Cooperative Agreement 70NANB15H296

This $359,990 project grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports research at Rutgers, The State University to establish a physics-informed machine learning (PIML) framework that can accurately predict the fatigue life and scattering behavior of metal components produced through laser powder bed fusion (LPBF) additive manufacturing (AM) processes. The key objectives are to: 1) characterize the quality and fatigue properties of...

This $249,999 Project Grant awarded by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) aims to establish a physics-informed machine learning (PIML) framework to enable accurate and transparent predictions of fatigue life and its variation for metal additive manufacturing (AM) components. The project will fabricate baseline fatigue samples of SS316L and Ti-6Al-4V alloys produced via laser powder bed fusion (LPBF) AM and post-processing, characterize their...

This Engineering Research Initiation (ERI) award of $200,000, funded by the National Science Foundation's Division of Civil, Mechanical, and Manufacturing Innovation (CFDA 47.041) and awarded to the University of North Florida beginning July 1, 2026, will develop an integrated quality-control system to reduce defects in laser powder bed fusion (LPBF) additive manufacturing. The primary deliverables include a lightweight computer vision model capable of detecting and quantifying recoater...

This Project Grant from the National Science Foundation's Division of Civil, Mechanical, and Manufacturing Innovation, under the Engineering program (CFDA 47.041), provides $598,148 to the University of California, San Diego for the period of January 1, 2023 through December 31, 2025. The award will support the development of new analytical frameworks to improve control of metal additive manufacturing processes through theoretical and experimental investigations. Specifically, the university...

This Project Grant award from the National Science Foundation's (NSF) Integrative Activities program (CFDA 47.083) provides $300,000 to Clemson University to advance the capability of metal additive manufacturing (AM) technologies in developing radiation-tolerant single-phase concentrated solid-solution alloys (SP-CSAS). The research aims to generate new knowledge on SP-CSAS' radiation tolerance and their intrinsic compositions, structures, and defects through experimental and computational...

This Project Grant award of $250,000 from the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research into quantifying the uncertainty in the process-structure-property-surface (PSPS) relationship for additive manufacturing of Inconel 718 parts. The primary objective is to leverage uncertainty quantification (UQ) and machine learning (ML) frameworks to analyze microstructure and surface topography data, and derive data-driven models that...

This $255,934 National Science Foundation Technology, Innovation, and Partnerships grant will support Exlattice, Inc.'s development of machine learning-powered additive manufacturing simulation software. Under the SBIR Phase I Project Grant, Exlattice will create a proof-of-concept for 3-5 orders of magnitude faster process simulation software to predict manufacturing failures from high temperature, residual distortion, and residual stresses. The proposed hybrid data-driven and physics-based...

This National Science Foundation (NSF) GOALI (Grant Opportunities for Academic Liaison with Industry) project grant, awarded under the Engineering program (CFDA 47.041), aims to develop uncertainty-aware modeling and control strategies for the microscale selective laser sintering (?-SLS) additive manufacturing process. The $609,991 award, effective September 1, 2024 through August 31, 2027, will enable researchers at the University of Texas at Austin to: Develop physics-based models with...

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 parameters...

The National Science Foundation (NSF) awarded a $75,000 Project Grant under the Engineering (CFDA 47.041) program to the University of California, Santa Barbara (UCSB) to support a collaborative research project titled "DMREF: Data-Driven Discovery of the Processing Genome for Heterogeneous Superalloy Microstructures." The project aims to revolutionize the creation of novel engineering alloys by developing a data-driven platform called "DRAGONS" (Data-Driven Recursive...