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Design of Easy-To-Use Structural Alloy Feedstocks for Additive Manufacturing

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-19-P-0020
Agency Tracking Number: A18B-003-0246
Amount: $149,995.20
Phase: Phase I
Program: STTR
Solicitation Topic Code: A18B-T003
Solicitation Number: 18.B
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2018-12-18
Award End Date (Contract End Date): 2019-06-17
Small Business Information
405 Young Ct. Unit 100C
Erie, CO 80516
United States
DUNS: 079973429
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jacob Nuechterlein
 (720) 545-9016
Business Contact
 Jacob Nuechterlein
Phone: (720) 545-9016
Research Institution
 Colorado School of Mines
 Karen Haines Karen Haines
1500 Illinois St.
Golden, CO 80401
United States

 (303) 273-3910
 Nonprofit College or University

There is a need for easy-to-use, versatile alloys for additive manufacturing that print consistently regardless of the specific process or machine, or the metallurgical and manufacturing knowledge of the user. Such capability is especially critical for soldiers to be able to make every day items on demand in the theater of war. Toward meeting this need, recent successes developing high performance aluminum and nickel-titanium based alloys for additive manufacture by aerospace and biomedical industries will be drawn upon to now instead develop low-cost, readily available (defined by a robust U.S. supply chain) and transportable feedstock for iron-based alloys that can be successfully manufactured by nearly anyone. These materials will print reliably across many wire-fed and powder-fed processes, and will require at most a 1-step heat treatment plus a simple chemical treatment after manufacture to be ready to use; no machining required. A modern Integrated Computational Materials Engineering (ICME) approach consisting of calculations using methods such as density functional theory (DFT) and calculated phase diagram (CALPHAD) will guide physical experiments. Data informatics will reduce the time and cost for alloy development cycles, taking advantage of sequential learning methods where step-by-step decisions are informed by statistical outliers and by uncertainty across the

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

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