SBIR/STTR Research Award

Modeling for the Robust Design of Materials for Superplastic Forming Processes for Titanium Structural Components

Award identifier 97ad7a1ab6809945012d84173f5041ac15e9cc4a5c41d4ff2cccd69c4086ad1eAwarding agency Department of Defense · Navy

Award amount

$79,993

Program and phase

STTR · Phase I

Award year

2012

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Award details

Awarding agency
Department of Defense
Branch
Navy
Recipient company
SCIENTIFIC FORMING TECHNOLOGIES CORPORATION
Proposal award date
2012-06-25
Contract end date
Not reported
UEI
C3KQLXA83X57
Solicitation number
2012.A
Topic code
N12A-T018

Research abstract

Superplastic forming process (SPF) takes advantage of unique ability of certain materials such as titanium alloys that exhibit exceptionally high tensile ductility beyond its normal limits of plastic deformation at elevated temperatures and low strain rates. Within a narrow window of a combination of elevated temperatures and low strain rates, titanium alloys can withstand elongation as high as 300%. One of the critical factors that determine the effectiveness and the optimum design of a SPF part is the quality of incoming sheet material used for the manufacture of the part. The key microstructural features of alpha-beta titanium sheet material that needs to be optimized include alpha grain size, grain aspect ratio, grain size distribution, relative volume fraction of alpha and beta phase and texture of the alpha phase. While there are established models to simulate cogging, rolling and SPF processes, there is still no single integrated material modeling tool that would predict the microstructural evolution during the primary processing of alpha-beta titanium alloys. Understanding how the key process variables and material behavior impact the microstructural evolution of sheet products during the rolling process will be paramount task in this project. Scientific Forming Technologies Corporation (SFTC) is teaming with the University of Texas at Austin, Timet and Boeing for the Phase I of this project. The objective of this project is to develop a modeling framework that enables the prediction of microstructure evolution leading to optimum design of sheet material for the SPF of titanium structural components. The various rolling, cross rolling and pack rolling schedules that a titanium plate material undergoes in its conversion process to sheet material need to be modeled and optimized for ideal microstructural features in the finished titanium sheet product. With the sensitivity analysis framework in DEFORM system, the user will be able to systematically analyze the variabilities and uncertainties associated with the processing conditions, boundary conditions, material properties and incoming starting grain size distribution of the plate material, thus providing a robust design of material for the SPF processes. At the end of phase I, our team would complete characterization of Ti 6-4 and Ti 54M plate, intermediate plate-sheet and final sheet product. Our team will complete modeling of lab scale rolling, cross rolling and pack rolling processes and track material history of selected locations across the various thermo-mechanical processing operations. Our team will investigate appropriate alpha lath spheroidization models as well as recrystallization and grain growth models for Ti 6-4 material that are available in the literature. Our team will work closely with Navy to develop an implementation and a validation plan for subsequent Phase 2 activities. It is envisioned that the implementation and validation of microstructure evolution models will be undertaken in the phase II of this project.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

22 SBIR/STTR records share this UEI (1994–2019). Reported award amounts total $7,381,883. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Defense

Agency intelligence

Department of Defense

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