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Adaptive and Scalable Information Modeling to Enable Autonomous Decision Making for Real-Time Interoperable Factories

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Adaptive and Scalable Information Modeling to Enable Autonomous Decision Making for Real-Time Interoperable Factories Adaptive and Scalable Information Modeling to Enable Autonomous Decision Making for Real-Time Interoperable Factories Adaptive und skalierbare Informationsmodellierung zur Erm¨oglichung autonomer Entscheidungsfindungsprozesse f¨urinteroperable, reaktive Fertigungen Von der Fakult¨atf¨ur Maschinenwesen der Rheinisch-Westf¨alischen Technischen Hochschule Aachen zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften genehmigte Dissertation vorgelegt von Max Hoffmann Berichter: Univ.-Prof. Dr. rer. nat. Sabina Jeschke Univ.-Prof. Dr.-Ing. Birgit Vogel-Heuser Tag der m¨undlichen Pr¨ufung: 26. Juni 2017 Diese Dissertation ist auf den Internetseiten der Universit¨atsbibliothekonline verf¨ugbar. Acknowledgments This work was created as part of my employment at the Institute of Information Management in Mechanical Engineering (IMA) of the RWTH Aachen University throughout the past five years. During this time, I had the opportunity to work on exciting projects, talk to people of many different scientific backgrounds and learn a lot about various interesting topics. First of all, I would like to thank my colleagues who accompanied me along this journey to my Ph.D. thesis. Great thanks are dedicated to Prof. Dr.-Ing. Tobias Meisen, whose scientific advise was extremely helpful during the process of my scientific development and in the course of this work. I would like to thank my working colleagues from the Production Technology research group at the IMA for many enlightening conversations and their feedback in terms of my research. I also thank all further colleagues at the Cybernetics Lab IMA/ZLW & IfU at the RWTH Aachen University for making this institute to such a special place. Many thanks go to the various student coworkers, who spend days { and sometimes night shifts { in developing and finalizing programming code, applications or demonstration scenarios for the use-cases of this work. Among all of them, especially to mention at this point are Fabian Neumann, Fabian Scheidt and Stefan Angerm¨ullerfor their extraordinary efforts during stressful working phases, e.g. ahead of the Hannover Fair 2016. Very special thanks are dedicated to the students that pursued their master thesis under my supervision. Without you the present work would have never reached the scientific level and degree of maturity that it has today. Special thanks go the Stephan Groß for his work on interoperability in OPC Unified Architecture networks, Philipp Thomas for his developments on Multi-Agent Systems enabled by OPC UA, Jan Schlageter for targeting machine learning scenarios by means of the framework and Felix Wolf for his works on interoperability with high-level planning systems. I thank the VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik for enabling my participation in the expert group "Agentensysteme" (FA 5.15: agent systems) under the supervision of Prof. Dr.-Ing. Birgit Vogel-Heuser, in which I gained significant insights and inspirations for use-case ideas, especially in terms of the "myJoghurt" joint project. I thank the excellence cluster "Integrative Production Technology for High-Wage Countries" of the RWTH Aachen University and the DFG for their support during my thesis, especially for enabling the presentation of my demonstration scenario at the Hannover Fair 2016. I am greatful for the supervision of my work by Prof. Dr. rer. nat. Sabina Jeschke from the Cybernetics Lab and Prof. Dr.-Ing. Birgit Vogel-Heuser from the Institute of Automation and Information Systems (AIS) at the Technische Universit¨atM¨unchen. Last but not least, I thank my family and my friends for their support during all phases of my dissertation, especially my father Helmut for deep conversations and critical inspirations, and my mother Elisabeth as well as my brother Tim for all their encouraging words. You all play a big role in the completion of this work. I Acknowledgments II Contents Acknowledgments I Abbreviations XI Glossary XVII Abstract 1 Zusammenfassung 2 1 Introduction 3 1.1 Motivation . .3 1.1.1 Changing reality { a global perspective . .4 1.1.2 Trends and visions of modern production . .4 1.1.3 Digitalization and the introduction of Cyber-Physical Systems . .5 1.1.4 Challenges of the digitalization and the industrial (r)evolution . .6 1.2 Research Goals . .8 1.3 Structure of the work . 10 2 Problem description and fundamental concepts 13 2.1 Strategical decision-making in a factory of the future . 13 2.2 Fundamental concepts of industrial manufacturing . 17 2.3 Emerged manufacturing systems in industrial production . 19 2.3.1 Static control systems in current factories . 19 2.3.2 The automation pyramid and hierarchical production organization . 20 2.3.3 Tight coupling in terms of conventional automation infrastructures . 23 2.4 The duality of loosely and tightly-coupled systems . 