As additive manufacturing (AM) continues to mature as a production technology, the limiting factors that have hindered its adoption in the past still exist, for example, process repeatability and material availability issues. Overcoming many of these production hurdles requires a further understanding of geometry-process-structure-property relationships for additively manufactured parts. In smaller sample sizes, empirical approaches that seek to harness data have proven to be effective in identifying material process-structure-property relationships. This paper presents a collaborative AM data management system developed at the National Institute of Standards and Technology (NIST). This data management system is built with NoSQL (Not Only Structured Query Language) database technology and provides a Representational State Transfer (REST) interface for application integration. In addition, a web interface is provided for data curating, exploring, and downloading. An AM data schema is provided by NIST for an alpha release, as well as a set of data generated from an interlaboratory study of additively manufactured nickel alloy (IN625) parts. For data exploration, the data management system provides a mechanism for customized web graphic user interfaces configurable through a visualization ontology. As a collaboration platform, the data management system is set to evolve through sharing of both the AM schema and AM development data among the stakeholders in the AM community. As data sets continue to accumulate, it becomes possible to establish new correlations between processes, materials, and parts. The functionality of the data management system is demonstrated through the curation and querying of the curated AM datasets.
Additive manufacturing of fatigue resistant austenitic stainless steels by understanding process-structure–property relationships
