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An Integrated Materials Informatics/Sequential Learning Framework to Predict the Effects of Defects in Metals Additive Manufacturing

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0706
Agency Tracking Number: N18A-013-0251
Amount: $124,975.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N18A-T013
Solicitation Number: 2018.0
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-08-14
Award End Date (Contract End Date): 2019-02-10
Small Business Information
702 Marshall St. #520, Redwood City, CA, 94063
DUNS: 079208424
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Aaron Stebner
 (303) 273-3091
 astebner@mines.edu
Business Contact
 Kyle Michel
Phone: (858) 336-1485
Email: kyle@citrine.io
Research Institution
 Colorado School of Mines
 Johanna Eagan
 1500 Illinois St.
BB306
Golden, CO, 80401
 (303) 384-2589
 Nonprofit college or university
Abstract
In this project, Citrine Informatics and the ADAPT Center at the Colorado School of Mines propose to build an informatics-driven system to understand the effects of defects in additive manufactured parts. The entire history of each sample will be captured on this system; from specific printing parameters and details of precursor materials through to part characterizations and performance measurements. Researchers will be able to add new data, edit and append to records as new information is collected, search for data relevant to their projects, and visualize measurements across samples. The information that is collected will be used as input to machine learning models used to predict the defect properties and performance characteristics of parts before they are printed. The system that is developed in this project will be used to generate sets of printing parameters that will achieve targeted performance metrics through an understanding of the defects that are produced.

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

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