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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• Foreword XVPreface XIXPart One Introduction 11 On Concepts and Challenges of Realizing Material-Integrated Intelligent Systems 3Stefan Bosse and Dirk Lehmhus1.1 Introduction 31.2 System Development Methodologies and Tools (Part Two) 71.3 Sensor Technologies and Material Integration (Part Three and Four) 81.4 Signal and Data Processing (Part Five) 151.5 Networking and Communication (Part Six) 171.6 Energy Supply and Management (Part Seven) 211.7 Applications (Part Eight) 21References 24Part Two System Development 292 Design Methodology for Intelligent Technical Systems 31Mareen Vaßholz, Roman Dumitrescu, and Jürgen Gausemeier2.1 From Mechatronics to Intelligent Technical Systems 322.2 Self-Optimizing Systems 362.3 Design Methodology for Intelligent Technical Systems 382.3.1 Domain-Spanning Conceptual Design 412.3.2 Domain-Specific Conceptual Design 50References 513 Smart Systems Design Methodologies and Tools 55Nicola Bombieri, Franco Fummi, Giuliana Gangemi, Michelangelo Grosso,Enrico Macii, Massimo Poncino, and Salvatore Rinaudo3.1 Introduction 553.2 Smart Electronic Systems and Their Design Challenges 563.3 The Smart Systems Codesign before SMAC 573.4 The SMAC Platform 603.4.1 The Platform Overview 613.4.1.1 System C–SystemVue Cosimulation 613.4.1.2 ADS and the Thermal Simulation 633.4.1.3 EMPro Extension and ADS Integration 643.4.1.4 Automated EM – Circuit Cosimulation in ADS 643.4.1.5 HIF Suite Toolsuite 653.4.1.6 The MEMS+ Platform 663.4.2 The (Co)Simulation Levels and the Design–Domains Matrix 673.5 Case Study: A Sensor Node for Drift-Free Limb Tracking 693.5.1 System Architecture 713.5.2 Model Development and System-Level Simulation 713.5.3 Results 733.6 Conclusions 76Acknowledgments 77References 77Part Three Sensor Technologies 814 Microelectromechanical Systems (MEMS) 83Li Yunjia4.1 Introduction 834.1.1 What Is MEMS 834.1.2 Why MEMS 844.1.3 MEMS Sensors 844.1.4 Goal of This Chapter 854.2 Materials 854.2.1 Silicon 854.2.2 Dielectrics 864.2.3 Metals 874.3 Microfabrication Technologies 874.3.1 Silicon Wafers 874.3.2 Lithography 884.3.3 Etching 914.3.4 Deposition Techniques 934.3.5 Other Processes 944.3.6 Surface and Bulk Micromachining 954.4 MEMS Sensor 954.4.1 Resistive Sensors 954.4.2 Capacitive Sensors 994.5 Sensor Systems 103References 1045 Fiber-Optic Sensors 107Yi Yang, Kevin Chen, and Nikhil Gupta5.1 Introduction to Fiber-Optic Sensors 1075.1.1 Sensing Principles 1085.1.2 Types of Optical Fibers 1085.2 Trends in Sensor Fabrication and Miniaturization 1105.3 Fiber-Optic Sensors for Structural Health Monitoring 1125.3.1 Sensors for Cure Monitoring of Composites 1145.3.2 Embedded FOS in Composite Materials 1145.3.3 Surface-Mounted FOS in Composite Materials 1155.3.4 FOS for Structural Monitoring 1155.3.4.1 Aerospace Structures 1155.3.4.2 Civil Structures 1165.3.4.3 Marine Structures 1165.4 Frequency Modulation Sensors 1175.4.1 Bragg Grating Sensors 1175.4.2 Fabry–Pérot Interferometer Sensor 1185.4.3 Whispering Gallery Mode Sensors 1195.5 Intensity Modulation Sensors 1225.5.1 Fiber Microbend Sensors 1225.5.2 Fiber-Optic Loop Sensor 1235.6 Some Challenges in SHM of Composite Materials 1285.7 Summary 128Acknowledgments 129References 1296 Electronics Development for Integration 137Jan Vanfleteren6.1 Introduction 1376.1.1 Standard Flat Rigid Printed Circuits Boards and Components Assembly 1376.1.2 Flexible Circuits 1386.1.3 Need for Alternative Circuit and Packaging Materials 1406.2 Chip Package Miniaturization Technologies 1406.2.1 Ultrathin Chip Package Technology 1406.2.2 UTCP Circuit Integration 1426.2.2.1 UTCP Embedding 1426.2.2.2 UTCP Stacking 1436.2.3 Applications 1436.3 Elastic Circuits 1456.3.1 Printed Circuit Board-Based Elastic Circuits 1456.3.2 Thin Film Metal-Based Elastic Circuits 1486.3.3 Applications 1486.3.3.1 Wearable Light Therapy 1486.3.4 Stretchable Displays 1496.4 2.5D Rigid Thermoplastic Circuits 1526.5 Large Area Textile-Based Circuits 1536.5.1 Electronic Module Integration Technology 1546.5.2 Applications 1556.6 Conclusions and Outlook 157References 157Part Four Material Integration Solutions 1597 Sensor Integration in Fiber-Reinforced Polymers 161Maryam Kahali Moghaddam, Mariugenia Salas, Michael Koerdt, Christian Brauner, Martina Hübner, Dirk Lehmhus, and Walter Lang7.1 Introduction to Fiber-Reinforced Polymers 1617.2 Applications of Integrated Systems in Composites 1647.2.1 Production Process Monitoring and Quality Control of Composites 1647.2.1.1 Monitoring of the Resin Flow 1667.2.1.2 Analytical Modeling of Resin Front by Means of Simulation 1667.2.1.3 Monitoring the Resin Curing 1667.2.2 In-Service Applications of Integrated Systems 1677.2.2.1 Use for Structural Health Monitoring (SHM) 1677.2.2.2 Use As Support to Nondestructive Evaluation and Testing (NDE/NDT) 1707.3 Fiber-Reinforced Polymer Production and Sensor Integration Processes 1707.3.1 Overview of Fiber-Reinforced Polymer Production Processes 1707.3.2 Sensor Integration in Fiber-Reinforced Polymers: Selected Case Studies 1757.4 Electronics Integration