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Multiple High-Fidelity Modeling Tools for Metal Additive Manufacturing Process Development

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX15CM17C
Agency Tracking Number: 140023
Amount: $749,991.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: T12.04
Solicitation Number: N/A
Solicitation Year: 2014
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-05-12
Award End Date (Contract End Date): 2017-05-11
Small Business Information
701 McMillian Way NW, Suite D
Huntsville, AL 35806-2923
United States
DUNS: 185169620
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 J. Cole
 Technical Fellow
 (256) 726-4800
Business Contact
 Silvia Harvey
Title: Business Official
Phone: (256) 726-4858
Research Institution
 University of Alabama
 Kevin Chou
152 Rose Administration Building
Tuscaloosa, AL 35487-0104
United States

 (205) 348-0044
 Domestic Nonprofit Research Organization

Despite the rapid commercialization of additive manufacturing technology such as selective laser melting, SLM, there are gaps in process modeling and material property prediction that contribute to slow and costly process development, process qualification and product certification. To address these gaps, CFDRC and our partner Dr. Kevin Chou, University of Alabama, will develop multiple computationally efficient, high-fidelity simulation tools for the SLM process. During Phase I the team demonstrated efficient thermomechanical simulations for centimeter size test coupon builds, the feasibility of applying multiphase flow models to analyze particle scale effects on material variations, application of phase field models to predict microstructure evolution, and experimental characterization for model verification and refinement. During Phase II, the modeling tools will be extended to improve computational efficiency and scalability to aerospace component dimensions by further leveraging parallel computing and other acceleration techniques. The fidelity of the models will be enhanced to better predict distortion, residual stress, microstructure and defects from process conditions; and additional process data will be used to validate the resulting codes. The high-fidelity, physics based nature of the codes will allow straightforward application to new materials, and to guiding development of and verifying analytical physics models for process control.

* Information listed above is at the time of submission. *

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