Smart Agents for the Industry 4.0

Enabling Machine Learning in Industrial Production
 
 
Springer Vieweg (Verlag)
  • 1. Auflage
  • |
  • erschienen am 26. September 2019
 
  • Buch
  • |
  • Hardcover
  • |
  • 352 Seiten
978-3-658-27741-3 (ISBN)
 
Max Hoffmann describes the realization of a framework that enables autonomous decision-making in industrial manufacturing processes by means of multi-agent systems and the OPC UA meta-modeling standard. The integration of communication patterns and SOA with grown manufacturing systems enables an upgrade of legacy environments in terms of Industry 4.0 related technologies. The added value of the derived solutions are validated through an industrial use case and verified by the development of a demonstrator that includes elements of self-optimization through Machine Learning and communication with high-level planning systems such as ERP.
About the Author:


Dr.-Ing. Max Hoffmann is a scientific researcher at the Institute of Information Management in Mechanical Engineering, RWTH Aachen University, Germany, and leads the group "Industrial Big Data". His research emphasizes on production optimization by means of data integration through interoperability and communication standards for industrial manufacturing and integrated analysis by using Machine Learning and stream-based information processing.
1st ed. 2019
  • Englisch
  • Wiesbaden
  • |
  • Deutschland
Springer Fachmedien Wiesbaden GmbH
  • Für Beruf und Forschung
  • 111 s/w Abbildungen
  • |
  • 111 Illustrations, black and white; XXXIV, 318 p. 111 illus.
  • Höhe: 216 mm
  • |
  • Breite: 153 mm
  • |
  • Dicke: 23 mm
  • 568 gr
978-3-658-27741-3 (9783658277413)
10.1007/978-3-658-27742-0
weitere Ausgaben werden ermittelt

Dr.-Ing. Max Hoffmann is a scientific researcher at the Institute of Information Management in Mechanical Engineering, RWTH Aachen University, Germany, and leads the group "Industrial Big Data". His research emphasizes on production optimization by means of data integration through interoperability and communication standards for industrial manufacturing and integrated analysis by using Machine Learning and stream-based information processing.

Agent OPC UA - Semantic Scalability and Interoperability Architecture for MAS through OPC UA.- Management System Integration of OPC UA Based MAS.- Flexible Manufacturing Based on Autonomous, Decentralized Systems.- Use Cases for Industrial Automation.
Max Hoffmann describes the realization of a framework that enables autonomous decision-making in industrial manufacturing processes by means of multi-agent systems and the OPC UA meta-modeling standard. The integration of communication patterns and SOA with grown manufacturing systems enables an upgrade of legacy environments in terms of Industry 4.0 related technologies. The added value of the derived solutions are validated through an industrial use case and verified by the development of a demonstrator that includes elements of self-optimization through Machine Learning and communication with high-level planning systems such as ERP.

Contents
- Agent OPC UA - Semantic Scalability and Interoperability Architecture for MAS through OPC UA
- Management System Integration of OPC UA Based MAS
- Flexible Manufacturing Based on Autonomous, Decentralized Systems
- Use Cases for Industrial Automation

Target Groups
- Scientists and students in automation technology, production technology, mechanical engineering, process control, factory planning
- Practitioners in these fields

About the Author
Dr.-Ing. Max Hoffmann is a scientific researcher at the Institute of Information Management in Mechanical Engineering, RWTH Aachen University, Germany, and leads the group "Industrial Big Data". His research emphasizes on production optimization by means of data integration through interoperability and communication standards for industrial manufacturing and integrated analysis by using Machine Learning and stream-based information processing.

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