Foreword	7
	Cyber-Physical Systems for Production Technology	7
	Contents	11
	About the Editors	15
	Introduction and Overview	18
	1 Industrial Internet of Things and Cyber Manufacturing Systems	19
	1 Introduction	19
	2 Foundations of the Industrial Internet of Things and Cyber Manufacturing Systems	20
	3 Potentials and Challenges	24
	4 Major Research Initiatives	26
	5 Approaches and Solutions	27
	5.1 Modeling for CPS and CMS	27
	5.2 Architectural Design Patterns for CMS and IIoT	29
	5.3 Communication and Networking	30
	5.4 Artificial Intelligence and Analytics	30
	5.5 Evolution of Workforce and Human-Machine-Interaction	32
	6 A Glance into the Future: Towards Autonomous Networked Manufacturing Systems	33
	Acknowledgments	34
	References	34
	2 An Application Map for Industrial Cyber-Physical Systems	36
	1 An Introduction to Cyber-Physical Systems	36
	2 Foundations of Industrial Cyber-Physical Systems	37
	2.1 Technical Dimension of Cyber-Physical Systems	38
	2.2 Human Dimension of Cyber-Physical Systems	39
	2.3 Organizational Dimension of Cyber-Physical Systems	40
	3 Categories of Potential Improvement for Industrial Cyber-Physical Systems	42
	3.1 Automatization	44
	3.2 Autonomization	45
	3.3 Human-Machine Interaction	46
	3.4 Decentralization	47
	3.5 Digitization for Process Alignment	47
	3.6 Big Data	48
	3.7 Cyber Security	49
	3.8 Knowledge Management	49
	3.9 Qualification	50
	4 Elaboration of an Application Map for Industrial Cyber-Physical Systems	50
	4.1 Smart Factory	51
	4.2 Industrial Smart Data	52
	4.3 Industrial Smart Services	54
	4.4 Smart Products	55
	4.5 Product-Related Smart Data	56
	4.6 Product-Related Smart Services	57
	4.7 Utilization of the Application Map	57
	5 Summary and Outlook	59
	References	60
	3 Cyber-Physical Electronics Production	62
	1 Trends and Requirements in Modern Electronics Production	62
	1.1 Miniaturization and Function Integration	63
	1.2 Flexibility and Complexity	65
	1.3 Logistics and Production Concepts	67
	2 Enabling E-CPS Technologies	71
	2.1 Sensor Integration, Printing Technologies and Communications	71
	2.2 Software Systems	74
	2.3 Autonomous and Smart One-Piece-Flow	79
	3 Concept of a Cyber-Physical Electronics Production System	83
	3.1 Self-Learning Electronics Production Processes	84
	3.2 Assistance Systems	86
	3.3 Integrated Cyber-Physical Electronics Production	89
	References	91
	Modeling for CPS and CMS	94
	4 Cyber-Physical Systems Engineering for Manufacturing	95
	1 Introduction	95
	2 Cyber-Physical Systems	96
	3 Systems Engineering	97
	4 Manufacturing Innovation	98
	5 Smart Manufacturing Systems Programs at NIST	99
	5.1 Smart Manufacturing Systems Design and Analysis	100
	5.1.1 Modeling Methodologies for Manufacturing System Analysis	101
	5.1.2 Predictive Analytics for Manufacturing Systems	102
	5.1.3 Performance Measurement for Smart Manufacturing	104
	5.1.4 Service-Based Manufacturing and Service Composition	105
	5.2 Smart Manufacturing Operations Planning and Control	107
	5.2.1 Digital Thread for Smart Manufacturing	108
	5.2.2 Systems Analysis Integration for Smart Manufacturing Operations	112
	5.2.3 Wireless Systems for Industrial Environments	112
	5.2.4 Cybersecurity for Smart Manufacturing Systems	115
	5.2.5 Prognostics, Health Management and Control	116
	5.2.6 Smart Manufacturing Systems Test Bed	118
	6 Summary and Concluding Remarks	119
	References	120
	5 Model-Based Engineering of Supervisory Controllers for Cyber-Physical Systems	125
	1 Introduction	125
	1.1 Model-Based Systems Engineering	125
