A Data Integration Framework for Additive Manufacturing Big Data Management




Perišić, Milica
Milenković, Dimitrije
Lu, Yan
Jones, Albert
Ivezić, Nenad
Kulvatunyou, Boonserm

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University of Texas at Austin


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.


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