Combining different perspectives from materials science, engineering, and computer science, this reference provides a unified view of the various aspects necessary for the successful realization of intelligent systems. The editors and authors are from academia and research institutions with close ties to industry, and are thus able to offer first-hand information here. They adopt a unique, three-tiered approach such that readers can gain basic, intermediate, and advanced topical knowledge. The technology section of the book is divided into chapters covering the basics of sensor integration in materials, the challenges associated with this approach, data processing, evaluation, and validation, as well as methods for achieving an autonomous energy supply. The applications part then goes on to showcase typical scenarios where material-integrated intelligent systems are already in use, such as for structural health monitoring and smart textiles.

Table of Contents:
Foreword XV Preface XIX Part One Introduction 1 1 On Concepts and Challenges of Realizing Material-Integrated Intelligent Systems 3 Stefan Bosse and Dirk Lehmhus 1.1 Introduction 3 1.2 System Development Methodologies and Tools (Part Two) 7 1.3 Sensor Technologies and Material Integration (Part Three and Four) 8 1.4 Signal and Data Processing (Part Five) 15 1.5 Networking and Communication (Part Six) 17 1.6 Energy Supply and Management (Part Seven) 21 1.7 Applications (Part Eight) 21 References 24 Part Two System Development 29 2 Design Methodology for Intelligent Technical Systems 31 Mareen Vaßholz, Roman Dumitrescu, and Jürgen Gausemeier 2.1 From Mechatronics to Intelligent Technical Systems 32 2.2 Self-Optimizing Systems 36 2.3 Design Methodology for Intelligent Technical Systems 38 2.3.1 Domain-Spanning Conceptual Design 41 2.3.2 Domain-Specific Conceptual Design 50 References 51 3 Smart Systems Design Methodologies and Tools 55 Nicola Bombieri, Franco Fummi, Giuliana Gangemi, Michelangelo Grosso, Enrico Macii, Massimo Poncino, and Salvatore Rinaudo 3.1 Introduction 55 3.2 Smart Electronic Systems and Their Design Challenges 56 3.3 The Smart Systems Codesign before SMAC 57 3.4 The SMAC Platform 60 3.4.1 The Platform Overview 61 3.4.1.1 System C–SystemVue Cosimulation 61 3.4.1.2 ADS and the Thermal Simulation 63 3.4.1.3 EMPro Extension and ADS Integration 64 3.4.1.4 Automated EM – Circuit Cosimulation in ADS 64 3.4.1.5 HIF Suite Toolsuite 65 3.4.1.6 The MEMS+ Platform 66 3.4.2 The (Co)Simulation Levels and the Design–Domains Matrix 67 3.5 Case Study: A Sensor Node for Drift-Free Limb Tracking 69 3.5.1 System Architecture 71 3.5.2 Model Development and System-Level Simulation 71 3.5.3 Results 73 3.6 Conclusions 76 Acknowledgments 77 References 77 Part Three Sensor Technologies 81 4 Microelectromechanical Systems (MEMS) 83 Li Yunjia 4.1 Introduction 83 4.1.1 What Is MEMS 83 4.1.2 Why MEMS 84 4.1.3 MEMS Sensors 84 4.1.4 Goal of This Chapter 85 4.2 Materials 85 4.2.1 Silicon 85 4.2.2 Dielectrics 86 4.2.3 Metals 87 4.3 Microfabrication Technologies 87 4.3.1 Silicon Wafers 87 4.3.2 Lithography 88 4.3.3 Etching 91 4.3.4 Deposition Techniques 93 4.3.5 Other Processes 94 4.3.6 Surface and Bulk Micromachining 95 4.4 MEMS Sensor 95 4.4.1 Resistive Sensors 95 4.4.2 Capacitive Sensors 99 4.5 Sensor Systems 103 References 104 5 Fiber-Optic Sensors 107 Yi Yang, Kevin Chen, and Nikhil Gupta 5.1 Introduction to Fiber-Optic Sensors 107 5.1.1 Sensing Principles 108 5.1.2 Types of Optical Fibers 108 5.2 Trends in Sensor Fabrication and Miniaturization 110 5.3 Fiber-Optic Sensors for Structural Health Monitoring 112 5.3.1 Sensors for Cure Monitoring of Composites 114 5.3.2 Embedded FOS in Composite Materials 114 5.3.3 Surface-Mounted FOS in Composite Materials 115 5.3.4 FOS for Structural Monitoring 115 5.3.4.1 Aerospace Structures 115 5.3.4.2 Civil Structures 116 5.3.4.3 Marine Structures 116 5.4 Frequency Modulation Sensors 117 5.4.1 