, , , Duckbong Kim
Additive manufacturing (AM) has been envisioned by many as the next industrial revolution. Potential benefits of AM include the production of low-volume, customized, complicated parts/products, supply chain efficiencies, shortened time-to-market, and environmental sustainability. Work remains, however, for AM to reach the status of a full production-ready technology. While the ability to create unique 3D geometries has been generally proven, some production challenges remain, including lacks of 1) data manageability through information management systems, 2) traceability to promote product producibility, process repeatability, and part-to-part reproducibility, and 3) accountability through mature certification and qualification methodologies. To address these challenges, this paper discusses the building of data models to support AM information analysis, identification, and data structure. We identify key attributes for producibility, process repeatability, and part-to-part reproducibility in an AM process. We propose conceptual data models to establish digital provenance in AM parts, a foundation on which future qualification and certification methodologies can be built.
Journal of ASTM International
metals additive manufacturing, digital thread, repeatability, reproducibility