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Enhanced Process Control of Electron Beam Additive Manufacturing

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC21C0243
Agency Tracking Number: 212969
Amount: $120,665.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: Z4
Solicitation Number: SBIR_21_P1
Solicitation Year: 2021
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-05-03
Award End Date (Contract End Date): 2021-11-19
Small Business Information
4915 W. 67th Street
Chicago, DE 60638-6408
United States
DUNS: 830974150
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Scott Stecker
 (708) 594-3800
Business Contact
 John O'Hara
Phone: (312) 459-6849
Research Institution

Sciaky has been witness to a number of process deviations that have resulted in component defects. One such ldquo;defectrdquo; is related to metal vapor condensate contamination. One of the benefits of the EBAM process is its efficient use of power as well as the high purity vacuum environment. These process characteristics allow EBAM to provide fast deposition speeds that in many cases lead the industry. One side effect of this high deposition rate with the vacuum is that the raw material is often vaporized at a high rate as well and this metal vapor condenses back into solid form on surfaces local to the process. Over time the condensate build up can become substantial and occurrences have been observed where the condensate gets dislodged and can fall into the melt pool. Sciaky has learned that in the case of EBAM deposition targeting Ti-6Al-4V, the condensate is made up of predominantly aluminum.nbsp; This issue is a well-documented concern when processing alloys with elements having various vapor pressures in a high vacuum environment.nbsp; If the condensate becomes entrapped within the molten pool, the local area no longer achieves the target chemistry or metallurgical properties.nbsp; There has been at least one instance where the condensate provided a crack initiation site that resulted catastrophic failure of a preform in response to residual stress buildup.Our phase 1 approach will target the real-time identification of metal condensate events, document the location of detected events and provide the basis for corrective action avoiding the scrapping of a part. This proof of concept shall provide the basis for a series of other identified defects to be addressed via a machine learning approach that may include material contaminants, process interruptions, gross lack of fusion type defects, large porosity and potentially others as the approaches get refined.

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

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