	1.2 Structure of This Chapter	128
	2 Synthesis-Based Development of Coordination Control	128
	3 Description of an AGV System	131
	3.1 Components of the Multi Mover	132
	3.2 Interaction of the Components	133
	4 Hybrid Models of the Uncontrolled System	134
	4.1 Multi Mover High-Level Modes and Movement	135
	4.2 Drive Motor	136
	4.3 Steer Motor	137
	4.4 Ride Control	137
	4.5 Battery Sensor	137
	4.6 Proximity Sensor	138
	4.7 Bumper Switch	138
	4.8 LEDs and Buttons	139
	4.9 Abstraction Form Hybrid Automata to Discrete-Event Models	139
	5 Requirements of the System	140
	5.1 Emergency and Error Handling	140
	5.2 LED Actuation	141
	5.3 Motor Actuation	141
	5.4 Button Handling	143
	5.5 Proximity Sensors and Ride Control Handling	144
	6 Synthesis of Supervisory Controller	145
	7 Simulation-Based Visualization	146
	8 Concluding Remarks	148
	References	148
	6 Formal Verification of SystemC-based Cyber Components	151
	1 Introduction	151
	2 Related Work	153
	3 Preliminaries	155
	3.1 Bounded Model Checking and Induction	155
	3.2 SystemC Basics	156
	4 TLM Property Checking	156
	4.1 Simplified Model of the SystemC Kernel	157
	4.2 Model Generation	161
	4.2.1 SystemC to SCTLMD	161
	4.2.2 Kernel Integration	162
	4.2.3 Limitations	163
	4.3 Property Language and Monitor Generation	165
	4.3.1 Simple Safety Properties	166
	4.3.2 Transaction Properties	166
	4.3.3 System-Level Properties	167
	4.4 BMC-Based Verification	167
	5 Induction-Based TLM Property Checking	169
	6 Experiments	172
	6.1 BMC-Based Verification	173
	6.1.1 FIFO Design	173
	6.1.2 TLM-2.0 Design	174
	6.2 Induction-Based Verification	176
	6.2.1 FIFO Design and TLM-2.0 Design	176
	6.2.2 JPEG Encoder	177
	6.2.3 Chain Benchmark	178
	6.3 Summary of Experimental Results	179
	7 Conclusions	179
	References	179
	7 Evaluation Model for Assessment of Cyber-Physical Production Systems	182
	1 Introduction	182
	1.1 Motivation	183
	2 A First Analysis of Value-Adds	185
	3 State-of-the-Art	187
	4 Approach and Methodology	191
	5 Abilities of Cyber-Physical Systems	193
	5.1 Abilities of Capturing and Processing Data and Information	194
	5.2 Abilities of High-Quality Analysis with Artificial Intelligence	195
	6 Advanced Abilities	196
	6.1 The “Industrie 4.0” Component	197
	6.2 Artificial Intelligence	198
	7 Performance Indicators	200
	7.1 Identified Performance Indicators	201
	7.2 Performance Indicators of the Overall Systems Architecture	202
	7.2.1 Modularity	202
	7.2.2 Complexity	203
	7.3 Performance Indicators of Production Operation	203
	7.3.1 Maintainability	203
	7.3.2 Production Efficiency	204
	7.4 Performance Indicators of Changing Production Systems	204
	7.4.1 Re-configurability	204
	7.4.2 Automatic Planning	204
	7.4.3 Automatic Adaptation	205
	7.5 Performance Indicators of Cyber Support	205
	7.5.1 Social Interaction	205
	7.5.2 Support of Decisions	205
	7.5.3 Further Characteristics: Usability	206
	7.6 Relation Between Performance Indicators and Abilities	206
	8 Validation	206
	9 Conclusion	208
	10 Further Work	210
	References	210
	Architectural Design Patterns for CMS and IIoT	213
	8 CPS-Based Manufacturing with Semantic Object Memories and Service Orchestration for Industrie 4.0 Applications	214
	1 Introduction	214
	2 Use Cases	216
	2.1 Use Case 1: Orchestrated Production	219
	2.2 Use Case 2: Automated Maintenance	220
	2.3 Use Case 3: Priority Management	221
	3 Related Work	222
	3.1 Semantic Technologies	222
	3.2 Hardware Requirements	223
	3.3 M2M Communication	224
	3.4 Digital Object Representations	225
