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Toward a Standard Data Architecture for Additive Manufacturing

Published

Author(s)

Shengyen Li, Shaw C. Feng, Alexander Kuan, Yan Lu

Abstract

To advance the additive manufacturing (AM) technologies, R&D projects may evaluate new facilities and different processes that need a scalable data architecture to accommodate the progressing knowledge. This work introduces a data pedigree to enable the traceability and scalability of the material lifecycle from R&D projects. This pedigree includes 5 data models covering the information about (1) project management, (2) feedstock materials, (3) AM building and post processing, (4) microstructure and properties measurements, and (5) computer simulations. This work focuses on the integration of the data architecture with the data models that adopt sub-models from other domain specific efforts for materials engineers. As a proof of concept, these material and process models include the metadata to accommodate the details for laser powder bed fusion AM. The additional data models can be embedded in this architecture without affecting the existing structure. To demonstrate the use cases, this architecture is implemented using XML and preliminarily tested using the data from America Makes.
Citation
JOM Journal of the Minerals Metals and Materials Society

Keywords

additive manufacturing, data management

Citation

Li, S. , Feng, S. , Kuan, A. and Lu, Y. (2024), Toward a Standard Data Architecture for Additive Manufacturing, JOM Journal of the Minerals Metals and Materials Society, [online], https://doi.org/10.1007/s11837-023-06367-4, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956778 (Accessed July 15, 2024)

Issues

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Created January 16, 2024, Updated February 26, 2024