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Physics-enhanced AI/ML tool for additive manufacturing defect detection and prediction

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-23-C-0057
Agency Tracking Number: N222-117-0316
Amount: $146,474.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N222-117
Solicitation Number: 22.2
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2022-11-07
Award End Date (Contract End Date): 2023-05-09
Small Business Information
1820 Ridge Avenue
Evanston, IL 60201-1111
United States
DUNS: 088176961
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Amit Behera
 (847) 425-8202
 abehera@questek.com
Business Contact
 Padma Kotaru
Phone: (847) 425-8216
Email: pkotaru@questek.com
Research Institution
N/A
Abstract

Through this proposed SBIR effort, QuesTek Innovations LLC will develop a physics-enhanced machine learning (ML) software to reduce exhaustive typical on-destructive testing or enable feed forward control methodologies for fabrication of high-quality additive manufacturing (AM) component. Process induced defects like keyholing and lack of fusion porosity are shown to be strongly correlated with AM process parameters and powder characteristics. On the other hand, stochastic flaws can often form even with optimized process conditions. The inability to fabricate defect-free, fully functional AM component has resulted in a requirement for tedious inspection and certification steps before final application. To address this problem, QuesTek will develop deep learning (DL) based models to process real-time multi-source sensor data for stochastic defect detection, classification, and localization. In addition, QuesTek proposes to fuse physics-based modeling (i.e., processibility map modeling) for predicting non-stochastic defects into the ML models for a comprehensive defect detection framework. In the Phase I program, proof-of-concept will be demonstrated by developing model framework for defect detection and model framework validation via powder bed fusion (PBF) AM fabrication and post build defect characterization

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

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