Foreword	5
	Preface	7
	Contents	9
	List of Contributors	11
	1 Introduction to the Multi-Disciplinary Engineeringfor Cyber-Physical Production Systems	13
	1.1 Motivation	14
	1.2 Background	18
	1.3 Research Questions	24
	1.4 Book Structure	27
	1.4.1 Part I: Product Design	27
	1.4.2 Part II: Production System Engineering	28
	1.4.3 Part III: Information Modeling and Integration	30
	1.5 Who Shall Read This Book?	32
	References	35
	Part I Product and Systems Design	37
	2 Product and Systems Engineering/CA* Tool Chains	38
	2.1 Introduction	38
	2.2 Generic Procedures for the Development of Interdisciplinary Products	41
	2.2.1 Micro-logic in Development	42
	2.2.2 Process Models as Macro-logic in Development	45
	2.2.3 Process Models for CPS as an Interdisciplinary Technical System	48
	2.2.4 Systems Engineering as an Interdisciplinary Approach for Development of CPS	51
	2.3 Concretisation of Process Descriptions in the Sense of a Workflow	53
	2.3.1 Adaptation of Development Processes to the Context	55
	2.3.2 Identification of Context Factors for Adaption of Development Processes	57
	2.3.3 An Approach for Systematic Analysis of Determining Factors for the Development Process	58
	2.3.3.1 Goal System	60
	2.3.3.2 Object System	61
	2.3.3.3 Process System	62
	2.3.3.4 Action System	62
	2.3.3.5 Control of Development Tasks via the ZOPH Approach	64
	2.4 Model-Based Engineering for Mastering Complexity	65
	2.4.1 Model Based Systems Engineering	67
	References	71
	3 Cyber-Physical Product-Service Systems	74
	3.1 Introduction	75
	3.2 Research Methodology and Objectives	77
	3.3 Elements and Definition of Cyber-Physical Product-Service Systems	78
	3.3.1 Cyber-Physical Systems	78
	3.3.2 Product-Service Systems	79
	3.3.3 Cyber-Physical Product-Service Systems	81
	3.4 Challenges for Integrating CPPS and PSS LifeCycles	83
	3.4.1 Product LifeCycle Management	83
	3.4.2 Service LifeCycle Management	84
	3.4.3 Integration of PLM and SLM	85
	3.4.4 Engineering Challenges	86
	3.5 Implications for the Engineering Process	88
	3.5.1 Cross-Domain Requirements Engineering and Design	89
	3.5.2 Servitized Business Models Enabled by CPS	91
	3.6 Industrial Use Case	93
	3.7 Summary and Conclusions	95
	3.7.1 Research Questions Answered	95
	3.7.2 Strengths and Limitations	96
	References	96
	4 Product Lifecycle Management Challenges of CPPS	100
	4.1 Introduction	100
	4.2 State of the Art and Challenges of PLM in the CPPS Context	107
	4.2.1 Processes and Methods	108
	4.2.2 Model Representation	110
	4.2.3 Information Management and Integration	111
	4.3 PLM Forward and Backward Information Flows in CPPS	113
	4.4 Summary and Outlook	118
	References	119
	Part II Production System Engineering	122
	5 Fundamentals of Artifact Reuse in CPPS	123
	5.1 Introduction	124
	5.2 Approach	127
	5.3 Generic Production System Architecture	128
	5.3.1 Literature Review	128
	5.3.2 Hierarchy Layers	129
	5.4 Production System Life Cycle	135
	5.4.1 Characteristics of Engineering Phase	137
	5.4.2 Characteristics of Operation and Maintenance Phase	140
	5.4.3 Characteristics of End-of-Life Phase	142
	5.5 Summary and Outlook	144
	References	145
	6 Identification of Artifacts in Life Cycle Phases of CPPS	149
	6.1 Introduction	150
	6.2 Engineering Phase	151
	6.2.1 Approach for the Identification of Artifactsin the Engineering Phase	151
	6.2.2 Identification Criteria for Artifacts in Engineering Phase	152
	6.2.2.1 Requirements	152
	6.2.2.2 Layouts and Visualizations	152
	6.2.2.3 Basic Specifications	152
	6.2.2.4 Behavior Models	152
	6.2.2.5 CAD Construction	153
	6.2.3 Usage of Engineering Phase Artifacts	154
	6.3 Operation and Maintenance Phase	157
	6.3.1 Approach for the Identification of Artifactsin the Operation and Maintenance Phase	157
	6.3.2 Identification Criteria for Artifacts in Operation and Maintenance Phase	158
	6.3.2.1 Construction Element	160
	6.3.2.2 Component	161
	6.3.2.3 Function Group	162
	6.3.2.4 Work Station	162
	6.3.2.5 Work Unit	163
	6.3.2.6 Production Line Segment	163
	6.3.2.7 Production Line	164
	6.3.2.8 Factory	164
	6.3.2.9 Production Network	165
	6.3.3 Usage of Operation and Maintenance Phase Artifacts	165
