Material-Integrated Intelligent Systems. Technology and Applications

  • ID: 4226323
  • Book
  • 696 Pages
  • John Wiley and Sons Ltd
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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.

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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 –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

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Stefan Bosse studied physics at the University of Bremen, Germany, from which he also received his PhD. Since 2008 he is actively involved in different projects in the University of Bremen′s Scientific Center ISIS (Integrated Solutions in Sensorial Structure Engineering) pushing interdisciplinary research, and recently joined the ISIS council.

Dirk Lehmhus joined the Fraunhofer Institute for Manufacturing Technology and Advanced Materials (IFAM) in Bremen, Germany, in 1998 and subsequently obtained a PhD in production technology from Bremen University for optimization studies of aluminium foam production processes and properties. Since May 2009 he is Managing Director at the University of Bremen′s Scientific Centre ISIS dedicated to the development of sensorial materials and sensor–equipped structures.

Walter Lang joined the Fraunhofer Institute for Solid State Technology (EMFT) in Munich, Germany, in 1987 where he worked on microsystems technology. In 1995, he became Head of the Sensors Department in the Institute of Micromachining and Information Technology of the Hahn Schickard Society. In 2003, he joined the University of Bremen where he is currently heading the Institute for Microsensors, –actuators and –systems at the Microsystems Center Bremen.

Matthias Busse holds the chair for near net–shape manufacturing technology in the Faculty of Production Engineering at the University of Bremen since 2003. At the same time, he became Director of the Fraunhofer IFAM. After his PhD in mechanical engineering he worked in various positions at Volkswagen Central Research, ultimately as Head of Production Research. Matthias Busse represents the University of Bremen′s Scientific Centre ISIS as speaker of the board of directors.
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