Researchers aim to disrupt how new materials are leveraged in the design process

SRI’s system navigates various factors like cost and time to build, structural and thermal performance, and more

SRI chosen by DARPA to develop next-generation computational design of metallic parts and intelligent testing of alloys.

SRI has been selected by the Defense Advanced Research Projects Agency (DARPA) to deliver advanced technology for its recently launched Multiobjective Engineering and Testing of ALloy Structures (METALS) program.

DARPA METALS aims to break today’s one-material-per-part paradigm, which can lead to vulnerabilities and reduced life when highly engineered components experience austere environments. The goal of the four-year program is to develop technologies that treat material selection — particularly metallic alloys — as a continuous variable in design that can be tailored across a single part.

Integrating material properties into the design process

SRI’s approach will radically expand the design space, enabling breakthroughs in system-level performance, cost, and sustainability. SRI and its collaborators will develop new ways to integrate materials into the design process and break new ground in rapid, intelligent testing for advanced material properties. SRI will leverage AI-based material informatics to inform design space exploration through testing.

The work is being led by researchers in SRI’s Future Concepts division, previously known as the Palo Alto Research Center (PARC), a team that has been at the forefront of digital design and manufacturing research for more than a decade.

Today, designers have to work with a discrete selection of materials that are provided to them by material scientists that are not necessarily optimal for the specific needs of a component or application.

Building a new way to think about designing materials into parts

“There is no design tool that can vary materials in three-dimensional space while simultaneously accounting for metallurgical constraints,” said Morad Behandish, Research Director for Design and Digital Manufacturing at SRI and Principal Investigator on the project. “We’re building a design tool that will allow us to create components with desired properties on the fly, place them precisely where needed, test them in the environment in which they’ll be used, and envision new alloys that do not yet exist.”

Behandish added, “metal additive manufacturing allows us to make such components; however, predicting advanced long-term properties like fatigue, creep, and oxidation in high temperatures in a scalable way demands radical new ways of testing that we will develop in this project, unlocking the true potential of modern manufacturing and its adoption.”

“The system will change design shape and materials simultaneously to optimize for many factors.”

SRI has teamed up with the University of Illinois Urbana-Champaign (UIUC) and the University of California San Diego (UCSD) to develop MIDAS-X: Material-Integrated Design with Agile Sampling for experimental testing. SRI’s capabilities in generative design exploration and AI, UIUC’s novel testing methods, and UCSD’s unique manufacturing capabilities make a strong team with a highly innovative approach. SRI aims to build design tools with an evolving understanding of materials feasibility grounded in rapid testing to make the best use of existing materials and discover new materials.

Designing first to prove out material characterization

SRI’s system will change design shape and materials simultaneously to optimize for many factors and navigate a trade space of cost and time to build, structural and thermal performance, supply chain risks, and more. The system relies on a unique approach, based on UIUC’s recent discoveries, to predict advanced properties from nanoscale events observable under an electron microscope enabling orders of magnitude faster material characterization.

“This program marks a turning point in how we design, make, and validate materials at an exciting crossroads of innovation in computational design, materials science, digital manufacturing, and AI,” Behandish emphasized. “The biggest impact will be on high-end applications, especially when human life is trusted with engineered equipment, where the reliance on outdated engineering methods in current practice takes significant time and cost in design cycles and material systems adoption.”

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