	3.5 Conclusion	227
	4 Digital Object Memories	227
	4.1 Data Model	228
	4.2 Storage Infrastructure	230
	4.3 Communication Interfaces	232
	5 Semantic Service Orchestration	233
	5.1 Service Discovery	234
	5.2 Semantic Orchestration	235
	6 Conclusion	237
	References	238
	9 Integration of a Knowledge Database and Machine Vision Within a Robot-Based CPS	241
	1 Introduction	241
	2 Terms and Conditions	242
	3 Research Efforts	243
	4 Handling Dangerous Goods Using Industrial Machine Vision	244
	4.1 Analysis of the Process Flow and the Boundary Conditions	245
	4.2 Solution	246
	4.3 Detection the Orientation of the Manhole Cover	248
	4.4 Examination of Manhole Cover Seal	249
	4.5 Operator Interface of the System	249
	4.6 Control and Communication Concept	250
	4.7 Conclusion	251
	5 Laundry Logistics in Conjunction with RFID Systems	251
	5.1 Prospective Applications of Industry 4.0 in the Textile Industry	253
	5.2 Implementation of Industry 4.0 Within Industrial Laundries	254
	5.3 Conclusion	256
	6 Production, in Particular Assembly with the Help of Physical Human-Robot Interaction (PHRI)	257
	6.1 Challenges	257
	6.1.1 Objectives and Approach	257
	6.1.2 Description of Process	258
	6.2 Planning	259
	6.2.1 Analysis	259
	6.2.2 Sub Problems	259
	6.2.3 Requirements	259
	6.3 Applied Technologies	260
	6.3.1 Robotics	260
	6.3.2 Machine Vision and Data Bases	260
	6.3.3 Pre-separation	260
	6.3.4 Robotics and Cell	261
	6.3.5 Tools Used	261
	6.3.6 Human Machine Interface	262
	6.3.7 Search Strategy	264
	6.3.8 Image Processing and Database	264
	6.4 Procedure for Developing the Solution	265
	6.5 Summary	266
	7 Research Challenges and Conclusions	267
	References	268
	10 Interoperability in Smart Automation of Cyber Physical Systems	271
	1 Introduction	271
	2 Related Work	273
	2.1 Machine-Machine Interaction	273
	2.1.1 Field Bus Systems and the Industrial Ethernet	274
	2.1.2 Data Distribution Service (DDS)	275
	2.1.3 OPC Unified Architecture	276
	2.2 Human-Robot-Interaction	279
	3 Holistic Interoperability in Production Networks	282
	3.1 Interoperability on Machine-Machine-Level Using OPC UA and Semantic Technologies	282
	3.2 Using Artificial Intelligence to Learn from Data	283
	4 Towards Interoperability in Human-Robot Interaction	284
	4.1 System Design	285
	4.2 Gathering	285
	4.3 Recognition and Evaluation	286
	4.4 Respond	286
	5 Use Cases	287
	5.1 Adaptable Demonstrator for Flexible Production Organization	287
	5.2 Virtual Production Intelligence Platform	290
	5.3 Canoe	292
	6 Conclusion	293
	References	294
	11 Enhancing Resiliency in Production Facilities Through Cyber Physical Systems	297
	1 Introduction	297
	2 The Need for Resilient Factories in the Context of Industry 4.0	298
	2.1 Cyber-Physical Systems, Self-Optimization and the Internet of Things	298
	2.1.1 Cyber-Physical Systems (CPS)	298
	2.1.2 Cyber-Physical Production System (CPPS)	299
	2.1.3 Self-Optimization	300
	2.1.4 Internet of Things	300
	2.1.5 Industry 4.0	301
	2.2 Resilient Production Systems	301
	2.2.1 Market Trend Customized Products	301
	2.2.2 Technology Trend Cyber-Physical Production Systems	302
	2.2.3 Global Trend of Energy and Resource Efficiency	303
	2.2.4 The Resilient Factory as a Response to the Given Trends	303
	3 Objectives of the Resilient Factory	304
	4 Two Example Cases for Cyber-Physical Systems in Production	306
	4.1 Energy-Oriented Manufacturing Planning and Control System	306
	4.1.1 Requirements and Conditions	306
	4.1.2 Concept and Procedure	308
	4.1.3 Applications and Potentials	311
