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

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 upg...

Full description

Main Author: Hoffmann, Max
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Wiesbaden Springer Fachmedien Wiesbaden 2019, 2019
Edition:1st ed. 2019
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
LEADER 02903nmm a2200325 u 4500
001 EB001874259
003 EBX01000000000000001037627
005 00000000000000.0
007 cr|||||||||||||||||||||
008 191022 ||| eng
020 |a 9783658277420 
100 1 |a Hoffmann, Max 
245 0 0 |a Smart Agents for the Industry 4.0  |h Elektronische Ressource  |b Enabling Machine Learning in Industrial Production  |c by Max Hoffmann 
250 |a 1st ed. 2019 
260 |a Wiesbaden  |b Springer Fachmedien Wiesbaden  |c 2019, 2019 
300 |a XXXIV, 318 p. 111 illus  |b online resource 
505 0 |a 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 
653 |a Communications Engineering, Networks 
653 |a Industrial engineering 
653 |a Industrial and Production Engineering 
653 |a Artificial Intelligence 
653 |a Artificial intelligence 
653 |a Telecommunication 
710 2 |a SpringerLink (Online service) 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
856 |u https://doi.org/10.1007/978-3-658-27742-0?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 006.3 
520 |a 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