24 3 State of the Art 25 3.1 Global strategies towards future manufacturing . 26 3.1.1 Reference architectures for an Industry 4.0 . 26 3.1.2 Important research programs targeting the global challenges . 27 3.2 Current approaches for production optimization . 28 3.2.1 Organizational optimization of the production process . 28 3.2.2 Long-term optimization of production goals using low-level data . 30 3.2.3 Autonomous optimization strategies in terms of self-optimization . 30 3.3 Interoperability approaches for manufacturing systems . 31 III Contents 3.4 Technical solutions for automated production control . 33 3.4.1 Fieldbus protocols . 34 3.4.2 Industrial Ethernet . 34 3.4.3 Publish-Subscribe within industrial applications . 37 3.4.4 TSN and real-time Ethernet in industrial applications . 39 3.5 Web Services and Service-Oriented Architectures . 40 3.6 Intelligent production automation by means of Multi-Agent Systems . 41 3.6.1 Fundamental concepts of Multi-Agent Systems . 42 3.6.2 Communication Protocols and Standards for Multi-Agent Systems . 47 3.6.3 Agent-based approaches in production . 49 3.6.4 Agent-Based solutions for intelligent manufacturing . 50 3.6.5 Holonic Manufacturing Systems . 52 3.6.6 Comparison of traditional and HMS inspired control solutions . 53 3.7 Machine Learning in distributed environments . 54 3.7.1 Basic characteristics of Machine Learning . 54 3.7.2 Applications of Machine Learning in Manufacturing . 55 3.8 Information modeling for intelligent automation . 56 3.9 Advanced interoperability standards for manufacturing . 58 3.9.1 OPC { The interface standard for generic shop floor interoperability . 59 3.9.2 OPC UA { The interface standard for integrated communication and information modeling . 63 3.9.3 OPC UA system architecture and basic services . 66 3.9.4 AddressSpace model and OPC UA based information modeling . 67 3.9.5 Security aspects of OPC UA for reliable information exchange . 69 3.9.6 Service-oriented and event-driven approaches through OPC UA . 70 3.9.7 Interoperability with tightly coupled systems by means of OPC UA and Time-Sensitive Networking . 71 3.9.8 Companion standards and domain-specific models for OPC UA . 75 3.9.9 Realization of agent-based and decentralized intelligence approaches through OPC UA . 77 4 Architecture of a framework for real-time interoperable factories 81 4.1 Requirement analysis for legacy systems in automated production sites . 82 4.1.1 Management perspective of automated production systems . 82 4.1.2 Engineering perspective for the requirements of flexible automation sys- tems..................................... 83 4.1.3 Requirements from an information technological perspective . 85 4.2 A multi-agent architecture enabling interoperability with existing automation solutions . 86 4.2.1 Basic Architecture . 87 4.2.2 Internal agent architecture . 89 4.2.3 Holonic Manufacturing System (HMS) reference architecture . 93 4.2.4 Cross-domain architecture . 95 4.3 Overall MAS architecture for cooperating agents in a smart factory environment 97 4.3.1 Multi-Agent System Architecture . 97 4.3.2 Internal agent behavior and hardware component interaction . 100 4.4 Smart automation of a flexible production by means of evolving software agents102 IV Contents 4.5 Limitations of the architecture { communication and evolutionary capabilities 103 4.5.1 Critical discussion in terms of communication flexibility . 103 4.5.2 Scalability limitations { Learning and evolutionary development . 104 5 Agent OPC UA { Semantic scalability and interoperability architecture for Multi-Agent Systems through OPC UA 105 5.1 Bridging the gap to OPC Classic . 107 5.2 Information modeling and infrastructure for CPPS . 108 5.3 Implementation of OPC UA enabled multi-agent systems . 110 5.3.1 Agent requirements specification . 110 5.3.2 Multi-Agent System requirements specification . 110 5.3.3 Required functionalities of an OPC UA enabled MAS . 111 5.3.4 Messaging System . 112 5.3.5 Data storage . 120 5.3.6 Reading values from remote devices . 123 5.3.7 White Page Services . 126 5.3.8 Yellow Page Service Realization . 128 5.3.9 Discovery Process in Multi-Agent System (MAS) and dynamic networking128 5.3.10 Incorporation of traditional MAS through gateway agents . 129 5.3.11 Interconnection of multiple MAS by means of mediation agents . 132 5.4 Representation of intelligent agents by means of OPC UA . 133 5.4.1 OPC UA AddressSpace representation for intelligent agents . 134 5.4.2 OPC UA AgentType model . 135 5.5 Semantic integration of agent communication and interoperability . 142 5.5.1 The MessageType object definition . 142 5.5.2 Hierarchical organization of Message objects . 144 5.5.3 Compatibility of OPC UA to legacy ACL messages . 144 5.5.4 The ContentType specification . 146 5.5.5 The AbilityType specification . 146 5.5.6 SensorType definitions . 147 5.5.7 Overall information model for OPC UA based MAS . 147 6 Management system integration of OPC UA based multi-agent systems 149 6.1 Architecture extension for decentralized planning . 149 6.1.1 General capabilities and requirements for resource planning .
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