A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments

TLDR

Manufacturing data scientists confront a fragmented landscape of communication standards, protocols, and technologies, making it difficult to locate, access, and extract data for use‑case‑specific applications. The study proposes a DIN Spec 91345 (RAMI 4.0)‑compliant semantic model for data pipelining that facilitates easy exploration, discovery, access, and extraction of manufacturing data. The authors develop a semantic model that maps smart‑manufacturing assets and their data across the life‑cycle, integrating existing RAMI 4.0 reference architectures and supporting multiple communication standards, protocols, and technologies.

Abstract

Today, data scientists in the manufacturing domain are confronted with various communication standards, protocols and technologies to save and transfer various kinds of data. These circumstances makes it hard to understand, find, access and extract data needed for use case depended applications. One solution could be a data pipelining approach enforced by a semantic model which describes smart manufacturing assets itself and the access to their data along their life-cycle. Many research contributions in smart manufacturing already came out with with reference architectures like the RAMI 4.0 or standards for meta data description or asset classification. Our research builds upon these outcomes and introduces a semantic model based DIN Spec 91345 (RAMI 4.0) compliant data pipelining approach with the smart manufacturing domain as exemplary use case. This paper has a focus on the developed semantic model used to enable an easy data exploration, finding, access and extraction of data, compatible with various used communication standards, protocols and technologies used to save and transfer data.