This National Science Foundation (NSF) Engineering program (CFDA 47.041) Project Grant award of $262,929 will support research to develop an in-situ processing data-driven framework that can link manufacturing processes to environmentally-related performance for laser powder bed fusion (L-PBF) metal additive manufacturing. The key objectives are to: Establish a physics-constrained artificial intelligence (PCAI)-based surrogate model to predict part-scale residual stress and microstructures using...
This $300,001 Project Grant awarded by the National Science Foundation (NSF) Division of Civil, Mechanical, and Manufacturing Innovation under the NSF Directorate for Engineering (CFDA 47.041) supports research at Virginia Polytechnic Institute & State University (Virginia Tech) to define, evaluate, and improve data quality for effective artificial intelligence (AI) deployment in manufacturing. The key products and services to be delivered under this 3-year award include: Developing...
The National Science Foundation awarded a $272,440 project grant to Virginia Polytechnic Institute & State University from September 2021 through August 2024 under the Engineering (47.041) federal grant program. The grant supports collaborative research titled "AI-Driven Multi-Scale Design of Materials under Processing Constraints." The research aims to develop artificial intelligence techniques to design new materials while accounting for real-world manufacturing limitations. By...
This National Science Foundation (NSF) Engineering (CFDA 47.041) Project Grant award to Virginia Polytechnic Institute & State University (Virginia Tech) in the amount of $2,999,319 provides funding to develop an "Anisotropic Multi-Axis Layerless Additive Manufacturing" (ANIMAL AM) system. This system leverages robotic arms with material extrusion tools and multi-axis printing toolpaths to enable precise 3D orientation control of composite materials. The goal is to transform...
This $224,493 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports fundamental research to understand the physical mechanisms of material removal in a novel manufacturing process called magnetically enhanced laser-induced plasma (M-ELIP) micromachining. The research team from Virginia Polytechnic Institute & State University (Virginia Tech) will characterize material removal and defect formation, develop physics-based models to predict...
This Project Grant award from the National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation provides $1,177,594 to support research titled "DMREF/GOALI/COLLABORATIVE RESEARCH: PHYSICS-INFORMED ARTIFICIAL INTELLIGENCE FOR PARALLEL DESIGN OF METAL MATRIX COMPOSITES AND THEIR ADDITIVE MANUFACTURING." The funding period is from September 1, 2021 to August 31, 2025. The research is being conducted under the NSF Engineering program (CFDA 47.041), which aims to...
This National Science Foundation (NSF) Project Grant award under CFDA 47.041 - Engineering provides $250,000 in funding to the University of Pittsburgh for a collaborative research project focused on "Towards Intelligent Scan Sequence Generation to Reduce Local Overheating, Distortion, and Residual Stress in LPBF Additive Manufacturing." The objective is to mathematically, numerically, and experimentally investigate the relationships between optimal scan sequences, temperature...
This National Science Foundation (NSF) Engineering Program (CFDA 47.041) Project Grant award of $649,345 to Carnegie Mellon University supports research to investigate an innovative power field control strategy. The goal is to achieve prescribed thermal histories throughout parts produced using powder bed fusion additive manufacturing. The research aims to: (1) understand the effects of power field control on porosity and microstructure, including solidification and solid-state...
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...
This $1.02 million National Science Foundation Project Grant, funded under the Engineering program (CFDA 47.041), supports the development of computation-informed deep learning approaches to enable real-time prognosis of melt pool dynamics for additive manufacturing. Over the three-year period from July 2022 to June 2025, the awardee, Rutgers University, will create an integrated model using computational fluid dynamics simulations and deep learning to predict melt pool overheating during...