NASA SBIR 2020-I Solicitation

Proposal Summary

 20-1- Z4.04-5894
 Real Time Defect Detection, Identification and Correction in Wire-Feed Additive Manufacturing Processes
 In-Situ Dimensional and Macro Surface Defect Mitigation in Wire-Fed Additive manufacturing Processes
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Keystone Synergistic Enterprises, LLC
664 Northwest Enterprise Drive, Suite 118
Port Saint Lucie, FL 34986
(772) 343-7544

Principal Investigator (Name, E-mail, Mail Address, City/State/Zip, Phone)

Mr. Michael Santangelo
664 Northwest Enterprise Drive, Suite 118 Port Saint Lucie, FL 34986 - 1565
(678) 386-9878

Business Official (Name, E-mail, Mail Address, City/State/Zip, Phone)

Bryant Walker
664 Northwest Enterprise Drive, Suite 118 Port Saint Lucie, FL 34986 - 1565
(772) 341-2700
Estimated Technology Readiness Level (TRL) :
Begin: 3
End: 4
Technical Abstract (Limit 2000 characters, approximately 200 words)

Additive Manufacturing (AM) technologies have reached promising heights in the recent years while offering several advantages over traditional manufacturing methods, especially in the case of aerospace parts and in-space manufacturing. Whereas the advancements in wire feed metal AM are promising for recently ramped up LEO and deep space efforts of NASA (Artemis, OSAM) and private space agencies, the technology still lacks build reliability which would have ensured a defect free part with correct form-factor the first time without needing post-build quality assurance or in the worst case, part rejection. Thus, it is significant for a wire feed metal AM system to have the ability of in-situ, layer-by-layer build quality assurance by maintaining parts’ dimensional accuracy and defect-free metal deposition. This Phase I effort presents a novel approach to solve the problem by targeting to detect, identify and correct optically detectable weld surface defects such as porosity, hot-cracking and lack of fusion. The first innovation is a camera vision-based layer-by-layer weld-surface defect identification system for wire feed AM process. The camera is mounted on an articulated robot for autonomous scanning of every single layer of deposited metal. A Convolutional Neural Network type Deep Learning-based software is utilized to aid defect identification and sending an output to the robot for a “Go”/”No-Go” decision over continuing deposit of subsequent layers. The second innovation is an automated laser displacement sensor for real-time measurement of part dimensions and auto-correction of robot path to compensate for discrepancies in metal deposition that can potentially result in parts with dimensional defects. The proposed innovations are targeted toward filling the gaps of real time dimensional and weld-surface defect detection, identification and correction for wire-feed metal AM processes.

Potential NASA Applications (Limit 1500 characters, approximately 150 words)

  Additive manufacturing of liquid rocket nozzle liners, regeneratively cooled nozzles and AM close-out builds for combustion chambers used in current and advanced engines (e.g, RS-25 Restart engines for SLS/Artemis program).

-  On-orbit manufacturing and repairing applications expected to benefit NASA to realize their On-orbit Servicing, Assembly and Manufacturing (OSAM) program and future explorations to Lunar and Martian surfaces.
Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words)

- Additive manufacturing of large engine nozzles and combustion chambers for commercial rocket engine manufacturing companies such as Aerojet-Rocketdyne, Orbital ATK, SpaceX and Blue Origin.

- Composite layup tooling manufacturing

- Non-critical applications such as tooling, structural components, and test components. E.g, Titanium parts manufacturing for Northrop Grumman. 

Duration: 6

Form Generated on 06/29/2020 21:06:15