DMREF/GOALI/Collaborative Research: Physics-Informed Artificial Intelligence for Parallel Design of Metal Matrix Composites and their Additive Manufacturing

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This Designing Materials to Revolutionize and Engineer our Future (DMREF) research enables physics-informed artificial intelligence (AI) design of metal materials reinforced with ceramic particles (metal matrix composites) and their additive manufacturing (3D printing). Such materials can exhibit superior mechanical performances at higher temperatures relative to the same metal material without ceramic reinforcements. Additive manufacturing provides unprecedented fabrication capability for high performance, lightweight structural components made from metal matrix composite materials. However, the design of metal matrix composites and their additive manufacturing is largely performed with expensive, time consuming trial and error methodologies; quality assurance of such parts is similarly challenged. AI-guided design and qualification of materials and their manufacturing can significantly lower the time and cost barriers to such technologies. The basic research performed in this program will fill critical gaps to enable AI discovery and optimization of these materials and their manufacturing toward reducing deployment times and costs by half, to meet the Materials Genome Initiative vision. The outreach programs and diversity, equity, and inclusion plans include AI manufacturing course curricula spanning kindergarten - graduate which include example problems and tools developed from this program. Atlanta and Salt Lake City high school teachers and students from underrepresented minority populations will receive hands-on experience and instruction in these curricula. The research maintains and expands robust programs supporting fundamental research in alloys, ceramics, and their composites; support modalities for free-flowing interactions among universities (Georgia Tech and Utah), start-up ventures (GOALI partner Elementum 3D), and national laboratories (Air Force Research Laboratory); expand investments in automated materials manufacturing research to ensure the U.S. is the leader in the field by 2030; all using, when appropriate, computational methods, data analytics, machine learning, and autonomous experimental 3D characterization.This research program enables physics-informed artificial intelligence (AI) - driven parallel design of metal matrix composites and their additive manufacturing. The concept of AI that discovers and optimizes new materials and their Additive Manufacturing (AM) in parallel promises to further revolutionize AM but is yet to be realized. Basic research is to enable autonomous AI discovery and optimization of materials and their manufacturing toward reducing deployment times and costs by half, to meet the Materials Genome Initiative vision. Five critical data-driven algorithmic gaps will be filled: 1) data analysis-interpretation-curation algorithms to enable automatic, pedigreed data curation from requisite process-structure-property data sources. 2) Algorithms that automate data cleaning and concatenation of databases so that AI can modify and append the data spaces when new data sources or data features are incorporated into a research problem. 3) Algorithms that automate data feature mapping across multiple length and time scales to complete process-structure-property data ontologies. 4) Data feature engineering algorithms that improve the AI performance. 5) Process-structure-property machine learning models that learn global relationships across multiple nested submodels. Physics-based models and experiments will be advanced to predict and verify their utility in discovering and optimizing metal matrix composites and their additive manufacturing at multiple length and time scales. High throughput one-dimensional, two-dimensional, and three-dimensional characterization data analyses will be automated. GOALI partner Elementum 3D will provide a techno-economic baseline study of commercializing a new metal matrix composite for additive manufacturing to be used as an overall assessment metric for the advancements made in this program. The development of a new AM test artifact will benefit researchers around the globe. The protocols and standards developed for automating data workflows can benefit materials science and engineering researchers around the world by increasing access to high-throughput and high-fidelity data sources, including machine learning models and AI knowledge systems, for all kinds of materials and manufacturing processes.This project is jointly funded by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) in the Directorate for Engineering (ENG), the Division of Information and Intelligent Systems (IIS) in the Directorate for Computer and Information Science and Engineering (CISE), and the Division of Materials Research (DMR) in the Directorate for Mathematical and Physical Sciences (MPS).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.