and Data Processing 1797.4.1 Materials Integration of Electronics 1807.4.2 Electronics for Wireless Sensing 1817.5 Examples of Sensors Integrated in Fiber-Reinforced Polymer Composites 1837.5.1 Ultrasound Reflection Sensing 1837.5.2 Pressure Sensors 1847.5.3 Thermocouples 1867.5.4 Fiber Optic Sensors 1877.5.5 Interdigital Planar Capacitive Sensors 1887.6 Conclusion 192Acknowledgments 193References 1938 Integration in Sheet Metal Structures 201Welf-Guntram Drossel, Roland Müller, Matthias Nestler, and Sebastian Hensel8.1 Introduction 2018.2 Integration Technology 2048.3 Forming of Piezometal Compounds 2058.4 Characterization of Functionality 2088.5 Fields of Application 2118.6 Conclusion and Outlook 212References 2129 Sensor and Electronics Integration in Additive Manufacturing 217Dirk Lehmhus and Matthias Busse9.1 Introduction to Additive Manufacturing 2179.2 Overview of AM Processes 2249.3 Links between Sensor Integration and Additive Manufacturing 2289.4 AM Sensor Integration Case Studies 2309.4.1 Cavity-Based Sensor and Electronic System Integration 2369.4.2 Multiprocess Hybrid Manufacturing Systems 2399.4.3 Toward a Single AM Platform for Structural Electronics Fabrication 2439.5 Conclusion and Outlook 245Abbreviations 246References 248Part Five Signal and Data Processing: The Sensor Node Level 25710 Analog Sensor Signal Processing and Analog-to-Digital Conversion 259John Horstmann, Marco Ramsbeck, and Stefan Bosse10.1 Operational Amplifiers 26010.2 Analog-to-Digital Converter Specifications 26210.3 Data Converter Architectures 26810.4 Low-Power ADC Designs and Power Classification 27610.5 Moving Window ADC Approach 277References 27911 Digital Real-Time Data Processing with Embedded Systems 281Stefan Bosse and Dirk Lehmhus11.1 Levels of Information 28111.2 Algorithms and Computational Models 28311.3 Scientific Data Mining 28711.4 Real-Time and Parallel Processing 291References 29712 The Known World: Model-Based Computing and Inverse Numeric 301Armin Lechleiter and Stefan Bosse12.1 Physical Models in Parameter Identification 30212.2 Noisy Data Due to Sensor and Modeling Errors 30412.3 Coping with Noisy Data: Tikhonov Regularization and Parameter Choice Rules 30612.4 Tikhonov Regularization 30812.5 Rules for the Choice of the Regularization Parameter 30912.6 Explicit Minimizers for Linear Models 31112.7 The Soft-Shrinkage Iteration 31212.8 Iterative Regularization Schemes 31312.9 Gradient Descent Schemes 31412.10 Newton-Type Regularization Schemes 31712.11 Numerical Examples in Load Reconstruction 318References 32613 The Unknown World: Model-Free Computing and Machine Learning 329Stefan Bosse13.1 Machine Learning – An Overview 32913.2 Learning of Data Streams 33113.3 Learning with Noise 33313.4 Distributed Event-Based Learning 33313.5 ε-Interval and Nearest-Neighborhood Decision Tree Learning 33413.6 Machine Learning – A Sensorial Material Demonstrator 336References 34014 Robustness and Data Fusion 343Stefan Bosse14.1 Robust System Design on System Level 345References 348Part Six Networking and Communication: The Sensor Network Level 34915 Communication Hardware 351Tim Tiedemann15.1 Communication Hardware in Their Applications 35115.2 Requirements for Embedded Communication Hardware 35215.3 Overview of Physical Communication Classes 35415.4 Examples of Wired Communication Hardware 35615.5 Examples of Wireless Communication Hardware 35815.6 Examples of Optical Communication Hardware 36015.7 Summary 360References 36116 Networks and Communication Protocols 363Stefan Bosse16.1 Network Topologies and Network of Networks 36416.2 Redundancy in Networks 36516.3 Protocols 36616.4 Switched Networks versus Message Passing 36816.5 Bus Systems 36916.6 Message Passing and Message Formats 37016.7 Routing 37016.8 Failures, Robustness, and Reliability 37716.9 Distributed Sensor Networks 37816.10 Active Messaging and Agents 381References 38217 Distributed and Cloud Computing: The Big Machine 385Stefan Bosse17.1 Reference 38618 The Mobile Agent and Multiagent Systems 387Stefan Bosse18.1 The Agent Computation and Interaction Model 38918.2 Dynamic Activity-Transition Graphs 39418.3 The Agent Behavior Class 39518.4 Communication and Interaction of Agents 39618.5 Agent Programming Models 39718.6 Agent Processing Platforms and Technologies 40418.7 Agent-Based Learning 41518.8 Event and Distributed Agent-Based Learning ofNoisy Sensor Data 416References 420Part Seven Energy Supply 42319 Energy Management and Distribution 425Stefan Bosse19.1 Design of Low-Power Smart Sensor Systems 42619.2 A Toolbox for Energy Analysis and Simulation 43019.3 Dynamic Power Management 43419.3.1 CPU-Centric DPM 43519.3.2 I/O-Centric DPM 43719.3.3 EDS Algorithm 43819.4 Energy-Aware Communication in Sensor Networks 44019.5 Energy Distribution in Sensor Networks 44219.5.1 Distributed Energy Management in Sensor NetworksUsing Agents 443References 44620 Microenergy Storage 449Robert Kun, Chi Chen, and Francesco Ciucci20.1 Introduction 44920.2 Energy Harvesting/Scavenging 45120.3 Energy Storage 45220.3.1 Capacitors 45220.3.2 Batteries 45820.3.3 Fuel Cells 46720.3.3.1 Low-Temperature Fuel Cells 46920.3.3.2 