的方式，（B）开发性能/加工的联系，（c）加速迭代设计的新添加剂ODS合金。ML框架将通过一系列广泛的实验来了解，这些实验用于生成多尺度多峰微观结构量化，评估机械和氧化行为，并对这些合金独特性能背后的机制进行基本理解。这种方法将使添加剂ODS合金在我们的合作伙伴感兴趣的两个设计目标上得到优化，包括（1）中/高温，用于长寿命应用，其中强度和微观结构稳定性至关重要，以及（2）极端温度应用，其中仅需要在短时间内保持快速发展的微观结构的强度和结构完整性。 添加剂ODS工艺路线打开了快速评估合金行为的大门，首次实现了有效ML方法用于新型ODS合金的合金-微观结构-性能优化。该奖项反映了NSF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.