Cantitate/Preț
Produs

Smart Agents for the Industry 4.0: Enabling Machine Learning in Industrial Production

Autor Max Hoffmann
en Limba Engleză Paperback – 27 sep 2020
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.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 93163 lei  6-8 săpt.
  Springer Fachmedien Wiesbaden – 27 sep 2020 93163 lei  6-8 săpt.
Hardback (1) 93617 lei  6-8 săpt.
  Springer Fachmedien Wiesbaden – 26 sep 2019 93617 lei  6-8 săpt.

Preț: 93163 lei

Preț vechi: 116454 lei
-20% Nou

Puncte Express: 1397

Preț estimativ în valută:
17826 18662$ 14750£

Carte tipărită la comandă

Livrare economică 05-19 aprilie

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783658277444
ISBN-10: 3658277440
Pagini: 318
Ilustrații: XXXIV, 318 p. 111 illus.
Dimensiuni: 148 x 210 mm
Greutate: 0.46 kg
Ediția:1st ed. 2019
Editura: Springer Fachmedien Wiesbaden
Colecția Springer Vieweg
Locul publicării:Wiesbaden, Germany

Cuprins

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.

Notă biografică

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.


Textul de pe ultima copertă

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.


Caracteristici

Multi-Agent Systems for Distributed AI in Manufacturing