Bragg Grating Sensors 117 5.4.2 Fabry–Pérot Interferometer Sensor 118 5.4.3 Whispering Gallery Mode Sensors 119 5.5 Intensity Modulation Sensors 122 5.5.1 Fiber Microbend Sensors 122 5.5.2 Fiber-Optic Loop Sensor 123 5.6 Some Challenges in SHM of Composite Materials 128 5.7 Summary 128 Acknowledgments 129 References 129 6 Electronics Development for Integration 137 Jan Vanfleteren 6.1 Introduction 137 6.1.1 Standard Flat Rigid Printed Circuits Boards and Components Assembly 137 6.1.2 Flexible Circuits 138 6.1.3 Need for Alternative Circuit and Packaging Materials 140 6.2 Chip Package Miniaturization Technologies 140 6.2.1 Ultrathin Chip Package Technology 140 6.2.2 UTCP Circuit Integration 142 6.2.2.1 UTCP Embedding 142 6.2.2.2 UTCP Stacking 143 6.2.3 Applications 143 6.3 Elastic Circuits 145 6.3.1 Printed Circuit Board-Based Elastic Circuits 145 6.3.2 Thin Film Metal-Based Elastic Circuits 148 6.3.3 Applications 148 6.3.3.1 Wearable Light Therapy 148 6.3.4 Stretchable Displays 149 6.4 2.5D Rigid Thermoplastic Circuits 152 6.5 Large Area Textile-Based Circuits 153 6.5.1 Electronic Module Integration Technology 154 6.5.2 Applications 155 6.6 Conclusions and Outlook 157 References 157 Part Four Material Integration Solutions 159 7 Sensor Integration in Fiber-Reinforced Polymers 161 Maryam Kahali Moghaddam, Mariugenia Salas, Michael Koerdt, Christian Brauner, Martina Hübner, Dirk Lehmhus, and Walter Lang 7.1 Introduction to Fiber-Reinforced Polymers 161 7.2 Applications of Integrated Systems in Composites 164 7.2.1 Production Process Monitoring and Quality Control of Composites 164 7.2.1.1 Monitoring of the Resin Flow 166 7.2.1.2 Analytical Modeling of Resin Front by Means of Simulation 166 7.2.1.3 Monitoring the Resin Curing 166 7.2.2 In-Service Applications of Integrated Systems 167 7.2.2.1 Use for Structural Health Monitoring (SHM) 167 7.2.2.2 Use As Support to Nondestructive Evaluation and Testing (NDE/NDT) 170 7.3 Fiber-Reinforced Polymer Production and Sensor Integration Processes 170 7.3.1 Overview of Fiber-Reinforced Polymer Production Processes 170 7.3.2 Sensor Integration in Fiber-Reinforced Polymers: Selected Case Studies 175 7.4 Electronics Integration and Data Processing 179 7.4.1 Materials Integration of Electronics 180 7.4.2 Electronics for Wireless Sensing 181 7.5 Examples of Sensors Integrated in Fiber-Reinforced Polymer Composites 183 7.5.1 Ultrasound Reflection Sensing 183 7.5.2 Pressure Sensors 184 7.5.3 Thermocouples 186 7.5.4 Fiber Optic Sensors 187 7.5.5 Interdigital Planar Capacitive Sensors 188 7.6 Conclusion 192 Acknowledgments 193 References 193 8 Integration in Sheet Metal Structures 201 Welf-Guntram Drossel, Roland Müller, Matthias Nestler, and Sebastian Hensel 8.1 Introduction 201 8.2 Integration Technology 204 8.3 Forming of Piezometal Compounds 205 8.4 Characterization of Functionality 208 8.5 Fields of Application 211 8.6 Conclusion and Outlook 212 References 212 9 Sensor and Electronics Integration in Additive Manufacturing 217 Dirk Lehmhus and Matthias Busse 9.1 Introduction to Additive Manufacturing 217 9.2 Overview of AM Processes 224 9.3 Links between Sensor Integration and Additive Manufacturing 228 9.4 AM Sensor Integration Case Studies 230 9.4.1 Cavity-Based Sensor and Electronic System Integration 236 9.4.2 Multiprocess Hybrid Manufacturing Systems 239 9.4.3 Toward a Single AM Platform for Structural Electronics Fabrication 243 9.5 Conclusion and Outlook 245 Abbreviations 246 References 248 Part Five Signal and Data Processing: The Sensor Node Level 257 10 Analog Sensor Signal Processing and Analog-to-Digital Conversion 259 John Horstmann, Marco Ramsbeck, and Stefan Bosse 10.1 Operational Amplifiers 260 10.2 Analog-to-Digital Converter Specifications 262 10.3 Data Converter Architectures 268 10.4 Low-Power ADC Designs and Power Classification 276 10.5 Moving Window ADC Approach 277 References 279 11 Digital Real-Time Data Processing with