NSF AI Disclosure Required
NSF requires disclosure of AI tool usage in proposal preparation. Ensure you disclose the use of FindGrants' AI drafting in your application.
Collaborative Research: Manufacturing of Low-cost Titanium Alloys by Tuning Highly-indexed Deformation Twinning
NSF
About This Grant
This grant supports fundamental research in titanium alloys manufacturing and promotes the progress of science and engineering. Titanium alloys are promising structural materials due to their lightweight, high strength and toughness, high temperature and corrosion resistance, and biocompatibility and have many critical applications in transportation, such as airplane engine components, and healthcare, such as human implants. However, the manufacturing of titanium alloys requires the addition of expensive alloying elements and high processing temperatures, which leads to their high costs and significantly restricted commercial use. This project investigates the scientific mechanisms involved in deformation twinning and develops a prototype system for low-cost manufacturing of advanced lightweight titanium alloys. A combination of experimental, computation, and machine learning efforts is performed to search for new compositions of titanium alloys with low-cost alloying elements and activate novel deformation mechanisms in order to achieve their room-temperature manufacturing. The new knowledge generated by this project advances the titanium industry and promotes technologies to reduce carbon dioxide emissions and improve human health, thus promoting national prosperity and welfare. This research provides a platform to train the next generation of titanium experts and skilled workforce, especially those from underrepresented groups, in the manufacturing of advanced materials as well as high-performance computing. This project is jointly funded by Advanced Manufacturing (AM) program and the Established Program to Stimulate Competitive Research (EPSCoR). This project aims to advance cost-effective room-temperature manufacturing of titanium alloys by a novel alloy design and processing strategy. In this strategy, a large portion (greater than 50 volume percent) of the body-centered cubic beta phase is stabilized at room temperature using low-cost elements after casting and homogenization processes. Furthermore, room-temperature ductility and workability of these alloys in the subsequent cold deformation process are improved by activating sufficient highly-indexed deformation twinning modes in the beta phase utilizing coupled twinning-induced plasticity (TWIP) and transformation-induced plasticity (TRIP) mechanisms. Two specific approaches, involving integration of experiment, simulation and machine learning, are followed. The first approach is to identify and tune the coupling mechanisms between phase transformations and highly-indexed twinning in representative titanium alloys through advanced characterization, crystallography models and atomistic simulations. The second approach is to manipulate and investigate alloying effects on twinning and room-temperature workability of these alloys by iterative feedback between the machine learning models, informed by first-principles calculations, and high-throughput fabrication and mechanical testing experiments. These results guide the discovery of beta phase stabilized titanium alloys containing low-cost alloying elements and attain high room-temperature workability. Finally, large-scale samples of titanium alloys with optimized compositions are cold deformation processed by rolling and drawing into specific shapes and tested for mechanical behavior to verify their room-temperature workability. 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.
Focus Areas
Eligibility
How to Apply
Up to $125K
2026-08-31
One-time $749 fee · Includes AI drafting + templates + PDF export
AI Requirement Analysis
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.