'The University of Texas at Austin, Bureau of Economic Geology'
Abstract
In smart connected factories, manufacturing machines are capable of generating vast amounts of
process data generated internally from within its control systems or from sensors coupled with the
process. This streaming data must be stored and queried to perform data analytics or closed loop
control to improve manufacturing processes. Currently, structured data schemas are ineffective in
handling image and time-series data generated from additive manufacturing machines. In this
paper, we propose an unstructured data schema through NoSQL document oriented database
systems as an effective and scalable approach to capturing and storing real-time streaming data for
process monitoring. In addition, we present an approach to linking in real-time, slice layer
information and tag it with process related sensor data for performing fast, scalable queries either
in real-time or post-fabrication. We have demonstrated our approach with two classes of additive
manufacturing machines – Fused Deposition Modeling and Electron Beam Melting Systems from
Makerbot and ARCAM respectively.Mechanical Engineerin
