A Data Integration Framework for Additive Manufacturing Big Data Management
Milica Perišić, Dimitrije Milenković, Yan Lu, Albert T. Jones, Nenad Ivezic, Boonserm Kulvatunyou
Large amounts of data are generated throughout the entire, AM, part-development lifecycle. Data are generated by various functions within process monitoring, material characterization, equipment status, and part qualification. Hence, data integration and management are critical in streamlining, accelerating, certifying, and deploying these functions. However, achieving that integration and management has 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 for real-time analysis and batch processing, which increases the effectiveness of archiving and querying that data. The framework also includes a description of the associated AM metadata, which links the various data types and improves browsing, discovering, and analyzing that data. Finally, the framework can be used to derive requirements for standards that enable data sharing.
Proceedings of the Solid Freeform Fabrication Conference
, Milenković, D.
, Lu, Y.
, Jones, A.
, Ivezic, N.
and Kulvatunyou, B.
A Data Integration Framework for Additive Manufacturing Big Data Management, Proceedings of the Solid Freeform Fabrication Conference, virtual, MD, US
(Accessed August 9, 2022)