Abstract Scope |
Large amounts of data are generated through the entire, AM part development lifecycle. Data include those produced for process monitoring, material-property characterization, and part qualification. Hence, data integration and management are critical in streamlining, accelerating, certifying, and deploying AM components. However, achieving that integration and management have several challenges because AM data embodies the four characteristics of Big Data - volume, velocity, variety, and veracity. This paper proposes an AM framework as a foundation for addressing those challenges. In the framework, AM data are streamed, curated, and configured automatically, which increases the effectiveness associated with archiving and querying. The framework also includes a description of the big data, named AM metadata. Metadata helps to link various types of big data and to improve browsing, discovering, and analyzing that data. Finally, the framework can be used to derive requirements for standards that enable interoperability for data sharing. |