The National Science Foundation (NSF) Division of Civil, Mechanical, and Manufacturing Innovation awarded a $207,158 Project Grant titled "COLLABORATIVE RESEARCH: FUSION OF SILOED DATA FOR MULTISTAGE MANUFACTURING SYSTEMS: INTEGRATIVE PRODUCT QUALITY AND MACHINE HEALTH MANAGEMENT" to The University of Iowa. This 3-year grant, which commenced on January 1, 2024, supports the development of data-driven methodologies to monitor and optimize multistage manufacturing systems through the...
The National Science Foundation's (NSF) Division of Civil, Mechanical, and Manufacturing Innovation awarded a $223,843 Project Grant to Rutgers, The State University located in New Brunswick, New Jersey. The award, which runs from January 1, 2024 to December 31, 2026, is under the NSF Engineering program (CFDA 47.041) to develop data-driven methodologies for integrative monitoring and operation of multistage manufacturing systems. The key products and services to be delivered include:...
The National Science Foundation (NSF) Directorate for Engineering (CFDA Program 47.041) awarded a $344,126 Project Grant to the University of Wisconsin System for a 3-year research project focused on developing new mathematical methods and computational tools to enable the use of complex data formats, such as visual and thermal images, for advanced model predictive control (MPC) systems. The key objectives are to: Integrate concepts from control theory, topology, machine learning, and Bayesian...
This $398,786 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research at North Carolina State University to develop a privacy-preserving collaborative condition monitoring and decision-making methodology for distributed manufacturing systems. The project aims to enable multiple geographically distributed manufacturing facilities to collectively utilize their data to construct more effective monitoring and decision-making models, while...
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 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $361,985 to the University of Wisconsin System to develop predictive mathematical models for manufacturing processes involving complex polymeric materials. The project aims to leverage recent advances in experimental methods and data science to better understand the relationship between the flow and microstructure of these materials, which is critical for applications like printed...
This National Science Foundation Project Grant of $728,684 supports research at The Pennsylvania State University to develop new machine learning and mathematical methods for quality control in manufacturing. Funded under the Engineering program (CFDA 47.041), the award runs from June 1, 2022 to May 31, 2025. Specifically, the university will first create improved algorithms for identifying a subset of key process variables in continuous manufacturing that best indicate overall plant control,...
The National Science Foundation (NSF) awarded a 3-year, $289,495 Project Grant to The Pennsylvania State University (Penn State) under the NSF Engineering program (CFDA 47.041) to develop an integrated materials-manufacturing-controls framework for improving the efficiency and resilience of manufacturing systems. The research will focus on enhancing the understanding of the interactions between raw material properties, manufacturing processes, and process control to enable more integrated and...
This $183,194 National Science Foundation (NSF) Engineering program (CFDA 47.041) award to Case Western Reserve University supports fundamental research on designing a next-generation process sensing-machine learning architecture for capturing manufacturing process dynamics. The research aims to develop high-accuracy modeling and high-efficiency computation capabilities to discover the underlying causal relationships between process parameters, sensor data, and product quality. This will...
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)...