MIT-Led Team Develops AI-Powered Design Tools for Next-Gen Aerospace Components

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On Wed, 9 Oct, 4:02 PM UTC

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Researchers from MIT, Carnegie Mellon, and Lehigh University have received DARPA funding to create AI-driven design tools for optimizing shape and material composition in aerospace structures, with a focus on improving rocket engine components.

DARPA Funds AI-Driven Aerospace Innovation

The U.S. Defense Advanced Research Projects Agency (DARPA) has awarded funding to a collaborative research team from Massachusetts Institute of Technology (MIT), Carnegie Mellon University (CMU), and Lehigh University under the Multiobjective Engineering and Testing of Alloy Structures (METALS) program. This initiative aims to develop cutting-edge design tools that leverage artificial intelligence for the optimization of aerospace components 12.

AI-Powered Design for Aerospace Advancements

The research focuses on creating novel design tools that simultaneously optimize shape and compositional gradients in multi-material structures. These tools will complement new high-throughput materials testing techniques, with a particular emphasis on the bladed disk (blisk) geometry commonly found in turbomachinery, including jet and rocket engines 1.

Zachary Cordero, the project's lead principal investigator from MIT, highlights the potential impact: "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" 1.

Overcoming Design Challenges with AI

The research addresses a critical challenge in aerospace component design. Currently, engineers must develop a single material composition and set of processing parameters to meet "one part-one material" constraints. This approach often leads to inefficient design trade-offs and compromises, as desired properties can be mutually exclusive 1.

The team's approach leverages recent advancements in additive manufacturing processes that enable voxel-based composition and property control. By combining classical mechanics analyses with cutting-edge generative AI design technologies, the researchers aim to unlock new possibilities in material performance and component design 1.

Interdisciplinary Collaboration for Innovation

The project brings together experts from various fields, including:

  • Hybrid integrated computational material engineering
  • Machine learning-based material and process design
  • Precision instrumentation and metrology
  • Topology optimization
  • Deep generative modeling
  • Additive manufacturing
  • Materials characterization
  • Thermostructural analysis
  • Turbomachinery 1

This interdisciplinary approach allows for a comprehensive exploration of the challenges and opportunities in aerospace component design.

Implications for Future Aerospace Technologies

The research has the potential to significantly impact the aerospace industry. By enabling more efficient and optimized designs, the project could lead to:

  1. Improved performance of rocket engines and other aerospace components
  2. Enhanced reliability and reusability of critical parts
  3. More sustainable and cost-effective manufacturing processes
  4. Advancements in materials science and engineering 12

A. John Hart, Professor and Head of the Department of Mechanical Engineering at MIT, emphasizes the project's unique opportunity: "It is a truly unique opportunity to build breakthrough capabilities that could underlie propulsion systems of the future, leveraging digital design and manufacturing technologies" 1.

As this DARPA-funded research progresses, it has the potential to revolutionize the design and performance of aerospace components, paving the way for the next generation of space exploration and aviation technologies.

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