	4.2 Smart Glasses in Industrial Assembly	311
	4.2.1 Overview of Smart Devices Established on the Market	312
	4.2.2 Current Applications and Suitability of Smart Devices in Production	313
	4.2.3 Potentials for Increased Productivity and New Applications	314
	5 Prerequisites and Requirements for Cyber-Physical Production Systems in the Resilient Factory	315
	5.1 Technical Requirements	316
	5.1.1 Decentralized IT Structures	316
	5.1.2 Data Security	317
	5.1.3 Modularity of Technical Systems	317
	5.1.4 Fusion of Shopfloor- and Office-IT	317
	5.1.5 Integration of People: Socio-Technical Systems	318
	5.2 Scientific Requirements	319
	5.2.1 Reliability of Adaptive, Learning Systems	319
	5.2.2 Integration of Risk Management for Non-deterministic Systems	319
	5.2.3 Model-Based Cooperation: Contradictions, Incompleteness, Failures	320
	5.2.4 Emergent Patters	320
	5.2.5 Conflicting Data and Information	320
	6 Conclusion	321
	References	322
	Communication and Networking	324
	12 Communication and Networking for the Industrial Internet of Things	325
	1 Introduction	325
	2 Communication in Industrial Automation	326
	2.1 Definitions	326
	2.2 Current Trends in Industrial Communications	328
	2.3 Challenges for the Industrial Internet of Things	329
	3 Communication Within a Local Automation Cell	330
	3.1 Diversity Schemes	331
	3.1.1 Frequency Diversity	331
	3.1.2 Time Diversity	332
	3.1.3 Spatial Diversity	332
	3.2 Medium Access Control	333
	3.2.1 Performance Metrics with Regard to the IIoT	333
	3.2.2 Example: IEEE 802.15.4	334
	4 Communication Within the Factory Hall and Beyond	335
	4.1 Routing and Addressing Background	336
	4.2 Challenges for Routing and Addressing in the IIoT	338
	4.2.1 IPv6 and 6LoWPAN	339
	4.3 Addressing and Routing in Standardized Protocol Stacks	340
	5 Application Layer Communication	344
	5.1 The Constrained Application Protocol	345
	5.1.1 The Block Mode	346
	5.1.2 The Observer Mode	348
	5.1.3 CoAP and Proxies	348
	5.2 Cloud and Distributed Processing	350
	6 Conclusion and Outlook	350
	References	351
	13 Communications for Cyber-Physical Systems	355
	1 Introduction	355
	2 Data Communication Networks	357
	3 Types of Communication Networks for CPSs	360
	4 Impact of Communication Network Deficiencies	363
	5 Reliable Communications Within CPSs	365
	6 Approaches to Improve Communications Reliability	366
	6.1 Network Provided QoS Provisioning	367
	6.2 Redundancy	367
	6.3 New Network Generations	368
	7 CPS Communications Using the Internet	369
	8 Communication Standards for CPSs	371
	9 Communication Patterns for CPSs	374
	9.1 Request-Response	375
	9.2 Discovery	375
	9.3 Publish-Subscribe	376
	10 Conclusion	376
	References	377
	Artificial Intelligence and Data Analytics for Manufacturing	381
	14 Application of CPS in Machine Tools	382
	1 Motivation for CPS in the Manufacturing Environment	382
	2 State of the Art—Literature Review	384
	2.1 Characteristics of Cyber-Physical Systems in Manufacturing	384
	2.2 Classification of Intelligent Objects in the Machining Process	385
	2.3 Definition of “Real Time” in the Context of Manufacturing	388
	2.4 Derivation of Requirements	389
	3 Approach to Information Distribution	390
	4 Solutions in the Area of Machining	392
	4.1 Intelligent Chuck for Turning Machine	392
	4.1.1 Phase I: Company Management Level—Analysis of the Turning Process	393
	4.1.2 Phase II: Production Management Level—Scheduling and Situational Production Control	393
	4.1.3 Phase III: Shop Floor—Development of a Chuck Control	394
	4.2 Intelligent Milling Tool	395
