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Data Analytics and Machine Learning Toolkit to Accelerate Materials Design and Processing Development

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-19-C-0395
Agency Tracking Number: N19A-020-0161
Amount: $139,846.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N19A-T020
Solicitation Number: 19.A
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-06-03
Award End Date (Contract End Date): 2019-12-09
Small Business Information
701 McMillian Way NW Suite D
Huntsville, AL 35806
United States
DUNS: 185169620
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Debasis Sengupta
 Technical Fellow
 (256) 726-4800
Business Contact
 Tanu Singhal
Phone: (256) 726-4800
Research Institution
 University at Buffalo
 Ruth Poczciwinski Ruth Poczciwinski
UB Commons Suite 211
Buffalo, NY 14228
United States

 (716) 645-4393
 Nonprofit College or University

Navy has identified refractory high entropy alloy (RHEA) and metal additive manufacturing as two potential areas of interest. This includes designing new RHEA and optimizing metal additive manufacturing with specific material property requirements. Developing materials and processes via applying traditional experimentation and process optimization techniques is painfully slow due to the large number of variables in these systems. Therefore, application of machine learning (ML) techniques is envisioned. The objective of the STTR is to develop a) algorithms for transformation raw material and process data to extract useful information and b) software and modeling tools for guiding the development by automatically detecting data patterns and predicting material properties. Phase I aims demonstrating the proof-of-concept algorithm and tools to meet this objective. The proposed work includes collecting data from multiple sources, merging and extracting features, classifying the transformed data with unsupervised learning, developing predictive correlation with neural network and preliminary algorithms for material discovery and process optimization. The proof-of-concept software will be demonstrated for both RHEAs and metal AM process. In Phase II, the individual codes will be integrated into a GUI based easy to use software which can be applied by non-experts with minimal training

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

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