CAREER: Manufacturing USA: Deep Learning to Understand Fatigue Performance and Processing Relationship of Complex Parts by Additive Manufacturing for High-consequence Applications 2028 Grant

Unknown funder

About this grant

This is a National Science Foundation CAREER award (No. 2624565) to the University of Florida, managed by the Civil, Mechanical, & Manufacturing Innovation (CMMI) division of the Engineering Directorate. It funds fundamental research on fatigue failure in metal additive manufacturing parts made by laser powder-bed fusion (LPBF), which is a barrier to using LPBF for high-consequence, dynamic load-bearing applications. The problem addressed is the limited knowledge of how LPBF processing creates defects and how those defects, together with geometry and multiaxial loading, drive fatigue scatter and failure. The proposed work is multiscale. At the micro-scale, crack-initiating defects will be identified using X-ray computed tomography or optical profilometry and correlated with observed fatigue failures, and their effects on crack growth will be analysed with fracture mechanics and statistics. At the part scale, finite element modelling will examine stress and strain at the highest-stress regions under decoupled multiaxial loading. These inputs will feed a physics-centric, machine learning framework, specifically a hierarchical graph convolutional network, to predict crack growth and fatigue life of complex LPBF parts. The award also funds education and outreach aimed at broadening participation, including engagement with K-12 students and recruitment of women and minorities into research. The stated goal is to provide a technological foundation for future metal additive manufacturing production and to strengthen U.S. industrial competitiveness.

Eligibility

The page does not state formal applicant eligibility rules. It identifies the award as a Faculty Early Career Development (CAREER) award and names the University of Florida as recipient, with Jia Liu as Principal Investigator. The award is a Standard Grant under NSF program codes for CAREER and Advanced Manufacturing.

How it's scored

The page states that the award was evaluated using NSF's intellectual merit and broader impacts review criteria. It does not describe scoring weights or panel composition.

Key requirements

• ✓Faculty Early Career Development (CAREER) award eligibility; typically requires tenure-track or equivalent position at US research institution
• ✓Research focus on metal additive manufacturing (LPBF) fatigue performance and processing relationships using deep learning
• ✓Application to high-consequence industries (aerospace, automotive, medical devices, energy)
• ✓Integration of research with educational/mentoring components typical of CAREER awards

Funder contact

The notice names a programme contact you can speak to before applying.

Frequently asked questions

How much funding does Unknown funder offer?

CAREER: Manufacturing USA: Deep Learning to Understand Fatigue Performance and Processing Relationship of Complex Parts by Additive Manufacturing for High-consequence Applications offers $403K – $403K.

When is the CAREER: Manufacturing USA: Deep Learning to Understand Fatigue Performance and Processing Relationship of Complex Parts by Additive Manufacturing for High-consequence Applications deadline?

Applications close 30 Apr 2028.

Who can apply for CAREER: Manufacturing USA: Deep Learning to Understand Fatigue Performance and Processing Relationship of Complex Parts by Additive Manufacturing for High-consequence Applications?

Eligibility covers organisations in US; university, research-institution. See the eligibility criteria above for the full conditions.

Relevant sectors

About this sector

Low-carbon materials funding covers cement, concrete, steel, chemicals, timber and novel building materials with lower embodied carbon. It ranges from research grants to demonstration plants and public procurement that specifies lower-carbon products. Calls often ask for lifecycle assessment, product testing against standards and interest from buyers in construction or manufacturing.