A graph-based knowledge representation and pattern mining supporting the Digital Twin creation of existing manufacturing systems

TL;DR

This paper introduces a graph-based method to integrate heterogeneous data sources and automatically identify templates, simplifying the creation and enhancement of Digital Twins for existing manufacturing systems.

Contribution

It proposes a novel graph-based approach for merging diverse information sources and automating template identification in Digital Twin development for brownfield manufacturing systems.

Findings

Effective merging of heterogeneous data sources.

Automated template identification using graph analysis.

Facilitates Digital Twin creation and enhancement.

Abstract

The creation of a Digital Twin for existing manufacturing systems, so-called brownfield systems, is a challenging task due to the needed expert knowledge about the structure of brownfield systems and the effort to realize the digital models. Several approaches and methods have already been proposed that at least partially digitalize the information about a brownfield manufacturing system. A Digital Twin requires linked information from multiple sources. This paper presents a graph-based approach to merge information from heterogeneous sources. Furthermore, the approach provides a way to automatically identify templates using graph structure analysis to facilitate further work with the resulting Digital Twin and its further enhancement.