	4.2.1 Phase I: Company Management Level—Smart Analytics	396
	4.2.2 Phase II: Production Management Level—Automatic Configuration	397
	4.2.3 Phase III: Shop Floor—Development of the Intelligent Tool	399
	5 Evaluation and Classification	401
	5.1 Validation of the Developed CPS	401
	5.2 Evaluation of the Defined Requirements	403
	5.3 Classification of the Developed Solutions	404
	6 Summary	405
	References	405
	15 Going Smart—CPPS for Digital Production	408
	1 Introduction	408
	2 Technology Knowledge for Digital Production	409
	2.1 Sensors—Perception Organs of CPPS	410
	2.1.1 Concepts of Sensor Integration and Fusion	411
	2.1.2 Applications in Production Environments	413
	2.2 CPPS—The Architecture for Smart Applications	420
	2.2.1 Technology Models—Knowledge Carriers of Production Entities	421
	2.2.2 Tech Apps—The CPPS Human Machine Interface	423
	3 Summary	426
	Acknowledgments	428
	References	428
	16 Manufacturing Cyber-Physical Systems (Industrial Internet of Things)	430
	1 Introduction	430
	2 Preconditions and Standards	431
	3 Challenges	433
	3.1 Steps During the Tank Wagon Loading	433
	3.2 Activities on Top of the Filling Plant	434
	3.3 Activities on the Underside of the Filling Plant	434
	3.4 Opening of the Tank Wagon	435
	4 Requirements from the Manual Process to the Automation	436
	5 Solution Concept (Available Technologies, Intelligent IT, CPS Development)	437
	5.1 Structure of the System	437
	5.2 Process of the Automatic Opening of the Dome Cover	438
	5.3 Vision System	439
	5.4 Gripper System	441
	5.5 Robot System	441
	5.6 Security System	442
	5.6.1 Risks	442
	5.6.2 Technical and Complementary Protective Actions	443
	5.7 Total System: Dome Cover Opening System	444
	5.8 Flow and Operating Concept	447
	6 Research Challenges	448
	7 Conclusions	450
	References	451
	17 Cyber-Physical System Intelligence	453
	1 Introduction	453
	2 Autonomy of Cyber-Physical Systems (CPS) in a Smart Factory	455
	2.1 Components for CPS Autonomy	455
	3 Achieving CPS Autonomy in Smart Factories	458
	4 The RoboCup Logistics League (RCLL)	462
	5 Case Studies on Task-Level Executives for the RCLL	464
	5.1 CLIPS-Based Agent Program	464
	5.2 OpenPRS	467
	5.3 YAGI	469
	5.4 Common Behavioral Architecture	469
	6 Evaluation	470
	6.1 RoboCup 2014 Evaluation Using the CLIPS-Based Agent	470
	6.2 Automated Simulation Tournament for CLIPS and OpenPRS	472
	7 Conclusion	474
	Acknowledgments	474
	References	475
	18 Big Data and Machine Learning for the Smart Factory—Solutions for Condition Monitoring, Diagnosis and Optimization	479
	1 Introduction	479
	2 Big Data in CPSs	480
	3 Requirements and Challenges to Data Quality	482
	4 Condition Monitoring and Diagnosis	483
	4.1 Anomaly Detection Using Identified Hybrid Timed Automata	483
	4.2 Identification of Behavior Models Using Map/Reduce Technology	484
	4.3 Condition Monitoring in Continuous Processes	485
	5 System Optimization	487
	6 Smart Services and Applications	488
	7 Summary and Outlook	490
	References	491
	19 Overview of the CPS for Smart Factories Project: Deep Learning, Knowledge Acquisition, Anomaly Detection and Intelligent User Interfaces	492
	1 Introduction	492
	2 Technical Infrastructure	493
	2.1 Deep Learning	494
	2.2 Knowledge Acquisition	495
	2.3 Anomaly Detection	496
	2.4 Intelligent User Interfaces	498
	3 Use Cases	501
	3.1 Industrial Robots and Anomaly Modeling	501
	3.2 Anomaly Treatment in the Steel Domain	502
	3.3 Outlook: Anomaly Detection in the Energy Domain	505
	Acknowledgments	507
	References	507