	6.4 End-of-Life Phase	169
	6.4.1 Approach for the Identification of Artifactsin the Engineering Phase	170
	6.4.2 Identification Criteria for Artifacts in End-of-Life Phase	170
	6.4.3 Usage of End-of-Life Phase Artifacts	173
	6.5 Summary and Outlook	174
	References	175
	7 Description Means for Information Artifacts Throughout the Life Cycle of CPPS	178
	7.1 Introduction	179
	7.2 Disambiguation: Description Means, Information Handling Methods, and Tools	180
	7.3 Description Means for Artifacts	181
	7.3.1 Description Means During Engineering Phase	181
	7.3.2 Description Means During Operation and Maintenance Phase	183
	7.3.3 Description Means During End-of-Life Phase	185
	7.4 Artifact Classification	187
	7.5 Summary and Outlook	187
	References	192
	8 Engineering of Next Generation Cyber-Physical Automation System Architectures	193
	8.1 Introduction	194
	8.2 The Evolution of Automation System Architectures	195
	8.2.1 Classical Automation System Architectures	196
	8.2.2 Emerging Automation System Architectures	197
	8.3 The Transformation of Automation System Architectures	202
	8.3.1 Towards Information-Driven Automation Systems	202
	8.3.2 Migration Strategies	204
	8.4 Considerations on Future Automation System Architectures	206
	8.4.1 Rethinking of Automation Systems Engineering	206
	8.4.2 Directions and Challenges	207
	8.5 Conclusion and Outlook	209
	References	211
	9 Engineering Workflow and Software Tool Chains of Automated Production Systems	215
	List of Abbreviations	215
	9.1 Introduction	216
	9.2 Engineering Workflow of Production System	217
	9.3 Established Tool Chains in Practice	220
	9.3.1 Tool Chain for Mechanical Design	222
	9.3.2 Tool Chains of Electrical Design	229
	9.3.3 Tool Chain of PLC/Software Design	231
	9.3.4 Tool Chain of Virtual Engineering	235
	9.3.5 Tool Chain of Virtual Commissioning	237
	9.4 Summary and Outlook	240
	References	241
	10 Standardized Information Exchange Within Production System Engineering	243
	10.1 Introduction	243
	10.2 Use Cases for Information Exchange	246
	10.2.1 Use Case 1: Production System Hierarchies	246
	10.2.2 Use Case 2: Integration of Pre-developed Production System Units	248
	10.2.3 Use Case 3: Exchange of Control System Engineering Information	248
	10.2.4 Use Case 4: Consistent and Up-To-Date Documentation	249
	10.2.5 Use Case 5: Combination of Engineering and Runtime Information	249
	10.2.6 Current Activities Related to Solution of the Use Cases	250
	10.2.6.1 Development of Modular and Hierarchical Production System Architectures	250
	10.2.6.2 Standardization of Data Exchange Formats	250
	10.2.6.3 Integration of Engineering Data Representations and Runtime Communication Systems	251
	10.3 Information Exchange Technologies	251
	10.4 AutomationML	254
	10.5 Challenges Within Standardization of Information Exchange	259
	10.6 Summary	263
	References	263
	Part III Information Modeling and Integration	266
	11 Model-Driven Systems Engineering: Principles and Application in the CPPS Domain	267
	11.1 Introduction	267
	11.2 Model-Driven Engineering in a Nutshell	271
	11.2.1 Metamodeling	272
	11.2.2 Model Transformations	273
	11.3 Selected MDSE Standards for CPPS Engineering	275
	11.3.1 Systems Modeling Language (SysML)	275
	11.3.2 Modeling and Analysis of Real-Time Embedded System Profile (MARTE)	277
	11.3.3 Performance Modeling Interchange Format (PMIF)	279
	11.3.4 AutomationML	280
	11.3.5 Synopsis	282
	11.4 MDSE of CPPS in Action	282
	11.4.1 Case Study	283
	11.4.2 CPPS Modeling	284
	11.4.2.1 Modeling in SysML	285
	11.4.2.2 Profiling SysML Models with MARTE	288
	11.4.2.3 Modeling in PMIF	289
	11.4.2.4 Modeling in AML	292
	11.4.3 CPPS Engineering Chain Automation	294
	11.4.3.1 Integrating SysML and AML	294
	11.4.3.2 Integrating AML and PMIF	296
	11.4.4 Synopsis	298
	11.4.5 Critical Discussion	299
	11.5 Conclusion and Future Challenges	300
	References	302
	12 Semantic Web Technologies for Data Integration in Multi-Disciplinary Engineering	306
	12.1 Introduction	306
	12.2 Industry Needs for Semantic Web Technologies	308
	12.3 Semantic Web Technologies: Key Concepts and Capabilities	315
