| Abstract Scope |
Additive manufacturing builds near-net-shape parts using high volume data for designing, operating, and certifying processes and products. To cost-effectively mature AM technology, a FAIR (Findable, Accessible, Interoperable, and Reusable) infrastructure is developed that enables integrations among machines, analytics, and computer tools. This infrastructure includes data models, which is developed following ASTM standards defining key terminologies in a structure for archiving AM related data. A selected dataset, including AM building files, in-situ sensing data, and measuring results, are used to validate the data models. This infrastructure also provides API tools to manage raw data and metadata for statistical analyses to identify the variability of the processes and assess the data quality. The results from this analytical process assist the following sensitivity analyses for the design of experiments and predictive model developments. This presentation will share a case study of identifying the metrology and analytics gaps and opportunities using this informatics-based framework. |