High-Temperature Fuel Cells 46920.3.4 Other Storage Systems 46920.4 Summary and Perspectives 470References 47021 Energy Harvesting 479Rolanas Dauksevicius and Danick Briand21.1 Introduction 47921.2 Mechanical Energy Harvesters 48021.2.1 Piezoelectric Micropower Generators 48221.2.2 Micropower Generators Based on Electroactive Polymers 48921.2.3 Electrostatic Micropower Generators 49021.2.4 Electromagnetic Micropower Generators 49121.2.5 Triboelectric Nanogenerators 49221.2.6 Hybrid Micropower Generators 49321.2.7 Wideband and Nonlinear Micropower Generators 49421.2.8 Concluding Remarks 49521.3 Thermal Energy Harvesters 49621.3.1 Introduction to Thermoelectric Generators 49621.3.2 Thermoelectric Materials and Efficiency 49921.3.3 Other Thermal-to-Electrical Energy ConversionTechniques 50121.4 Radiation Harvesters 50221.4.1 Light Energy Harvesters 50221.4.2 RF Energy Harvesters 50621.5 Summary and Perspectives 507References 512Part Eight Application Scenarios 52922 Structural Health Monitoring (SHM) 531Dirk Lehmhus and Matthias Busse22.1 Introduction 53122.2 Motivations for SHM System Implementation 53622.3 SHM System Classification and Main Components 54022.3.1 Sensor and Actuator Elements for SHM Systems 54222.3.2 Communication in SHM Systems 55022.3.3 SHM Data Evaluation Approaches and Principles 55222.4 SHM Areas and Application and Case Studies 55522.5 Implications of Material Integration for SHM Systems 56122.6 Conclusion and Outlook 562References 56423 Achievements and Open Issues Toward Embedding Tactile Sensing and Interpretation into Electronic Skin Systems 571Ali Ibrahim, Luigi Pinna, Lucia Seminara, and Maurizio Valle23.1 Introduction 57123.2 The Skin Mechanical Structure 57323.2.1 Transducers and Materials 57323.2.2 An Example of Skin Integration into an Existing Robotic Platform 57523.3 Tactile Information Processing 57923.4 Computational Requirements 58223.4.1 Electrical Impedance Tomography 58223.4.2 Tensorial Kernel 58323.5 Conclusions 585References 58524 Intelligent Materials in Machine Tool Applications: A Review 595Hans-Christian Möhring24.1 Applications of Shape Memory Alloys (SMA) 59624.2 Applications of Piezoelectric Ceramics 59624.3 Applications of Magnetostrictive Materials 59824.4 Applications of Electro- and MagnetorheologicalFluids 60024.5 Intelligent Structures and Components 60124.6 Summary and Conclusion 603References 60425 New Markets/Opportunities through Availability of Product Life Cycle Data 613Thorsten Wuest, Karl Hribernik, and Klaus-Dieter Thoben25.1 Product Life Cycle Management 61325.1.1 Closed-Loop and Item-Level PLM 61525.1.2 Data and Information in PLM 61525.1.3 Supporting Concepts for Data and Information Integration in PLM 61625.2 Case Studies 61725.2.1 Case Study 1: Life Cycle of Leisure Boats 61725.2.1.1 Sensors Used 61825.2.1.2 Potential Application of Sensorial Materials 61925.2.1.3 Limitations and Opportunities of Sensorial Materials 61925.2.2 Case Study 2: PROMISE – Product Life Cycle Management and Information Using Smart Embedded Systems 62025.2.2.1 Sensors Used 62025.2.2.2 Potential Application of Sensorial Materials 62125.2.2.3 Limitations and Opportunities of Sensorial Materials 62125.2.3 Case Study 3: Composite Bridge 62225.2.3.1 Sensors Used 62325.2.3.2 Potential Application of Sensorial Materials 62325.2.3.3 Limitations and Opportunities of Sensorial Materials 62325.3 Potential of Sensorial Materials in PLM Application 623Acknowledgment 624References 62426 Human–Computer Interaction with Novel and Advanced Materials 629Tanja Döring, Robert Porzel, and Rainer Malaka26.1 Introduction 62926.2 New Forms of Human–Computer Interaction 63026.3 Applications and Scenarios 63326.3.1 Domestic and Personal Devices 63326.3.1.1 The Marble Answering Machine 63326.3.1.2 Living Wall: An Interactive Wallpaper 63426.3.1.3 Sprout I/O and Shutters: Ambient Textile Information Displays 63426.3.1.4 FlexCase: A Flexible Sensing and Display Cover 63526.3.2 Learning, Collaboration, and Entertainment 63526.3.2.1 Tangibles for Learning and Creativity 63526.3.2.2 inFORM: Supporting Remote Collaboration through Shape Capture and Actuation 63626.3.2.3 The Soap Bubble Interface 63726.4 Opportunities and Challenges 63726.5 Conclusions 639References 639Index 645

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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• Foreword XVPreface XIXPart One Introduction 11 On Concepts and Challenges of Realizing Material-Integrated Intelligent Systems 3Stefan Bosse and Dirk Lehmhus1.1 Introduction 31.2 System Development Methodologies and Tools (Part Two) 71.3 Sensor Technologies and Material Integration (Part Three and Four) 81.4 Signal and Data Processing (Part Five) 151.5 Networking and Communication (Part Six) 171.6 Energy Supply and Management (Part Seven) 211.7 Applications (Part Eight) 21References 24Part Two System Development 292 Design Methodology for Intelligent Technical Systems 31Mareen Vaßholz, Roman Dumitrescu, and Jürgen Gausemeier2.1 From Mechatronics to Intelligent Technical Systems 322.2 Self-Optimizing Systems 362.3 Design Methodology for Intelligent Technical