HDF5 Hierarchies for Additive Manufacturing digital representations and Enhanced Analytics

Date
2022
Authors
Monnier, L.V.
Ko, H.
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Abstract

Advancement in Additive Manufacturing (AM) technologies and data acquisition techniques have led to an increase in AM data generated. However, due to the large volume and the diversity of AM data available it is becoming challenging to efficiently store, analyze, and represent AM processes. HDF5 has the potential to allow an easy access to big data by offering a hierarchical data catalog. Thus, AM processes could be represented through a hierarchy based on the data analytic needs and directly link the corresponding AM data. This paper investigates the use of data formats to represent big data and AM dataset. Existing AM ontologies and models are reviewed in order to effectively encapsulate AM information and incorporate the hierarchy into an HDF5 AM wrapper. Three hierarchies are proposed to represent specific perspectives of AM processes: the digital twin of AM Product Lifecycle, the AM V model representation, and the material centric characteristics.

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