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Additive Manufacturing Data and Metadata Acquisition—General Practice
Published
Author(s)
Yan Lu, Ho Yeung, Jason Fox, Felix Kim, Luke Mohr
Abstract
Increasingly, a vast variety of additive manufacturing (AM) datasets are generated through AM development lifecycles. The amount, type, and speed of the collected data are unprecedented. The datasets are created and collected for material development, process improvement, machine calibration, and product quality control and qualification. In the details of these activities, a large range of overlapping data needs exist that may benefit from sharing data. Design data, machine data, as well as measurements from material characterization, process monitoring, part inspection and testing are commonly used across many AM activities. Engineering decisions rely on a broad spectrum of data beyond individual stakeholder's data generation capability. To enhance the usability and reusability of the datasets, metadata, "description of data", must be collected and accessible by the users. Among all the metadata elements, the information about data acquisition is critical for data interpretation and downstream analysis. In this chapter, common AM data acquisition methods are introduced, covering pre-process material characterization in the lab, machine calibration data in the field, automated process data acquisition during a build, and post-process part inspections and tests. The focus is to identify necessary metadata for the datasets to improve data usability and reusability. This chapter also includes exemplar metadata definitions for the dataset acquired from light scattering based particle size analysis.
Citation
ASM Handbook 24A: Additive Manufacturing Design and Applications
Lu, Y.
, Yeung, H.
, Fox, J.
, Kim, F.
and Mohr, L.
(2023),
Additive Manufacturing Data and Metadata Acquisition—General Practice, ASM Handbook 24A: Additive Manufacturing Design and Applications, ASM International, Materials Park, OH, [online], https://doi.org/10.31399/asm.hb.v24A.a0006981, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934787
(Accessed December 3, 2024)