Semantic Degrees for Industrie 4.0

Under the context of Industrie 4.0 (I4.0), future production systems provide balanced operations between manufacturing flexibility and efficiency, realized in an autonomous, horizontal, and decentralized item-level production control framework. Structured interoperability via precise formulations on an appropriate degree is crucial to achieve engineering efficiency in the system life cycle. However, selecting the degree of formalization can be challenging, as it crucially depends on the desired common understanding (semantic degree) between multiple parties. In this paper, we categorize different semantic degrees and map a set of technologies in industrial automation to their associated degrees. Furthermore, we created guidelines to assist engineers selecting appropriate semantic degrees in their design. We applied these guidelines on publically available scenarios to examine the validity of the approach, and identified semantic elements over internally developed use cases targeting semantically-enabled plug-and-produce.

[1]  Ray Piasecki,et al.  The Smart Grid as a Semantically Enabled Internet of Things , 2011 .

[2]  Edward A. Lee,et al.  Scalable Semantic Annotation Using Lattice-Based Ontologies , 2009, MoDELS.

[3]  Miltiadis D. Lytras,et al.  Semantic Web applications: a framework for industry and business exploitation - What is needed for the adoption of the Semantic Web from the market and industry , 2008, Int. J. Knowl. Learn..

[4]  Paola Velardi,et al.  From Glossaries to Ontologies: Extracting Semantic Structure from Textual Definitions , 2008, Ontology Learning and Population.

[5]  Edward A. Lee Computing needs time , 2009, CACM.

[6]  Peter A. Fritzson,et al.  Principles of object-oriented modeling and simulation with Modelica 2.1 , 2004 .

[7]  D. Nardi,et al.  An Introduction to Description Logic , 2017 .

[8]  York Sure-Vetter,et al.  Ontology-Based Information Integration in the Automotive Industry , 2003, SEMWEB.

[9]  Nikolaj Bjørner,et al.  Z3: An Efficient SMT Solver , 2008, TACAS.

[10]  Marko Bacic,et al.  Model predictive control , 2003 .

[11]  Anna Philippou,et al.  Tools and Algorithms for the Construction and Analysis of Systems , 2018, Lecture Notes in Computer Science.

[12]  Edward A. Lee,et al.  Taming heterogeneity - the Ptolemy approach , 2003, Proc. IEEE.

[13]  Edward A. Lee,et al.  Industrial Cyber-Physical Systems - iCyPhy , 2013, CSDM.

[14]  Wolfgang Wahlster The Semantic Product Memory: An Interactive Black Box for Smart Objects , 2013, SemProM.

[15]  Cesare Tinelli,et al.  Handbook of Satisfiability , 2021, Handbook of Satisfiability.

[16]  Daniele Nardi,et al.  An Introduction to Description Logics , 2003, Description Logic Handbook.