Embedded Systems 281 Stefan Bosse and Dirk Lehmhus 11.1 Levels of Information 281 11.2 Algorithms and Computational Models 283 11.3 Scientific Data Mining 287 11.4 Real-Time and Parallel Processing 291 References 297 12 The Known World: Model-Based Computing and Inverse Numeric 301 Armin Lechleiter and Stefan Bosse 12.1 Physical Models in Parameter Identification 302 12.2 Noisy Data Due to Sensor and Modeling Errors 304 12.3 Coping with Noisy Data: Tikhonov Regularization and Parameter Choice Rules 306 12.4 Tikhonov Regularization 308 12.5 Rules for the Choice of the Regularization Parameter 309 12.6 Explicit Minimizers for Linear Models 311 12.7 The Soft-Shrinkage Iteration 312 12.8 Iterative Regularization Schemes 313 12.9 Gradient Descent Schemes 314 12.10 Newton-Type Regularization Schemes 317 12.11 Numerical Examples in Load Reconstruction 318 References 326 13 The Unknown World: Model-Free Computing and Machine Learning 329 Stefan Bosse 13.1 Machine Learning – An Overview 329 13.2 Learning of Data Streams 331 13.3 Learning with Noise 333 13.4 Distributed Event-Based Learning 333 13.5 ε-Interval and Nearest-Neighborhood Decision Tree Learning 334 13.6 Machine Learning – A Sensorial Material Demonstrator 336 References 340 14 Robustness and Data Fusion 343 Stefan Bosse 14.1 Robust System Design on System Level 345 References 348 Part Six Networking and Communication: The Sensor Network Level 349 15 Communication Hardware 351 Tim Tiedemann 15.1 Communication Hardware in Their Applications 351 15.2 Requirements for Embedded Communication Hardware 352 15.3 Overview of Physical Communication Classes 354 15.4 Examples of Wired Communication Hardware 356 15.5 Examples of Wireless Communication Hardware 358 15.6 Examples of Optical Communication Hardware 360 15.7 Summary 360 References 361 16 Networks and Communication Protocols 363 Stefan Bosse 16.1 Network Topologies and Network of Networks 364 16.2 Redundancy in Networks 365 16.3 Protocols 366 16.4 Switched Networks versus Message Passing 368 16.5 Bus Systems 369 16.6 Message Passing and Message Formats 370 16.7 Routing 370 16.8 Failures, Robustness, and Reliability 377 16.9 Distributed Sensor Networks 378 16.10 Active Messaging and Agents 381 References 382 17 Distributed and Cloud Computing: The Big Machine 385 Stefan Bosse 17.1 Reference 386 18 The Mobile Agent and Multiagent Systems 387 Stefan Bosse 18.1 The Agent Computation and Interaction Model 389 18.2 Dynamic Activity-Transition Graphs 394 18.3 The Agent Behavior Class 395 18.4 Communication and Interaction of Agents 396 18.5 Agent Programming Models 397 18.6 Agent Processing Platforms and Technologies 404 18.7 Agent-Based Learning 415 18.8 Event and Distributed Agent-Based Learning of Noisy Sensor Data 416 References 420 Part Seven Energy Supply 423 19 Energy Management and Distribution 425 Stefan Bosse 19.1 Design of Low-Power Smart Sensor Systems 426 19.2 A Toolbox for Energy Analysis and Simulation 430 19.3 Dynamic Power Management 434 19.3.1 CPU-Centric DPM 435 19.3.2 I/O-Centric DPM 437 19.3.3 EDS Algorithm 438 19.4 Energy-Aware Communication in Sensor Networks 440 19.5 Energy Distribution in Sensor Networks 442 19.5.1 Distributed Energy Management in Sensor Networks Using Agents 443 References 446 20 Microenergy Storage 449 Robert Kun, Chi Chen, and Francesco Ciucci 20.1 Introduction 449 20.2 Energy Harvesting/Scavenging 451 20.3 Energy Storage 452 20.3.1 Capacitors 452 20.3.2 Batteries 458 20.3.3 Fuel Cells 467 20.3.3.1 Low-Temperature Fuel Cells 469 20.3.3.2 High-Temperature Fuel Cells 469 20.3.4 Other Storage Systems 469 20.4 Summary and Perspectives 470 References 470 21 Energy Harvesting 479 Rolanas Dauksevicius and Danick Briand 21.1 Introduction 479 21.2 Mechanical Energy Harvesters 480 21.2.1 Piezoelectric Micropower Generators 482 21.2.2 Micropower Generators Based on Electroactive Polymers 489 21.2.3 Electrostatic Micropower Generators 490 21.2.4 Electromagnetic