This $399,161 National Science Foundation project grant supports research at the University of Texas at Austin to develop new algorithms and simulations for co-designing the geometry and fabrication plans for direct-ink writing 3D printing. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), this three-year award will transform additive manufacturing design tools by allowing users to specify an object's desired mechanical behavior and automatically...
This $310,000 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research to develop new computational methods for predicting the properties of disordered materials. The project at Arizona State University aims to create efficient and explainable machine learning models that can link the microstructure of two-phase composite materials to their mechanical and thermal transport properties. The research will focus on...
This $500,001 Project Grant was awarded by the National Science Foundation (CFDA #47.070 - Computer and Information Science and Engineering) to the Texas A&M Engineering Experiment Station (Tees) to conduct research on advancing autonomous, materials-on-demand manufacturing capabilities through the convergence of manufacturing, AI, and materials science.
The key objectives are to develop foundational principles for shape-constrained machine learning, effective handling of...
The National Science Foundation awarded North Carolina State University a two-year $400,000 Project Grant under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to develop process monitoring methods for improving product quality in electron beam powder bed fusion additive manufacturing. The university will research an in-situ quality control framework using real-time sensing data to monitor product quality during manufacturing and adaptively adjust...
This $700,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support The Johns Hopkins University's research on amorphous metal additive manufacturing. The project aims to develop simulation-informed models and computational tools to enable design of additively manufactured amorphous metals with desired strength and toughness properties. This will involve using machine learning to quantify structural order...
This National Science Foundation (NSF) Project Grant award, under the Engineering program (CFDA 47.041), provides $650,000 in funding to Carnegie Mellon University (CMU) from June 1, 2024 to May 31, 2027. The project aims to fully understand the mechanisms controlling shape distortion in additive manufacturing (AM) processes, particularly during the sintering of nano/microparticles. The research involves integrated experimental and theoretical work to identify critical AM process parameters...
This $199,040 Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop new classes of computational algorithms that combine the benefits of direct computer simulations and the speed of machine learning predictions. The project, titled "XTRIPODS: HYBRID SCIENCE-MACHINE LEARNING SOLVERS FOR NANOPHOTONICS AND METAMATERIALS," will embed scientific knowledge into the machine learning...
The National Science Foundation awarded a $300,000 Project Grant to the University of Michigan under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to develop an artificial intelligence framework called the AI-enabled Microstructure Model Builder (AMMBER). AMMBER will autonomously determine input parameters for phase-field models based on a variety of data sources to establish constraints on model parameters. It will leverage automated data pipelines...
This $262,929 Project Grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research to develop a physics-constrained artificial intelligence (PCAI) framework for in-situ monitoring-enabled multiscale modeling and optimization of advanced manufactured metal components. The key objectives are to:
Establish a PCAI-based surrogate model that incorporates in-situ monitoring data to predict part-scale residual stress and microstructures for laser...
This National Science Foundation (NSF) Designing Materials to Revolutionize and Engineer our Future (DMREF) Project Grant award, valued at $150,000 and running from October 1, 2023 to September 30, 2027, supports collaborative research to develop simulation-informed models for additive manufacturing of amorphous metals. The research team at The Washington University aims to derive meaningful measures of material structure from electron nanodiffraction and simulation data, and build predictive...
This $499,999 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is funding the development of a computational framework to accelerate the prediction and optimization of microstructures in additive manufacturing (AM) processes. The project aims to drastically reduce the time required to model material microstructures from days to minutes, enabling faster and more efficient design and manufacturing of advanced materials with targeted properties. This will be achieved by designing novel graph neural networks that incorporate physical laws to enable accurate and generalizable microstructure predictions. The expected impacts include accelerating the discovery of new materials, enhancing the quality of manufactured components, and expanding the application of AM in critical industries such as aerospace, healthcare, and energy. The award is being led by Arizona State University and does not include any planned subawards.