Taming Electric Drive Train Complexity at Festo with Knowledge Graphs and Reasoning

Tracking #: 3046-4260

This paper is currently under review
Authors: 
Kathrin Gerber
Thorsten Liebig1
Andreas Maisenbacher
Christoph Petermann
Gunther Sudra
Daniel Wurst
Jens Wissmann

Responsible editor: 
Guest Editors SW for Industrial Engineering 2022

Submission type: 
Application Report
Abstract: 
This article describes the successful outcome of applying Semantic Technologies at Festo, a global supplier of solutions for factory automation. As part of its Industry 4.0 initiative Festo uses OWL ontology models and SWRL reasoning to capture product complexity in order to power various knowledge intensive applications. We introduce the underlying product ontology model and present the data processing workflow originating from RDBMS tables through reasoning to a SPARQLdriven backend that computes a ranked selection of proper product solutions out of millions of electric drive trains from given parameters such as working range, moving mass, travel time, etc. in less than a second.
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Under Review