SAM #DARPA-SN-26-81

DARPA RFI: Revolutionizing Industrial Scale Materials Processing

This opportunity is a Request for Information (RFI) from the Defense Advanced Research Projects Agency (DARPA) focused on revolutionizing industrial-scale materials processing. - Agency: Defense Advanced Research Projects Agency (DARPA) - Purpose: Gather input from industrial producers of raw materials (cast, rolled, and forged metals, bulk ceramics, composites) - Objective: Inform a future program to reduce process uncertainty and improve yield and product quality using material informatics and physical modeling - Participation: Open to large-scale producers willing to provide multi-year, sensitive, proprietary processing data - Collaboration: Participants may work with teams including private companies and universities - No specific OEMs, vendors, products, part numbers, or quantities are listed - Commercial opportunity: Potential for future program participation and collaboration with DARPA - Requirements: Ability to share proprietary data and collaborate in research efforts

Description

BACKGROUNDDARPA is seeking engagement from industrial producers of raw materials including cast, rolled, and forged metals, bulk ceramics, and composites to aid in developing a future program. This RFI is meant to provide the reasoning behind our need for engagement and to present the potential value proposition. Included is a simple survey to gather information on willingness to participate in a future effort.DARPA is considering a program to bring together performer teams composed of the best material scientists, process engineers, and data scientists to revolutionize industrial scale materials processing. Currently, process uncertainty forces system designers to rely on minimum expected property values (e.g., Metallic Materials Properties Development and Standardization (MMPDS)) to guarantee reliability. As a result, even though most processed materials exceed these data sheet minimums, designers must either sacrifice potential system performance margins or use unnecessarily expensive materials to account for statistical variability. Furthermore, this same process uncertainty inevitably yields some products that fall below minimum specifications, reducing overall yield and forcing producers into costly re-processing or scrapping of materials.DARPA believes recent advances in material informatics and physical modeling can demystify the "art" of large-scale processing. While modern industry excels at minimizing variability to meet current baselines, there is significant room for growth; for example, many 7000 series aluminum alloys have a potential performance margin exceeding 15% at fixed plate thicknesses and heat treatments. The challenge lies in the complex, interconnected nature of production variables—such as slight fluctuations in composition, heat treatment, rolling or forging temperatures, and environmental conditions. Because these factors are highly interdependent, optimizing one step does not guarantee a better final product if a subsequent step introduces new variations. Currently, navigating this complexity relies heavily on the intuitive, tacit knowledge that operators develop over decades—an approach that is virtually impossible to replicate or scale.DARPA aims to develop quantitative, physics-informed techniques to uncover the exact causes of product variability. This approach allows producers to fix inefficiencies using their current processes, avoiding the need for expensive capital investments or major facility overhauls. As a result, producers can increase yield and offer superior products at standard market prices, easily outcompeting lower-quality alternatives. To make this work, the program needs access to multi-year processing data from large-scale producers. DARPA completely understands that this data is sensitive and vital to your competitive advantage. Therefore, while DARPA is a government agency, the teams executing this program will likely include private companies and universities equipped to manage and protect proprietary information.

View original listing