	20 Applying Multi-objective Optimization Algorithms to a Weaving Machine as Cyber-Physical Production System	510
	1 Introduction	510
	2 Approach and Implementation of Multi-objective Self-optimization Procedure	512
	2.1 Signal Processing	512
	2.2 Measurement Technology of Warp Tension	514
	2.3 Measurement Technology for Energy Consumption	514
	2.3.1 Air Consumption Measurement	514
	2.3.2 Active Power Measurement	514
	2.3.3 Measurement Technology for Fabric Quality	515
	2.4 Program Steps	515
	3 Desirability Functions and Nelder/Mead Algorithm	516
	3.1 Desirability Functions	516
	3.2 Nelder/Mead Algorithm	517
	3.3 Experimental Results	518
	4 Conclusion and Outlook	521
	References	521
	21 Cyber Physical Production Control	523
	1 Current Challenges of Production Control	523
	2 Vision of a Cyber Physical Production Control	525
	2.1 Smart Decision Support in Daily Life	525
	2.2 Enabler for Decision Support Systems in Production Control	526
	2.2.1 Cyber-Physical Systems	526
	2.2.2 Fast Mobile Internet Infrastructure	527
	2.2.3 Automatic Model Generation in Simulation Software	528
	3 Data Analytics Enable Cyber Physical Production Control	529
	3.1 Data Quality as an Enabler of Cyber Physical Production Control	532
	3.2 Descriptive Analytics	534
	3.3 Diagnostic Analytics	535
	3.4 Predictive Analytics	537
	3.5 Prescriptive Analytics	538
	3.6 Adjusting Production	539
	4 Summary and Outlook	539
	Acknowledgments	540
	References	540
	22 A Versatile and Scalable Production Planning and Control System for Small Batch Series	544
	1 Introduction	544
	1.1 Industry 4.0 Based on Cyber-Physical Systems	546
	1.2 Smart Factory Versus Smart Logistics	547
	1.3 Area of Conflict: Deterministic Planning Versus Decentralized Control	548
	1.4 Overview of Structure	549
	2 Conceptual Approach	549
	3 Exploiting Flexibility Potentials	552
	4 Production Planning	555
	5 Production Control	557
	6 Conclusion and Outlook	559
	Acknowledgments	560
	References	560
	Evolution of Workforce and Human-Machine Interaction	563
	23 CPS and the Worker: Reorientation and Requalification?	564
	1 Enter the Process Worker	564
	2 The Opening of the Lights-Out Factory	565
	3 What Skills Now?	566
	4 The (Temporary?) Return of the Gods	567
	5 The New Factory: Connecting the Dots	568
	6 Worker and Management: Converging Roles?	570
	7 Conclusion: Moving up the Ladder	571
	References	573
	24 Towards User-Driven Cyber-Physical Systems—Strategies to Support User Intervention in Provisioning of Information and Capabilities of Cyber-Physical Systems	576
	1 Introduction	576
	2 Background	578
	2.1 Cyber-Physical Systems	578
	2.2 Solutions for User Intervention in Cyber-Physical Systems	579
	2.3 Architectures for Cyber-Physical Systems	580
	2.4 Requirements of User Intervention for Cyber-Physical System Architecture	581
	3 Example—A User-Driven Cyber Physical Production System	581
	4 Strategies for User-Driven Cyber-Physical Systems	584
	4.1 Strategies for User Intervention in Behaviour of CPS	585
	4.2 Strategies for User Intervention in Behaviour of Supporting Services	586
	5 Architecture for a User-Driven Cyber-Physical System	588
	5.1 End User Viewpoint	588
	5.2 CPS Developer Viewpoint	590
	6 Discussion	591
	7 Conclusions	593
	References	593
	25 Competence Management in the Age of Cyber Physical Systems	595
	1 Companies in the Age of Industrie 4.0	595
	2 Cyber Physical Systems	597
	3 Competencies and Competence Management	599
	3.1 Defining Individual and Organizational Competencies	599
