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Magnesium Alloys for Additive Manufacturing by Artificial Intelligence (MAGAMAI)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-21-C-0089
Agency Tracking Number: N20B-T026-0043
Amount: $140,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N20B-T026
Solicitation Number: 20.B
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2020-10-07
Award End Date (Contract End Date): 2021-04-14
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855-2814
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ercan Balikci
 (301) 294-4251
Business Contact
 Mark James
Phone: (301) 294-5221
Research Institution
 The Pennsylvania State University
 Todd Palmer
Research Building West
University Park, PA 16802-1400
United States

 (814) 863-8865
 Nonprofit College or University

NAVY seeks high strength, low density, and high corrosion resistant alloys for structural components which can be processed by additive manufacturing (AM). Magnesium (Mg) alloys are candidates for fuel-efficiency applications, especially the aircrafts. They satisfy density, strength, and stiffness for many designs. However, their low corrosion resistance cannot ensure design lifetimes.  This limits them to non-/semi-structural applications in Navy aircrafts. To overcome this limitation, Intelligent Automation, Inc. proposes development of AM processable magnesium alloy(s) by data driven Artificial Neural Networks (ANN), their powder production, and property characterizations. Alloy design is a multi-criteria decision-making procedure that matches materials traits with design requirements. At this juncture, ICME emerges as an overarching approach aiming to link processing, microstructure, properties, and performance (PMPP) of materials to design expectations. Present computational alloy design approaches, such as PHAse COMPutations (PHACOMP) and CALculations of PHAse Diagrams (CALPHAD), require a sequential treatment of several microstructural constituents, thermodynamic properties, mechanical properties, and employment of a multitude number of phase diagrams. Consequently, high fidelity computational thermodynamics/mechanics simulations for multicomponent alloys are still in their infancy. In such a context, ANN appears as a fast and reliable alloy design approach, which can uncover complex correlations between material traits and design criteria.

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

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