MIT News - Machine learning 2024年10月09日
Artificial intelligence meets “blisk” in new DARPA-funded collaboration
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美国国防高级研究计划局 (DARPA) 最近颁发了一项奖项,将麻省理工学院 (MIT)、卡内基梅隆大学 (CMU) 和利哈伊大学 (Lehigh) 的研究人员汇聚在一起,共同参与“合金结构多目标工程与测试 (METALS)”项目。该团队将研究用于多材料结构中形状和成分梯度同时优化的全新设计工具,这些工具与新的高通量材料测试技术相辅相成,特别关注涡轮机械(包括喷气式和火箭发动机)中常见的叶盘 (blisk) 几何形状,将其作为具有代表性的挑战性问题。研究人员希望通过将经典力学分析与尖端的生成式 AI 设计技术相结合,释放成分梯度合金的塑性储备,从而在以前无法达到的条件下安全运行。

🚀 **多材料结构的形状和成分梯度优化** 该项目旨在开发用于多材料结构中形状和成分梯度同时优化的全新设计工具,这些工具与新的高通量材料测试技术相辅相成。研究人员将特别关注涡轮机械(包括喷气式和火箭发动机)中常见的叶盘 (blisk) 几何形状,将其作为具有代表性的挑战性问题。 叶盘的不同位置需要不同的热机械性能和性能,例如抗蠕变、低周疲劳、高强度等。大规模生产还需要考虑成本和可持续性指标,例如合金的采购和回收。 目前,使用标准制造和设计程序,人们必须想出一个单一的“神奇材料”,一个组成和处理参数来满足“一个部件一个材料”的约束。理想的性能通常是相互排斥的,这会导致低效的设计权衡和妥协。 虽然单一材料方法可能在一个部件的特定位置上是最佳的,但它可能会使其他位置暴露在失效风险中,或者可能需要将一种关键材料贯穿整个部件,而实际上它可能只在一个特定位置需要。随着能够实现基于体素的成分和性能控制的增材制造工艺的快速发展,该团队看到了结构部件性能跃升的独特机会。

🧬 **先进的增材制造技术** 该项目将利用增材制造技术的快速发展,探索基于体素的成分和性能控制,以实现结构部件性能的跃升。增材制造能够在部件的不同位置实现材料成分和性能的梯度变化,从而满足不同的性能需求。 该项目将整合经典力学分析、生成式 AI 设计技术和增材制造技术,以实现成分梯度合金的塑性储备的释放,从而在以前无法达到的条件下安全运行。

🔬 **跨学科研究团队** 该项目由来自麻省理工学院 (MIT)、卡内基梅隆大学 (CMU) 和利哈伊大学 (Lehigh) 的研究人员组成,拥有跨学科的专业知识,涵盖混合集成计算材料工程、基于机器学习的材料和工艺设计、精密仪器、计量学、拓扑优化、深度生成模型、增材制造、材料表征、热结构分析和涡轮机械等领域。 该团队将共同努力,开发新的计算方法,构建在极端条件下运行的测试台,最终实现突破性的能力,为未来的推进系统奠定基础,并利用数字设计和制造技术。

🔧 **应用前景** 该项目的研究成果预计将对航空航天技术产生重要影响,并可能实现更可靠、可重复使用的火箭发动机,为下一代重型运载火箭提供动力。该项目将为航空航天领域带来突破性的创新,并推动可持续性和高效的材料设计与制造的发展。

🚀 **项目资助** 该研究由美国国防高级研究计划局 (DARPA) 资助,合同号为 HR00112420303。作者表达的观点、意见和/或发现仅代表作者本人,不应被解释为代表美国国防部或美国政府的官方观点或政策,也不应推断出任何官方认可。

A recent award from the U.S. Defense Advanced Research Projects Agency (DARPA) brings together researchers from Massachusetts Institute of Technology (MIT), Carnegie Mellon University (CMU), and Lehigh University (Lehigh) under the Multiobjective Engineering and Testing of Alloy Structures (METALS) program. The team will research novel design tools for the simultaneous optimization of shape and compositional gradients in multi-material structures that complement new high-throughput materials testing techniques, with particular attention paid to the bladed disk (blisk) geometry commonly found in turbomachinery (including jet and rocket engines) as an exemplary challenge problem.

“This project could have important implications across a wide range of aerospace technologies. Insights from this work may enable more reliable, reusable, rocket engines that will power the next generation of heavy-lift launch vehicles,” says Zachary Cordero, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Aeronautics and Astronautics (AeroAstro) and the project’s lead principal investigator. “This project merges classical mechanics analyses with cutting-edge generative AI design technologies to unlock the plastic reserve of compositionally graded alloys allowing safe operation in previously inaccessible conditions.”

Different locations in blisks require different thermomechanical properties and performance, such as resistance to creep, low cycle fatigue, high strength, etc. Large scale production also necessitates consideration of cost and sustainability metrics such as sourcing and recycling of alloys in the design.

“Currently, with standard manufacturing and design procedures, one must come up with a single magical material, composition, and processing parameters to meet ‘one part-one material’ constraints,” says Cordero. “Desired properties are also often mutually exclusive prompting inefficient design tradeoffs and compromises.”

Although a one-material approach may be optimal for a singular location in a component, it may leave other locations exposed to failure or may require a critical material to be carried throughout an entire part when it may only be needed in a specific location. With the rapid advancement of additive manufacturing processes that are enabling voxel-based composition and property control, the team sees unique opportunities for leap-ahead performance in structural components are now possible.

Cordero’s collaborators include Zoltan Spakovszky, the T. Wilson (1953) Professor in Aeronautics in AeroAstro; A. John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering; Faez Ahmed, ABS Career Development Assistant Professor of mechanical engineering at MIT; S. Mohadeseh Taheri-Mousavi, assistant professor of materials science and engineering at CMU; and Natasha Vermaak, associate professor of mechanical engineering and mechanics at Lehigh.

The team’s expertise spans hybrid integrated computational material engineering and machine-learning-based material and process design, precision instrumentation, metrology, topology optimization, deep generative modeling, additive manufacturing, materials characterization, thermostructural analysis, and turbomachinery.

“It is especially rewarding to work with the graduate students and postdoctoral researchers collaborating on the METALS project, spanning from developing new computational approaches to building test rigs operating under extreme conditions,” says Hart. “It is a truly unique opportunity to build breakthrough capabilities that could underlie propulsion systems of the future, leveraging digital design and manufacturing technologies.”

This research is funded by DARPA under contract HR00112420303. The views, opinions, and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. government and no official endorsement should be inferred.

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DARPA 合金结构 增材制造 人工智能 涡轮机械
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