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Licensed Unlicensed Requires Authentication Published by De Gruyter (O) October 9, 2024

Capability knowledge base query to allocate process resources for master recipe formulation

Abfrage einer Fähigkeitswissensbasis zur Zuweisung von Prozessressourcen für die Erstellung von Grundrezepten
  • Michael Winter

    Michael Winter has been a researcher at the Chair of Information and Automation Systems for Process and Material Technology (IAT) at RWTH Aachen University since 2021. His research focuses on automated identification of plant configurations through process descriptions with knowledge based systems.

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    , Tobias Klausmann

    Tobias Klausmann has been a researcher at Lenze SE since 2019. His research focus is on modelling resource capabilities by abstracting machine interfaces for the realization of volatile automation tasks. The research is executed in conjunction with the IAT at RWTH Aachen.

    and Tobias Kleinert

    Tobias Kleinert heads the Chair of IAT at RWTH Aachen, with focus on data management and flexible industrial automation. He graduated in Mechanical Engineering, completed PhD in 2005 at Ruhr-Universität Bochum and worked at BASF in Advanced Control, Functional Safety, DCS, MES and Smart Manufacturing.

Abstract

We present a method for matching the required capabilities of general recipes with available resources to formulate master recipes. Process elements extracted from BatchML XML files are used to decompose the required capabilities and match them with the capabilities offered by components at distinct levels. A capability OWL ontology with properties aids in querying and matching capabilities with resources, enabling validation via description logic (DL) queries post-inference and integration into the master recipe. DL queries and matching within a knowledge base demonstrate AI-based knowledge processing for production systems.

Zusammenfassung

In diesem Beitrag werden geforderte Fähigkeiten aus Verfahrensrezepten mit verfügbaren Ressourcen für die Formulierung von Grundrezepten abgeglichen. Extrahierte Prozesselemente aus BatchML XML Dateien werden verwendet, um die geforderten Fähigkeiten zu zerlegen und sie mit den von Komponenten auf verschiedenen Ebenen angebotenen Fähigkeiten abzugleichen. Eine OWL Ontologie für Fähigkeiten mit Eigenschaften erlaubt die Abfrage und den Abgleich von Fähigkeiten mit Ressourcen und ermöglicht die Validierung über Description Logic (DL) Abfragen nach der Inferenz sowie die Integration in Grundrezepte. DL Abfragen und Matching innerhalb einer Wissensbasis demonstrieren KI-basierte Wissensverarbeitung für Produktionssysteme.


Corresponding author: Michael Winter, RWTH Aachen University, Aachen, Germany, E-mail: 

About the authors

Michael Winter

Michael Winter has been a researcher at the Chair of Information and Automation Systems for Process and Material Technology (IAT) at RWTH Aachen University since 2021. His research focuses on automated identification of plant configurations through process descriptions with knowledge based systems.

Tobias Klausmann

Tobias Klausmann has been a researcher at Lenze SE since 2019. His research focus is on modelling resource capabilities by abstracting machine interfaces for the realization of volatile automation tasks. The research is executed in conjunction with the IAT at RWTH Aachen.

Tobias Kleinert

Tobias Kleinert heads the Chair of IAT at RWTH Aachen, with focus on data management and flexible industrial automation. He graduated in Mechanical Engineering, completed PhD in 2005 at Ruhr-Universität Bochum and worked at BASF in Advanced Control, Functional Safety, DCS, MES and Smart Manufacturing.

Acknowledgments

This work is partially supported by German Federal Ministry of Education and Research in the scope of the BaSys 4 Transfer project (01IS22089).

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-11-30
Accepted: 2024-08-19
Published Online: 2024-10-09
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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