TY - BOOK
T1 - Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects
AU - Scime, Luke
AU - Haley, James
AU - Halsey, William
AU - Singh, Alka
AU - Sprayberry, Michael
AU - Ziabari, Amir
AU - Paquit, Vincent
PY - 2020
Y1 - 2020
N2 - The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear components which will be assembled into a fully functional microreactor core. Compared with traditional manufacturing technologies, AM technologies allow (1) real-time observation of the manufacturing process at a much higher resolution using in-situ monitoring technologies to capture the sensor signatures that scientifically describe each event occurring over time and space and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on in-situ and ex-situ data correlation and associated data analytics results. Examples are provided to illustrate progress with respect to laser powder bed fusion (L-PBF), binder jetting, computed tomography (CT) reconstruction, and mechanical testing. Elements of the Digital Thread and data management infrastructure are discussed in the main document, and an extensive supplemental appendix is provided detailing the Digital Platform, as well as its implementation and subcomponents. In conclusion, the path forward for the next fiscal year is also discussed.
AB - The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear components which will be assembled into a fully functional microreactor core. Compared with traditional manufacturing technologies, AM technologies allow (1) real-time observation of the manufacturing process at a much higher resolution using in-situ monitoring technologies to capture the sensor signatures that scientifically describe each event occurring over time and space and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on in-situ and ex-situ data correlation and associated data analytics results. Examples are provided to illustrate progress with respect to laser powder bed fusion (L-PBF), binder jetting, computed tomography (CT) reconstruction, and mechanical testing. Elements of the Digital Thread and data management infrastructure are discussed in the main document, and an extensive supplemental appendix is provided detailing the Digital Platform, as well as its implementation and subcomponents. In conclusion, the path forward for the next fiscal year is also discussed.
KW - 42 ENGINEERING
KW - 97 MATHEMATICS AND COMPUTING
U2 - 10.2172/1684671
DO - 10.2172/1684671
M3 - Commissioned report
BT - Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects
CY - United States
ER -