Viability of Data Analytics to Ascertain Component Performance for Additive Manufacturing

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to produce multiple nuclear components to be assembled into a fully functional microreactor core. AM was selected as the main manufacturing technology for TCR because it has the potential to disrupt the nuclear industry on two fronts: (1) it enables the manufacturing of very complex geometries with optimized and tailored material properties for the intended use of the component, opening up new options for reactor designs, and (2) it allows for a better understanding of the manufacturing process through real-time in situ monitoring, data analytics, and artificial intelligence, which can lead to a streamlined qualification and certification process. Within TCR, the development and deployment of a digital platform aims at addressing the latter opportunity. As part of this effort, the collection of pedigree datasets is vital, should these data be generated before, during or after the manufacturing process. This report focuses on the development of data analytics tools enabling qualitative assessment of the manufacturing processes to automate the identification of flaws in in situ monitoring imagery and to correlate those flaws to the resulting mechanical testing data. Through examples, the report gives an overview of the data collected and presents a path forward for the digital platform.