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

Description

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. Front Matter ....Pages I-XXXIV Introduction (Max Hoffmann)....Pages 1-14 Problem Description and Fundamental Concepts (Max Hoffmann)....Pages 15-32 State of the Art (Max Hoffmann)....Pages 33-112 Architecture of a Framework For Real-Time Interoperable Factories (Max Hoffmann)....Pages 113-144 Agent OPC UA – Semantic Scalability and Interoperability Architecture for Multi-Agent Systems through OPC UA (Max Hoffmann)....Pages 145-202 Management System Integration of OPC UA based MAS (Max Hoffmann)....Pages 203-214 Flexible Manufacturing based on Autonomous, Decentralized Systems (Max Hoffmann)....Pages 215-230 Use-cases for Industrial Automation Processes (Max Hoffmann)....Pages 231-256 Future Research Topics (Max Hoffmann)....Pages 257-259 Back Matter ....Pages 261-318