Systems 382.3.1 Domain-Spanning Conceptual Design 412.3.2 Domain-Specific Conceptual Design 50References 513 Smart Systems Design Methodologies and Tools 55Nicola Bombieri, Franco Fummi, Giuliana Gangemi, Michelangelo Grosso,Enrico Macii, Massimo Poncino, and Salvatore Rinaudo3.1 Introduction 553.2 Smart Electronic Systems and Their Design Challenges 563.3 The Smart Systems Codesign before SMAC 573.4 The SMAC Platform 603.4.1 The Platform Overview 613.4.1.1 System C–SystemVue Cosimulation 613.4.1.2 ADS and the Thermal Simulation 633.4.1.3 EMPro Extension and ADS Integration 643.4.1.4 Automated EM – Circuit Cosimulation in ADS 643.4.1.5 HIF Suite Toolsuite 653.4.1.6 The MEMS+ Platform 663.4.2 The (Co)Simulation Levels and the Design–Domains Matrix 673.5 Case Study: A Sensor Node for Drift-Free Limb Tracking 693.5.1 System Architecture 713.5.2 Model Development and System-Level Simulation 713.5.3 Results 733.6 Conclusions 76Acknowledgments 77References 77Part Three Sensor Technologies 814 Microelectromechanical Systems (MEMS) 83Li Yunjia4.1 Introduction 834.1.1 What Is MEMS 834.1.2 Why MEMS 844.1.3 MEMS Sensors 844.1.4 Goal of This Chapter 854.2 Materials 854.2.1 Silicon 854.2.2 Dielectrics 864.2.3 Metals 874.3 Microfabrication Technologies 874.3.1 Silicon Wafers 874.3.2 Lithography 884.3.3 Etching 914.3.4 Deposition Techniques 934.3.5 Other Processes 944.3.6 Surface and Bulk Micromachining 954.4 MEMS Sensor 954.4.1 Resistive Sensors 954.4.2 Capacitive Sensors 994.5 Sensor Systems 103References 1045 Fiber-Optic Sensors 107Yi Yang, Kevin Chen, and Nikhil Gupta5.1 Introduction to Fiber-Optic Sensors 1075.1.1 Sensing Principles 1085.1.2 Types of Optical Fibers 1085.2 Trends in Sensor Fabrication and Miniaturization 1105.3 Fiber-Optic Sensors for Structural Health Monitoring 1125.3.1 Sensors for Cure Monitoring of Composites 1145.3.2 Embedded FOS in Composite Materials 1145.3.3 Surface-Mounted FOS in Composite Materials 1155.3.4 FOS for Structural Monitoring 1155.3.4.1 Aerospace Structures 1155.3.4.2 Civil Structures 1165.3.4.3 Marine Structures 1165.4 Frequency Modulation Sensors 1175.4.1 Bragg Grating Sensors 1175.4.2 Fabry–Pérot Interferometer Sensor 1185.4.3 Whispering Gallery Mode Sensors 1195.5 Intensity Modulation Sensors 1225.5.1 Fiber Microbend Sensors 1225.5.2 Fiber-Optic Loop Sensor 1235.6 Some Challenges in SHM of Composite Materials 1285.7 Summary 128Acknowledgments 129References 1296 Electronics Development for Integration 137Jan Vanfleteren6.1 Introduction 1376.1.1 Standard Flat Rigid Printed Circuits Boards and Components Assembly 1376.1.2 Flexible Circuits 1386.1.3 Need for Alternative Circuit and Packaging Materials 1406.2 Chip Package Miniaturization Technologies 1406.2.1 Ultrathin Chip Package Technology 1406.2.2 UTCP Circuit Integration 1426.2.2.1 UTCP Embedding 1426.2.2.2 UTCP Stacking 1436.2.3 Applications 1436.3 Elastic Circuits 1456.3.1 Printed Circuit Board-Based Elastic Circuits 1456.3.2 Thin Film Metal-Based Elastic Circuits 1486.3.3 Applications 1486.3.3.1 Wearable Light Therapy 1486.3.4 Stretchable Displays 1496.4 2.5D Rigid Thermoplastic Circuits 1526.5 Large Area Textile-Based Circuits 1536.5.1 Electronic Module Integration Technology 1546.5.2 Applications 1556.6 Conclusions and Outlook 157References 157Part Four Material Integration Solutions 1597 Sensor Integration in Fiber-Reinforced Polymers 161Maryam Kahali Moghaddam, Mariugenia Salas, Michael Koerdt, Christian Brauner, Martina Hübner, Dirk Lehmhus, and Walter Lang7.1 Introduction to Fiber-Reinforced Polymers 1617.2 Applications of Integrated Systems in Composites 1647.2.1 Production Process Monitoring and Quality Control of Composites 1647.2.1.1 Monitoring of the Resin Flow 1667.2.1.2 Analytical Modeling of Resin Front by Means of Simulation 1667.2.1.3 Monitoring the Resin Curing 1667.2.2 In-Service Applications of Integrated Systems 1677.2.2.1 Use for Structural Health Monitoring (SHM) 1677.2.2.2 Use As Support to Nondestructive Evaluation and Testing (NDE/NDT) 1707.3 Fiber-Reinforced Polymer Production and Sensor Integration Processes 1707.3.1 Overview of Fiber-Reinforced Polymer Production Processes 1707.3.2 Sensor Integration in Fiber-Reinforced Polymers: Selected Case Studies 1757.4 Electronics Integration and Data Processing 1797.4.1 Materials Integration of Electronics 1807.4.2 Electronics for Wireless Sensing 1817.5 Examples of Sensors Integrated in Fiber-Reinforced Polymer Composites 1837.5.1 Ultrasound Reflection Sensing 1837.5.2 Pressure Sensors 1847.5.3 Thermocouples 1867.5.4 Fiber Optic Sensors 1877.5.5 Interdigital Planar Capacitive Sensors 1887.6 Conclusion 192Acknowledgments 193References 1938 Integration in Sheet Metal Structures 201Welf-Guntram Drossel, Roland Müller, Matthias Nestler, and Sebastian Hensel8.1 Introduction 2018.2 Integration Technology 2048.3 Forming of Piezometal Compounds 2058.4 Characterization of Functionality 2088.5 Fields of Application 2118.6 Conclusion and Outlook 212References 2129 Sensor and Electronics Integration in Additive Manufacturing 217Dirk Lehmhus and Matthias Busse9.1 Introduction to Additive Manufacturing 2179.2 Overview