NSF Engines Development Award: Advancing additive manufacturing technologies (FL)

This Regional Innovation Engines Development Award is focused on piloting the AM (Additive Manufacturing) Forward partnership announced in May 2022. This project coordinates support for small business suppliers adopting additive manufacturing (AM). AM Forward Florida will drive innovation, economic development, community investment, and job growth across Florida. AM Forward's sponsor companies include Boeing, GE Aviation, Honeywell Aerospace, Lockheed Martin, Northrop Grumman, Raytheon Technologies, and Siemens Energy. Each will play an important role in this project, informing requirements and future demand for production technology and materials. The project team will thus explore how best to expedite the qualification of suppliers for industrial production (including small manufacturer partners Sintavia, Keystone, and ACMT Aero). Also driving AM adoption will be a new generation of engineers and technicians - a workforce reflecting diverse experiences. Partners in this project include the nation's leading minority-serving institutions, including Florida International University, Florida A&M University and Florida State University College of Engineering, University of Central Florida, and the University of Florida, the state's flagship research university. Over 18 months of performance, the proposing team will hold six workshops - three participant workshops and three stakeholder workshops - across the state to discern the best orientation of Florida's manufacturing innovation ecosystem. The project will address current limitations in the state’s supplier base, with the fifth lowest concentration of manufacturing jobs in the U.S. To reverse this, the project will explore regionally-based AM, pursuing breakthroughs in material formulations, integrated advanced robotics, and composite manufacturing. A range of machine types, materials, and testing processes will be explored to ensure effective AM deployment into Florida supply chains near companies/end-users. Industrial additive manufacturing depends on data management, topology, and analytics for design optimization, build preparation and simulation, in situ monitoring, qualification, and testing. Data-driven qualification approaches based on probabilistic modeling and machine learning will underpin university research. These thrusts will focus on end-to-end solutions, efficient design, automated quality methodologies, ensure repeatability, and support scalability for large throughput. This NSF Engine Development Award will enable the team to develop a full NSF Engine proposal, making it much more informed, realistic, and attainable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.