Smart Agents for the Industry 4.0 : Enabling Machine Learning in Industrial Production / by Max Hoffmann

Von:

Hoffmann, Max [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Springer eBooks Computer ScienceVerlag: Wiesbaden : Springer Vieweg, 2019Auflage: 1st ed. 2019Beschreibung: 1 Online-Ressource (XXXIV, 318 p. 111 illus)ISBN:

9783658277420

Schlagwörter:

Andere physische Formen: 9783658277413 | Erscheint auch als: 978-3-658-27741-3 Druck-AusgabeDDC-Klassifikation:

006.3 23

LOC-Klassifikation:

Q334-342

DOI: DOI: 10.1007/978-3-658-27742-0Online-Ressourcen:

Zusammenfassung: 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 AutomationZusammenfassung: 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 processingPPN: PPN: 1678681725Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-SEB | ZDB-2-SXCS

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