of AM Processes 2249.3 Links between Sensor Integration and Additive Manufacturing 2289.4 AM Sensor Integration Case Studies 2309.4.1 Cavity-Based Sensor and Electronic System Integration 2369.4.2 Multiprocess Hybrid Manufacturing Systems 2399.4.3 Toward a Single AM Platform for Structural Electronics Fabrication 2439.5 Conclusion and Outlook 245Abbreviations 246References 248Part Five Signal and Data Processing: The Sensor Node Level 25710 Analog Sensor Signal Processing and Analog-to-Digital Conversion 259John Horstmann, Marco Ramsbeck, and Stefan Bosse10.1 Operational Amplifiers 26010.2 Analog-to-Digital Converter Specifications 26210.3 Data Converter Architectures 26810.4 Low-Power ADC Designs and Power Classification 27610.5 Moving Window ADC Approach 277References 27911 Digital Real-Time Data Processing with Embedded Systems 281Stefan Bosse and Dirk Lehmhus11.1 Levels of Information 28111.2 Algorithms and Computational Models 28311.3 Scientific Data Mining 28711.4 Real-Time and Parallel Processing 291References 29712 The Known World: Model-Based Computing and Inverse Numeric 301Armin Lechleiter and Stefan Bosse12.1 Physical Models in Parameter Identification 30212.2 Noisy Data Due to Sensor and Modeling Errors 30412.3 Coping with Noisy Data: Tikhonov Regularization and Parameter Choice Rules 30612.4 Tikhonov Regularization 30812.5 Rules for the Choice of the Regularization Parameter 30912.6 Explicit Minimizers for Linear Models 31112.7 The Soft-Shrinkage Iteration 31212.8 Iterative Regularization Schemes 31312.9 Gradient Descent Schemes 31412.10 Newton-Type Regularization Schemes 31712.11 Numerical Examples in Load Reconstruction 318References 32613 The Unknown World: Model-Free Computing and Machine Learning 329Stefan Bosse13.1 Machine Learning – An Overview 32913.2 Learning of Data Streams 33113.3 Learning with Noise 33313.4 Distributed Event-Based Learning 33313.5 ε-Interval and Nearest-Neighborhood Decision Tree Learning 33413.6 Machine Learning – A Sensorial Material Demonstrator 336References 34014 Robustness and Data Fusion 343Stefan Bosse14.1 Robust System Design on System Level 345References 348Part Six Networking and Communication: The Sensor Network Level 34915 Communication Hardware 351Tim Tiedemann15.1 Communication Hardware in Their Applications 35115.2 Requirements for Embedded Communication Hardware 35215.3 Overview of Physical Communication Classes 35415.4 Examples of Wired Communication Hardware 35615.5 Examples of Wireless Communication Hardware 35815.6 Examples of Optical Communication Hardware 36015.7 Summary 360References 36116 Networks and Communication Protocols 363Stefan Bosse16.1 Network Topologies and Network of Networks 36416.2 Redundancy in Networks 36516.3 Protocols 36616.4 Switched Networks versus Message Passing 36816.5 Bus Systems 36916.6 Message Passing and Message Formats 37016.7 Routing 37016.8 Failures, Robustness, and Reliability 37716.9 Distributed Sensor Networks 37816.10 Active Messaging and Agents 381References 38217 Distributed and Cloud Computing: The Big Machine 385Stefan Bosse17.1 Reference 38618 The Mobile Agent and Multiagent Systems 387Stefan Bosse18.1 The Agent Computation and Interaction Model 38918.2 Dynamic Activity-Transition Graphs 39418.3 The Agent Behavior Class 39518.4 Communication and Interaction of Agents 39618.5 Agent Programming Models 39718.6 Agent Processing Platforms and Technologies 40418.7 Agent-Based Learning 41518.8 Event and Distributed Agent-Based Learning ofNoisy Sensor Data 416References 420Part Seven Energy Supply 42319 Energy Management and Distribution 425Stefan Bosse19.1 Design of Low-Power Smart Sensor Systems 42619.2 A Toolbox for Energy Analysis and Simulation 43019.3 Dynamic Power Management 43419.3.1 CPU-Centric DPM 43519.3.2 I/O-Centric DPM 43719.3.3 EDS Algorithm 43819.4 Energy-Aware Communication in Sensor Networks 44019.5 Energy Distribution in Sensor Networks 44219.5.1 Distributed Energy Management in Sensor NetworksUsing Agents 443References 44620 Microenergy Storage 449Robert Kun, Chi Chen, and Francesco Ciucci20.1 Introduction 44920.2 Energy Harvesting/Scavenging 45120.3 Energy Storage 45220.3.1 Capacitors 45220.3.2 Batteries 45820.3.3 Fuel Cells 46720.3.3.1 Low-Temperature Fuel Cells 46920.3.3.2 High-Temperature Fuel Cells 46920.3.4 Other Storage Systems 46920.4 Summary and Perspectives 470References 47021 Energy Harvesting 479Rolanas Dauksevicius and Danick Briand21.1 Introduction 47921.2 Mechanical Energy Harvesters 48021.2.1 Piezoelectric Micropower Generators 48221.2.2 Micropower Generators Based on Electroactive Polymers 48921.2.3 Electrostatic Micropower Generators 49021.2.4 Electromagnetic Micropower Generators 49121.2.5 Triboelectric Nanogenerators 49221.2.6 Hybrid Micropower Generators 49321.2.7 Wideband and Nonlinear Micropower Generators 49421.2.8 Concluding Remarks 49521.3 Thermal Energy Harvesters 49621.3.1 Introduction to Thermoelectric Generators 49621.3.2 Thermoelectric Materials and Efficiency 49921.3.3 Other Thermal-to-Electrical Energy ConversionTechniques 50121.4 Radiation Harvesters 50221.4.1 Light Energy Harvesters 50221.4.2 RF Energy