Micropower Generators 491 21.2.5 Triboelectric Nanogenerators 492 21.2.6 Hybrid Micropower Generators 493 21.2.7 Wideband and Nonlinear Micropower Generators 494 21.2.8 Concluding Remarks 495 21.3 Thermal Energy Harvesters 496 21.3.1 Introduction to Thermoelectric Generators 496 21.3.2 Thermoelectric Materials and Efficiency 499 21.3.3 Other Thermal-to-Electrical Energy Conversion Techniques 501 21.4 Radiation Harvesters 502 21.4.1 Light Energy Harvesters 502 21.4.2 RF Energy Harvesters 506 21.5 Summary and Perspectives 507 References 512 Part Eight Application Scenarios 529 22 Structural Health Monitoring (SHM) 531 Dirk Lehmhus and Matthias Busse 22.1 Introduction 531 22.2 Motivations for SHM System Implementation 536 22.3 SHM System Classification and Main Components 540 22.3.1 Sensor and Actuator Elements for SHM Systems 542 22.3.2 Communication in SHM Systems 550 22.3.3 SHM Data Evaluation Approaches and Principles 552 22.4 SHM Areas and Application and Case Studies 555 22.5 Implications of Material Integration for SHM Systems 561 22.6 Conclusion and Outlook 562 References 564 23 Achievements and Open Issues Toward Embedding Tactile Sensing and Interpretation into Electronic Skin Systems 571 Ali Ibrahim, Luigi Pinna, Lucia Seminara, and Maurizio Valle 23.1 Introduction 571 23.2 The Skin Mechanical Structure 573 23.2.1 Transducers and Materials 573 23.2.2 An Example of Skin Integration into an Existing Robotic Platform 575 23.3 Tactile Information Processing 579 23.4 Computational Requirements 582 23.4.1 Electrical Impedance Tomography 582 23.4.2 Tensorial Kernel 583 23.5 Conclusions 585 References 585 24 Intelligent Materials in Machine Tool Applications: A Review 595 Hans-Christian Möhring 24.1 Applications of Shape Memory Alloys (SMA) 596 24.2 Applications of Piezoelectric Ceramics 596 24.3 Applications of Magnetostrictive Materials 598 24.4 Applications of Electro- and Magnetorheological Fluids 600 24.5 Intelligent Structures and Components 601 24.6 Summary and Conclusion 603 References 604 25 New Markets/Opportunities through Availability of Product Life Cycle Data 613 Thorsten Wuest, Karl Hribernik, and Klaus-Dieter Thoben 25.1 Product Life Cycle Management 613 25.1.1 Closed-Loop and Item-Level PLM 615 25.1.2 Data and Information in PLM 615 25.1.3 Supporting Concepts for Data and Information Integration in PLM 616 25.2 Case Studies 617 25.2.1 Case Study 1: Life Cycle of Leisure Boats 617 25.2.1.1 Sensors Used 618 25.2.1.2 Potential Application of Sensorial Materials 619 25.2.1.3 Limitations and Opportunities of Sensorial Materials 619 25.2.2 Case Study 2: PROMISE – Product Life Cycle Management and Information Using Smart Embedded Systems 620 25.2.2.1 Sensors Used 620 25.2.2.2 Potential Application of Sensorial Materials 621 25.2.2.3 Limitations and Opportunities of Sensorial Materials 621 25.2.3 Case Study 3: Composite Bridge 622 25.2.3.1 Sensors Used 623 25.2.3.2 Potential Application of Sensorial Materials 623 25.2.3.3 Limitations and Opportunities of Sensorial Materials 623 25.3 Potential of Sensorial Materials in PLM Application 623 Acknowledgment 624 References 624 26 Human–Computer Interaction with Novel and Advanced Materials 629 Tanja Döring, Robert Porzel, and Rainer Malaka 26.1 Introduction 629 26.2 New Forms of Human–Computer Interaction 630 26.3 Applications and Scenarios 633 26.3.1 Domestic and Personal Devices 633 26.3.1.1 The Marble Answering Machine 633 26.3.1.2 Living Wall: An Interactive Wallpaper 634 26.3.1.3 Sprout I/O and Shutters: Ambient Textile Information Displays 634 26.3.1.4 FlexCase: A Flexible Sensing and Display Cover 635 26.3.2 Learning, Collaboration, and Entertainment 635 26.3.2.1 Tangibles for Learning and Creativity 635 26.3.2.2 inFORM: Supporting Remote Collaboration through Shape Capture and Actuation 636 26.3.2.3 The Soap Bubble Interface 637 26.4 Opportunities and Challenges 637 26.5 Conclusions 639 References 639 Index 645