Additive manufacturing (AM) technologies continue to mature, evolving into stalwarts of high-end production lines, particularly with metals AM. Technology maturation has been facilitated by efforts in materials characterization, process sensing, and part qualification, among others. Advancements have been accompanied by a proliferation of AM data that is creating many new learning opportunities that have yet to be realized, hindered by a lack of curation and sharing. Data is often being generated in silos; associated with a specific time, process, material, location, etc. This manuscript investigates the state of data curation and analytics in AM. It begins by investigating AM data types and how this data is currently generated, curated, and shared. It then looks toward the future, where improvements in data curation will support emerging analytics. Finally, short-term needs and long-term opportunities are discussed, outlining future directions in data analytics for AM.
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