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

Kevin Nagorny, Sebastian Scholze, Armando Walter Colombo, José Barata Oliveira

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
52 Downloads (Pure)

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.

Original languageEnglish
Article number9296293
Pages (from-to)223114-223129
Number of pages16
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Data pipeline
  • industry 4.0
  • RAMI 4.0
  • semantic model
  • smart manufacturing

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