Harvesters 50621.5 Summary and Perspectives 507References 512Part Eight Application Scenarios 52922 Structural Health Monitoring (SHM) 531Dirk Lehmhus and Matthias Busse22.1 Introduction 53122.2 Motivations for SHM System Implementation 53622.3 SHM System Classification and Main Components 54022.3.1 Sensor and Actuator Elements for SHM Systems 54222.3.2 Communication in SHM Systems 55022.3.3 SHM Data Evaluation Approaches and Principles 55222.4 SHM Areas and Application and Case Studies 55522.5 Implications of Material Integration for SHM Systems 56122.6 Conclusion and Outlook 562References 56423 Achievements and Open Issues Toward Embedding Tactile Sensing and Interpretation into Electronic Skin Systems 571Ali Ibrahim, Luigi Pinna, Lucia Seminara, and Maurizio Valle23.1 Introduction 57123.2 The Skin Mechanical Structure 57323.2.1 Transducers and Materials 57323.2.2 An Example of Skin Integration into an Existing Robotic Platform 57523.3 Tactile Information Processing 57923.4 Computational Requirements 58223.4.1 Electrical Impedance Tomography 58223.4.2 Tensorial Kernel 58323.5 Conclusions 585References 58524 Intelligent Materials in Machine Tool Applications: A Review 595Hans-Christian Möhring24.1 Applications of Shape Memory Alloys (SMA) 59624.2 Applications of Piezoelectric Ceramics 59624.3 Applications of Magnetostrictive Materials 59824.4 Applications of Electro- and MagnetorheologicalFluids 60024.5 Intelligent Structures and Components 60124.6 Summary and Conclusion 603References 60425 New Markets/Opportunities through Availability of Product Life Cycle Data 613Thorsten Wuest, Karl Hribernik, and Klaus-Dieter Thoben25.1 Product Life Cycle Management 61325.1.1 Closed-Loop and Item-Level PLM 61525.1.2 Data and Information in PLM 61525.1.3 Supporting Concepts for Data and Information Integration in PLM 61625.2 Case Studies 61725.2.1 Case Study 1: Life Cycle of Leisure Boats 61725.2.1.1 Sensors Used 61825.2.1.2 Potential Application of Sensorial Materials 61925.2.1.3 Limitations and Opportunities of Sensorial Materials 61925.2.2 Case Study 2: PROMISE – Product Life Cycle Management and Information Using Smart Embedded Systems 62025.2.2.1 Sensors Used 62025.2.2.2 Potential Application of Sensorial Materials 62125.2.2.3 Limitations and Opportunities of Sensorial Materials 62125.2.3 Case Study 3: Composite Bridge 62225.2.3.1 Sensors Used 62325.2.3.2 Potential Application of Sensorial Materials 62325.2.3.3 Limitations and Opportunities of Sensorial Materials 62325.3 Potential of Sensorial Materials in PLM Application 623Acknowledgment 624References 62426 Human–Computer Interaction with Novel and Advanced Materials 629Tanja Döring, Robert Porzel, and Rainer Malaka26.1 Introduction 62926.2 New Forms of Human–Computer Interaction 63026.3 Applications and Scenarios 63326.3.1 Domestic and Personal Devices 63326.3.1.1 The Marble Answering Machine 63326.3.1.2 Living Wall: An Interactive Wallpaper 63426.3.1.3 Sprout I/O and Shutters: Ambient Textile Information Displays 63426.3.1.4 FlexCase: A Flexible Sensing and Display Cover 63526.3.2 Learning, Collaboration, and Entertainment 63526.3.2.1 Tangibles for Learning and Creativity 63526.3.2.2 inFORM: Supporting Remote Collaboration through Shape Capture and Actuation 63626.3.2.3 The Soap Bubble Interface 63726.4 Opportunities and Challenges 63726.5 Conclusions 639References 639Index 645

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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• Foreword XVPreface XIXPart One Introduction 11 On Concepts and Challenges of Realizing Material-Integrated Intelligent Systems 3Stefan Bosse and Dirk Lehmhus1.1 Introduction 31.2 System Development Methodologies and Tools (Part Two) 71.3 Sensor Technologies and Material Integration (Part Three and Four) 81.4 Signal and Data Processing (Part Five) 151.5 Networking and Communication (Part Six) 171.6 Energy Supply and Management (Part Seven) 211.7 Applications (Part Eight) 21References 24Part Two System Development 292 Design Methodology for Intelligent Technical Systems 31Mareen Vaßholz, Roman Dumitrescu, and Jürgen Gausemeier2.1 From Mechatronics to Intelligent Technical Systems 322.2 Self-Optimizing Systems 362.3 Design Methodology for Intelligent Technical Systems 382.3.1 Domain-Spanning Conceptual Design 412.3.2 Domain-Specific Conceptual Design 50References 513 Smart Systems Design Methodologies and Tools 55Nicola Bombieri, Franco Fummi, Giuliana Gangemi, Michelangelo Grosso,Enrico Macii, Massimo Poncino, and Salvatore Rinaudo3.1 Introduction 553.2 Smart Electronic Systems and Their Design Challenges 563.3 The Smart Systems Codesign before SMAC 573.4 The SMAC Platform 603.4.1 The Platform Overview 613.4.1.1 System C–SystemVue Cosimulation 613.4.1.2 ADS and the Thermal Simulation 633.4.1.3 EMPro Extension and ADS Integration 643.4.1.4 Automated EM – Circuit Cosimulation in ADS 643.4.1.5 HIF Suite Toolsuite 653.4.1.6 The MEMS+ Platform 663.4.2 The (Co)Simulation Levels and the Design–Domains Matrix 673.5 Case Study: A Sensor Node for Drift-Free Limb Tracking 693.5.1 System Architecture 713.5.2 Model Development and System-Level