This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $491,583 to the Texas A&M Engineering Experiment Station (Tees) to develop a comprehensive education and hands-on training program that integrates computational materials science (CMS), machine learning/artificial intelligence techniques, and accelerated high-performance computing. The program aims to equip the future workforce with cutting-edge skills to drive innovation in...
This $500,000 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) supports research at Northeastern University to advance computational and data-enabled science and engineering. The project aims to develop a theoretical foundation for "Mechanics Informatics" - a new approach to learning material properties from a single, optimized mechanical test rather than requiring many tests. This will enable more efficient and cost-effective design of...
This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $500,000 to the University of Texas at Arlington (UTA) to support research integrating advanced sensing and artificial intelligence (AI) to characterize the geometry-property relationship of engineered metamaterials during additive manufacturing (AM) processes. The goal is to establish a computational framework for property-centric monitoring and optimization of AM to achieve...
This Project Grant award for $289,065, provided by the National Science Foundation's (NSF) Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) and the Division of Information and Intelligent Systems (IIS), will fund research to accelerate the understanding, inverse design, and fabrication of architected materials with tailorable dynamic behaviors. The goal is to develop an integrated framework that seamlessly combines graph network theory, machine learning, numerical...
The National Science Foundation Division of Materials Research awarded a $1.8 million Project Grant to the Texas A&M Engineering Experiment Station (doing business as Tees) to support the "DMREF: AI-GUIDED ACCELERATED DISCOVERY OF MULTI-PRINCIPAL ELEMENT MULTI-FUNCTIONAL ALLOYS" project. The funding period is from October 1, 2021 to September 30, 2025. Under this award, Tees will utilize artificial intelligence-guided methods to rapidly discover new multi-principal element alloys...
This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research to address challenges in high deposition rate metal additive manufacturing. The $570,160 award to the University of Texas Rio Grande Valley aims to investigate and control the metallurgical transformation of deposited material by simultaneously employing multiple thermal energy sources, such as laser, induction heating, and ultrasonic vibration. The key research tasks include...
The National Science Foundation (NSF) awarded a $499,999 Project Grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to Arizona State University (ASU) for the project "ACED: COMPAM: ENABLING COMPUTATIONAL ADDITIVE MANUFACTURING." The goal of this 2-year project is to develop a computational framework that can dramatically reduce the time required to predict and optimize microstructures in additive manufacturing (AM) or 3D printing, from days to...
The National Science Foundation (NSF) awarded a $399,916 Project Grant under its Engineering program (CFDA 47.041) to the University of Nevada, Reno (UNR) to fund research on developing ultrastrong and ultraelastic metallic alloys using artificial intelligence (AI) enabled automated design. The goal of this 16-month project is to leverage AI, computational modeling, and experimental tools to rapidly design, synthesize, and test new metallic alloy compositions that can withstand extreme stress...
The National Science Foundation (NSF) awarded a $183,420 Project Grant under the Engineering program (CFDA 47.041) to the University of Texas at Arlington (UTA) Office of Research Administration Division. The grant will fund a collaborative research project to develop an integrated materials-manufacturing-controls framework for enhancing the efficiency and resilience of manufacturing systems. Key objectives include: 1) quantifying the effects of raw ingredients on feedstock properties, 2)...
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 generate...
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 "surprise" observations, safeguarding extrapolation using digital twins, and advancing knowledge capture on process-material relationships. The interdisciplinary research team aims to validate these approaches by discovering new pathways to manufacture high-entropy alloy products with enhanced performance and affordability. In addition to the research, the project will provide hands-on training and education opportunities, leveraging Tees' expertise and collaborations with the Manufacturing USA network, industry partners, and academic institutions.