	3.2 Classification and Measuring of Competencies	601
	3.2.1 Competence Classification	601
	3.2.2 Competence Measurement	602
	4 Consequences and New Competence Requirements for Employees Through CPS Complexity	604
	5 Development of a Measurement Instrument for Competencies in the Age of CPS	608
	6 Conclusions	610
	References	610
	Adjacent Fields and Ecosystems	615
	26 Cyber-Physical Systems for Agricultural and Construction Machinery—Current Applications and Future Potential	616
	1 Introduction	616
	2 Challenges	617
	2.1 Challenges in Agricultural Machinery	618
	2.2 Challenges in Construction Machinery	619
	3 CPS for Mobile Machines	619
	4 Data	621
	5 Key Technologies	623
	6 Key Algorithms	625
	7 Exemplary Processes	628
	7.1 Construction Process	628
	7.1.1 Conclusion CPS in Construction Machinery and Future Potential	632
	7.2 Agricultural Process	633
	7.2.1 Conclusion CPS in Agricultural Machinery and Future Potential	636
	8 Conclusion	637
	References	638
	27 Application of CPS Within Wind Energy—Current Implementation and Future Potential	645
	1 Motivation	645
	2 Potential in Wind Energy	648
	2.1 Wind Turbine	649
	2.2 Wind Farm	649
	2.3 Grid and Local Smart Grid	650
	2.4 Community Interests	651
	3 CPS in Wind Energy	651
	3.1 Current Applications of CPS in Wind Energy	652
	3.1.1 Condition Monitoring System	652
	3.1.2 Supervisory Control and Data Acquisition	653
	3.1.3 Smart Grid and the Integration of Wind Energy	654
	3.1.4 Interaction of Wind Energy and Storage Systems	655
	3.2 Future CPS in Wind Energy	657
	3.2.1 Wind Turbine/Farm	657
	3.2.2 Wind Data	659
	3.2.3 Supply Predictor	660
	3.2.4 Operation and Condition Analyzer	660
	3.2.5 Demand Predictor/Power Requirement	661
	3.2.6 Storage	661
	3.2.7 Electricity Exchange	662
	3.2.8 Community Interests	663
	3.2.9 Central Analyzer and Optimizer	663
	3.2.10 Operation and Maintenance Controller	664
	3.2.11 Energy Controller	665
	4 Outlook and Conclusions	666
	References	666
	28 Transfer Printing for Cyber-Manufacturing Systems	669
	1 Introduction	669
	2 Fundamentals of Transfer Printing	670
	2.1 Basic Concepts in Transfer Printing	671
	2.2 Advanced Transfer Printing Techniques	673
	3 Opportunities of Transfer Printing for Cyber-Manufacturing Systems	675
	3.1 Stretchable Electronics	676
	3.2 Dissolvable Electronics	677
	3.3 Opportunities of Transfer Printing Enabled Devices for Cyber-Manufacturing Systems	679
	4 Challenges of Transfer Printing for Cyber-Manufacturing Systems	680
	5 Future Scope	683
	Acknowledgments	684
	References	684
	29 Advanced Manufacturing Innovation Ecosystems: The Case of Massachusetts	689
	1 Introduction	689
	2 Definition of Key Terms	691
	3 Trends in Advanced Manufacturing	693
	4 The Competitive Position of Manufacturing in Massachusetts	694
	5 The Massachusetts Manufacturing Innovation Ecosystem	697
	5.1 OEMs Within the Manufacturing Innovation Ecosystem	699
	5.2 SMEs Within the Manufacturing Innovation Ecosystem	701
	5.3 Universities in the Manufacturing Innovation Ecosystem	703
	5.4 Startups in the Manufacturing Innovation Ecosystem	705
	6 Manufacturing Intermediaries	707
	7 Recommendations to Improve the Innovation Ecosystem	708
	7.1 Advanced Manufacturing Strategy and Agenda	708
	7.2 Collaboration with OEMs to Drive Innovation and Upgrade SME Capabilities	709
	7.3 Technological and Managerial Support for Innovation in SMEs	709
	7.4 Connections Between Startups and the Innovation Ecosystem	710
	8 Conclusion	711
	References	712
	30 Erratum to: Industrial Internet of Things	714
	Erratum to:	714