	12.3.1 Key Elements of Semantic Web Technologies	316
	12.3.2 Data Integration with Semantic Web Technologies	318
	12.3.3 Semantic Web Capabilities	319
	12.4 Adoption of Semantic Web Technologies in Multi-Disciplinary Engineering Settings	322
	12.5 Use Case: Engineering Data Integration in a Multi-Disciplinary Engineering Setting	324
	12.6 A SWT-Based Solution for Data Integration	325
	12.6.1 Ontologies Used for Data Integration	326
	12.6.2 Mappings Across Local and Common Ontologies	327
	12.6.3 Implementation Details and Functionality	329
	12.7 Summary	331
	References	332
	13 Patterns for Self-Adaptation in Cyber-Physical Systems	335
	13.1 Introduction	336
	13.2 Background	337
	13.2.1 Uncertainties	337
	13.2.2 Adaptation	339
	13.2.2.1 Architecture-Based Adaptation	339
	13.2.2.2 Multi-Agent Based Approaches	340
	13.2.2.3 Self-Organizing Based Approaches	340
	13.2.3 Collective Intelligence Systems	341
	13.3 Research Questions	343
	13.4 Systematic Mapping Study Method	344
	13.4.1 Search and Selection Strategy	345
	13.4.2 Data Extraction	346
	13.4.3 Data Analysis and Reporting	346
	13.5 Adaptation in Cyber-Physical Systems	347
	13.6 Threats to Validity	354
	13.7 Reflection of the Systematic Mapping Study Results	355
	13.8 Patterns for Self-Adaptation	355
	13.8.1 Synthesize-Utilize Pattern	356
	13.8.2 Synthesize-Command Pattern	358
	13.8.3 Collect-Organize Pattern	359
	13.9 Potential of Collective Intelligence Systems for Cyber-Physical Systems and Cyber-Physical Production Systems	361
	13.9.1 Collective Intelligence Systems for Capability Augmentation	361
	13.9.2 Collective Intelligence Systems as Enabler for Emergent Machine-To-Machine Interactions	362
	13.9.3 Collective Intelligence Systems as Coordinators and Knowledge Integrators Across Heterogeneous, Multi-Disciplinary Domains	364
	13.10 Related Work	365
	13.11 Conclusion and Future Work	367
	References	368
	14 Service-Oriented Architectures for Interoperability in Industrial Enterprises	373
	14.1 Introduction	373
	14.2 Technical Features of the Industrial Enterprise	374
	14.3 Service-Oriented Architectures and the Industrial Enterprise	377
	14.3.1 IoT@Work	378
	14.3.2 PLANTCockpit	378
	14.3.3 IMC-AESOP	380
	14.3.4 eScop	381
	14.3.5 Arrowhead Framework	384
	14.4 Realizations of the Reference Architectures	385
	14.4.1 Service Discovery	385
	14.4.2 Service Description	387
	14.4.3 Data Representation and Access	389
	14.4.4 Information and Message Encoding	391
	14.4.5 Message Exchange	392
	14.4.6 Networking, Data Link and Media	394
	14.4.7 Security	395
	14.5 Discussion	397
	References	398
	15 A Deterministic Product Ramp-up Process: How to Integrate a Multi-Disciplinary Knowledge Base	403
	15.1 Introduction	404
	15.2 Strategy-Dependent Relevance	406
	15.3 Structure of a Production Process	408
	15.4 Qualification of a Production Process	409
	15.5 Product Ramp-up and the Agility of Production Systems	413
	15.6 Invoking an Effective Multi-disciplinary Knowledge Base	419
	15.7 Information Model and Matchmaking Scenarios	421
	15.8 Needs for Standardization Across Enterprises	431
	15.9 Outlook: Deterministic Product Ramp-up for Supply Chains	432
	References	433
	16 Towards Model Quality Assurance for Multi-Disciplinary Engineering	436
	16.1 Introduction	437
	16.2 Background	439
	16.2.1 Stakeholder Needs for Model Quality Assurance	439
	16.2.2 Model-Driven Engineering	440
	16.2.3 AutomationML	441
	16.2.4 Quality Assurance and Model Review	441
	16.3 Research Questions	443
	16.4 Model Quality Assurance Concept	445
	16.4.1 Adapted Review Process for MDE and AutomationML MQA	445
	16.4.2 A Generic Reviewing Language	447
	16.4.3 Utilizing the Generic Reviewing Language for AutomationML	449
	16.5 Conceptual Evaluation	451
	16.5.1 Illustrative Use Case: Round-Trip-Engineering	452
	16.5.2 MQA-Review Needs and Expected Tool Capabilities	454
	16.5.3 Evaluation of MQA-Review with Tool Support	455
	16.6 Summary, Limitations, and Outlook	457
	References	459
	17 Conclusions and Outlook on Research for Multi-Disciplinary Engineering for Cyber-Physical Production Systems	461
	Index	471