Simulation 713.5.3 Results 733.6 Conclusions 76Acknowledgments 77References 77Part Three Sensor Technologies 814 Microelectromechanical Systems (MEMS) 83Li Yunjia4.1 Introduction 834.1.1 What Is MEMS 834.1.2 Why MEMS 844.1.3 MEMS Sensors 844.1.4 Goal of This Chapter 854.2 Materials 854.2.1 Silicon 854.2.2 Dielectrics 864.2.3 Metals 874.3 Microfabrication Technologies 874.3.1 Silicon Wafers 874.3.2 Lithography 884.3.3 Etching 914.3.4 Deposition Techniques 934.3.5 Other Processes 944.3.6 Surface and Bulk Micromachining 954.4 MEMS Sensor 954.4.1 Resistive Sensors 954.4.2 Capacitive Sensors 994.5 Sensor Systems 103References 1045 Fiber-Optic Sensors 107Yi Yang, Kevin Chen, and Nikhil Gupta5.1 Introduction to Fiber-Optic Sensors 1075.1.1 Sensing Principles 1085.1.2 Types of Optical Fibers 1085.2 Trends in Sensor Fabrication and Miniaturization 1105.3 Fiber-Optic Sensors for Structural Health Monitoring 1125.3.1 Sensors for Cure Monitoring of Composites 1145.3.2 Embedded FOS in Composite Materials 1145.3.3 Surface-Mounted FOS in Composite Materials 1155.3.4 FOS for Structural Monitoring 1155.3.4.1 Aerospace Structures 1155.3.4.2 Civil Structures 1165.3.4.3 Marine Structures 1165.4 Frequency Modulation Sensors 1175.4.1 Bragg Grating Sensors 1175.4.2 Fabry–Pérot Interferometer Sensor 1185.4.3 Whispering Gallery Mode Sensors 1195.5 Intensity Modulation Sensors 1225.5.1 Fiber Microbend Sensors 1225.5.2 Fiber-Optic Loop Sensor 1235.6 Some Challenges in SHM of Composite Materials 1285.7 Summary 128Acknowledgments 129References 1296 Electronics Development for Integration 137Jan Vanfleteren6.1 Introduction 1376.1.1 Standard Flat Rigid Printed Circuits Boards and Components Assembly 1376.1.2 Flexible Circuits 1386.1.3 Need for Alternative Circuit and Packaging Materials 1406.2 Chip Package Miniaturization Technologies 1406.2.1 Ultrathin Chip Package Technology 1406.2.2 UTCP Circuit Integration 1426.2.2.1 UTCP Embedding 1426.2.2.2 UTCP Stacking 1436.2.3 Applications 1436.3 Elastic Circuits 1456.3.1 Printed Circuit Board-Based Elastic Circuits 1456.3.2 Thin Film Metal-Based Elastic Circuits 1486.3.3 Applications 1486.3.3.1 Wearable Light Therapy 1486.3.4 Stretchable Displays 1496.4 2.5D Rigid Thermoplastic Circuits 1526.5 Large Area Textile-Based Circuits 1536.5.1 Electronic Module Integration Technology 1546.5.2 Applications 1556.6 Conclusions and Outlook 157References 157Part Four Material Integration Solutions 1597 Sensor Integration in Fiber-Reinforced Polymers 161Maryam Kahali Moghaddam, Mariugenia Salas, Michael Koerdt, Christian Brauner, Martina Hübner, Dirk Lehmhus, and Walter Lang7.1 Introduction to Fiber-Reinforced Polymers 1617.2 Applications of Integrated Systems in Composites 1647.2.1 Production Process Monitoring and Quality Control of Composites 1647.2.1.1 Monitoring of the Resin Flow 1667.2.1.2 Analytical Modeling of Resin Front by Means of Simulation 1667.2.1.3 Monitoring the Resin Curing 1667.2.2 In-Service Applications of Integrated Systems 1677.2.2.1 Use for Structural Health Monitoring (SHM) 1677.2.2.2 Use As Support to Nondestructive Evaluation and Testing (NDE/NDT) 1707.3 Fiber-Reinforced Polymer Production and Sensor Integration Processes 1707.3.1 Overview of Fiber-Reinforced Polymer Production Processes 1707.3.2 Sensor Integration in Fiber-Reinforced Polymers: Selected Case Studies 1757.4 Electronics Integration and Data Processing 1797.4.1 Materials Integration of Electronics 1807.4.2 Electronics for Wireless Sensing 1817.5 Examples of Sensors Integrated in Fiber-Reinforced Polymer Composites 1837.5.1 Ultrasound Reflection Sensing 1837.5.2 Pressure Sensors 1847.5.3 Thermocouples 1867.5.4 Fiber Optic Sensors 1877.5.5 Interdigital Planar Capacitive Sensors 1887.6 Conclusion 192Acknowledgments 193References 1938 Integration in Sheet Metal Structures 201Welf-Guntram Drossel, Roland Müller, Matthias Nestler, and Sebastian Hensel8.1 Introduction 2018.2 Integration Technology 2048.3 Forming of Piezometal Compounds 2058.4 Characterization of Functionality 2088.5 Fields of Application 2118.6 Conclusion and Outlook 212References 2129 Sensor and Electronics Integration in Additive Manufacturing 217Dirk Lehmhus and Matthias Busse9.1 Introduction to Additive Manufacturing 2179.2 Overview of AM Processes 2249.3 Links between Sensor Integration and Additive Manufacturing 2289.4 AM Sensor Integration Case Studies 2309.4.1 Cavity-Based Sensor and Electronic System Integration 2369.4.2 Multiprocess Hybrid Manufacturing Systems 2399.4.3 Toward a Single AM Platform for Structural Electronics Fabrication 2439.5 Conclusion and Outlook 245Abbreviations 246References 248Part Five Signal and Data Processing: The Sensor Node Level 25710 Analog Sensor Signal Processing and Analog-to-Digital Conversion 259John Horstmann, Marco Ramsbeck, and Stefan Bosse10.1 Operational Amplifiers 26010.2 Analog-to-Digital Converter Specifications 26210.3 Data Converter Architectures 26810.4 Low-Power ADC Designs and Power Classification 27610.5 Moving Window ADC Approach 277References 27911 Digital Real-Time Data Processing with Embedded Systems 281Stefan Bosse and Dirk Lehmhus11.1 