Evolution of Data Management and Common Data Models for Additive Manufacturing

Abstract

Abstract Additive manufacturing, as the first fully digital manufacturing process, is critically dependent on data, including the input materials, the process parameters guiding the three-dimensional printing process execution, any postprocessing steps, and any inspections performed on the printed coupons and/or parts, to name just a few examples. This article presents the standards to enable findable, accessible, interoperable, and reusable (FAIR) data. It then discusses three main types of data models that are used to capture different levels of detail and granularity of data: conceptual, logical, and physical. Different approaches and techniques with their own strengths and weaknesses are developed to model data. Four of the major types of data models include hierarchical, relational, object-oriented, and network/graph-based. The article also presents the evolution of data management approaches. It then describes the characteristics of effective logical data models.