Role in Certification There are many ways that in situ monitoring data can be used to help support the AM certification process. Examples given below use terminology from NASA standard NASA-STD-6030: Additive Manufacturing Requirements for Spaceflight Systems (2021), but these same principles could be used in any certification approach. Qualified Material Process: The qualified material process is the locked process used to additively manufacture parts for certification. There are many steps involved in qualifying the process. The aim is to optimize process parame- ters to produce high-quality material in a repeat- able fashion. In situ monitoring can be used during parameter development builds to assess the subtle effects of different parameter sets and help achieve optimization. Part Classification: Parts can be classified based on the consequence of failure, the structural demand of their intended application, and the AM risk associated with the complexity of the AM build and difficulty of inspection (see figure). The higher classifications require more stringent material quality verification. If an in situ monitoring process can be qualified for use as a quantitative indicator of part quality as described previously, this can help improve the inspect- ability and potentially decrease the AM risk. Integrated Structural Integrity Rationale: The integrated structural integrity rationale includes a detailed description of all the defect screening actions that will be performed to assess the struc- tural integrity of a part, which typically includes both NDE and proof testing. In situ process moni- toring can be included in this rationale as a defect screening action, if qualified as a quantitative indicator of part quality as described previously, or even as a qualitative indicator of process control to reduce risk. Statistical Process Control: Statistical process control typically involves testing witness specimens that are built with each AM build and plotting the data to track the system performance over time. In situ monitoring data could serve as an additional measure of long-term statistical process control. This would require determining nominal metrics in the data that can be tracked for each build and that would be a good indicator of overall process variation over time. In summary, NASA encourages the use of in situ monitoring for any AM process, whether it is used for real-time process monitoring or qualified as a quantitative indicator of part quality. The release of NASA-STD-6030 lays the groundwork for expanding the role of in situ monitoring in AM certification. AUTHOR Erin Lanigan: Materials Engineer, Damage Tolerance Branch, Non-Destructive Evaluation Team, NASA Marshall Space Flight Center, Huntsville, AL erin.l.lanigan@nasa.gov CITATION Materials Evaluation  (): - https://doi.org/./.me- © American Society for Nondestructive Testing REFERENCES Grasso, M., and B.M. Colosimo, , “Process Defects and In Situ Monitoring Methods in Metal Powder Bed Fusion: A Review,” Measurement Science and Technology, Vol. , https:// doi.org/./-/aacf McCann, R., M.A. Obeidi, C. Hughes, E. McCarthy, D.S. Egan, R.V. Vijayaraghavan, A.M. Joshi, V.A. Garzon, D.P. Dowling, P.J. McNally, and D. Brabarzon, , “In-situ Sensing, Process Monitoring, and Machine Control in Laser Powder Bed Fusion: A Review,” Additive Manufacturing, Vol. , https://doi.org/./j.addma.. NASA, , NASA-STD-: Additive Manufacturing Require- ments for Spaceflight Systems, National Aeronautics and Space Administration, https://standards.nasa.gov/standard/nasa/ nasa-std- Petrich, J., Z. Snow, D. Corbin, and E.W. Reutzel, , “Multi-modal Sensor Fusion with Machine Learning for Data-Driven Process Monitoring for Additive Manufac- turing,” Additive Manufacturing, Vol. , Part B, https://doi. org/./j.addma.. Snow, Z., J. Keist, G. Jones, R. Reed, E. Reutzel, and V. Sundar, , “Flaw Identification in Additively Manufacturing Parts Using X-ray Computed Tomography and Destructive Serial Sectioning,” Journal of Materials Engineering and Performance, Vol. , pp. –, https://doi.org/./s-- -w Waller, J. M., B.H. Parker, K.L. Hodges, E.R. Burke, and J.L. Walker, , “Nondestructive Evaluation of Additive Manu- facturing State-of-the-Discipline Report,” NASA/TM-- , National Aeronautics and Space Administration, https://doi.org/./RG.... A Structural demand Primary classification Secondary classification AM risk AM risk AM risk AM risk Structural demand B C Class B4 Class B3 Class B2 Class B1 Class A4 Class A3 Class A2 Class A1 High Low High Low High Low High Low High Low High Low High Low Yes No Negligible risk? Consequence of failure AM classification (from NASA-STD-6030). A P R I L 2 0 2 2 • M A T E R I A L S E V A L U A T I O N 27

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