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

ISBN-13: 9783658277413 / Angielski / Twarda / 2019 / 318 str.

Max Hoffmann

Spis treści

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.

O autorze

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.

Blurb

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.