This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports a collaborative research project focused on developing intelligent scan sequence generation to reduce local overheating, distortion, and residual stress in laser powder bed fusion (LPBF) additive manufacturing. The $250,000 award, spanning January 1, 2025 to December 31, 2027, will enable researchers at the University of Pittsburgh to mathematically, numerically, and experimentally...
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 Project Grant award from the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) supports research by Carnegie Mellon University (CMU) to investigate an innovative power field control strategy for achieving prescribed thermal histories throughout parts produced by powder bed fusion additive manufacturing. The $649,345 award, effective May 1, 2025 through April 30, 2030, aims to understand the effects of power field control on porosity, microstructure,...
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 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 $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...
This Project Grant award, funded by the National Science Foundation (NSF) Engineering program (CFDA 47.041), supports research to develop methodologies for monitoring and improving personalized manufacturing processes, particularly for one-of-a-kind parts produced using additive manufacturing. The award, totaling $129,624 and spanning from June 1, 2024, to May 31, 2027, will enable researchers at the University of Oklahoma to establish a novel latent space monitoring approach based on...
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 that...
The National Science Foundation (NSF) Engineering program (CFDA 47.041) awarded a $300,001 Project Grant to the University of Southern California (USC) for the project "COLLABORATIVE RESEARCH: PROCESS-INFORMED LATENT SPACE REPRESENTATION, LEARNING, AND MONITORING FOR SMART PERSONALIZED MANUFACTURING." This 3-year award, effective June 1, 2024, will develop novel methodologies to enable process monitoring and geometric quality control for personalized manufacturing of one-of-a-kind...
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...