Levels of Information 28111.2 Algorithms and Computational Models 28311.3 Scientific Data Mining 28711.4 Real-Time and Parallel Processing 291References 29712 The Known World: Model-Based Computing and Inverse Numeric 301Armin Lechleiter and Stefan Bosse12.1 Physical Models in Parameter Identification 30212.2 Noisy Data Due to Sensor and Modeling Errors 30412.3 Coping with Noisy Data: Tikhonov Regularization and Parameter Choice Rules 30612.4 Tikhonov Regularization 30812.5 Rules for the Choice of the Regularization Parameter 30912.6 Explicit Minimizers for Linear Models 31112.7 The Soft-Shrinkage Iteration 31212.8 Iterative Regularization Schemes 31312.9 Gradient Descent Schemes 31412.10 Newton-Type Regularization Schemes 31712.11 Numerical Examples in Load Reconstruction 318References 32613 The Unknown World: Model-Free Computing and Machine Learning 329Stefan Bosse13.1 Machine Learning – An Overview 32913.2 Learning of Data Streams 33113.3 Learning with Noise 33313.4 Distributed Event-Based Learning 33313.5 ε-Interval and Nearest-Neighborhood Decision Tree Learning 33413.6 Machine Learning – A Sensorial Material Demonstrator 336References 34014 Robustness and Data Fusion 343Stefan Bosse14.1 Robust System Design on System Level 345References 348Part Six Networking and Communication: The Sensor Network Level 34915 Communication Hardware 351Tim Tiedemann15.1 Communication Hardware in Their Applications 35115.2 Requirements for Embedded Communication Hardware 35215.3 Overview of Physical Communication Classes 35415.4 Examples of Wired Communication Hardware 35615.5 Examples of Wireless Communication Hardware 35815.6 Examples of Optical Communication Hardware 36015.7 Summary 360References 36116 Networks and Communication Protocols 363Stefan Bosse16.1 Network Topologies and Network of Networks 36416.2 Redundancy in Networks 36516.3 Protocols 36616.4 Switched Networks versus Message Passing 36816.5 Bus Systems 36916.6 Message Passing and Message Formats 37016.7 Routing 37016.8 Failures, Robustness, and Reliability 37716.9 Distributed Sensor Networks 37816.10 Active Messaging and Agents 381References 38217 Distributed and Cloud Computing: The Big Machine 385Stefan Bosse17.1 Reference 38618 The Mobile Agent and Multiagent Systems 387Stefan Bosse18.1 The Agent Computation and Interaction Model 38918.2 Dynamic Activity-Transition Graphs 39418.3 The Agent Behavior Class 39518.4 Communication and Interaction of Agents 39618.5 Agent Programming Models 39718.6 Agent Processing Platforms and Technologies 40418.7 Agent-Based Learning 41518.8 Event and Distributed Agent-Based Learning ofNoisy Sensor Data 416References 420Part Seven Energy Supply 42319 Energy Management and Distribution 425Stefan Bosse19.1 Design of Low-Power Smart Sensor Systems 42619.2 A Toolbox for Energy Analysis and Simulation 43019.3 Dynamic Power Management 43419.3.1 CPU-Centric DPM 43519.3.2 I/O-Centric DPM 43719.3.3 EDS Algorithm 43819.4 Energy-Aware Communication in Sensor Networks 44019.5 Energy Distribution in Sensor Networks 44219.5.1 Distributed Energy Management in Sensor NetworksUsing Agents 443References 44620 Microenergy Storage 449Robert Kun, Chi Chen, and Francesco Ciucci20.1 Introduction 44920.2 Energy Harvesting/Scavenging 45120.3 Energy Storage 45220.3.1 Capacitors 45220.3.2 Batteries 45820.3.3 Fuel Cells 46720.3.3.1 Low-Temperature Fuel Cells 46920.3.3.2 High-Temperature Fuel Cells 46920.3.4 Other Storage Systems 46920.4 Summary and Perspectives 470References 47021 Energy Harvesting 479Rolanas Dauksevicius and Danick Briand21.1 Introduction 47921.2 Mechanical Energy Harvesters 48021.2.1 Piezoelectric Micropower Generators 48221.2.2 Micropower Generators Based on Electroactive Polymers 48921.2.3 Electrostatic Micropower Generators 49021.2.4 Electromagnetic Micropower Generators 49121.2.5 Triboelectric Nanogenerators 49221.2.6 Hybrid Micropower Generators 49321.2.7 Wideband and Nonlinear Micropower Generators 49421.2.8 Concluding Remarks 49521.3 Thermal Energy Harvesters 49621.3.1 Introduction to Thermoelectric Generators 49621.3.2 Thermoelectric Materials and Efficiency 49921.3.3 Other Thermal-to-Electrical Energy ConversionTechniques 50121.4 Radiation Harvesters 50221.4.1 Light Energy Harvesters 50221.4.2 RF Energy Harvesters 50621.5 Summary and Perspectives 507References 512Part Eight Application Scenarios 52922 Structural Health Monitoring (SHM) 531Dirk Lehmhus and Matthias Busse22.1 Introduction 53122.2 Motivations for SHM System Implementation 53622.3 SHM System Classification and Main Components 54022.3.1 Sensor and Actuator Elements for SHM Systems 54222.3.2 Communication in SHM Systems 55022.3.3 SHM Data Evaluation Approaches and Principles 55222.4 SHM Areas and Application and Case Studies 55522.5 Implications of Material Integration for SHM Systems 56122.6 Conclusion and Outlook 562References 56423 Achievements and Open Issues Toward Embedding Tactile Sensing and Interpretation into Electronic Skin Systems 571Ali Ibrahim, Luigi Pinna, Lucia Seminara, and Maurizio Valle23.1 Introduction 57123.2 The Skin Mechanical Structure 57323.2.1 Transducers and Materials 57323.2.2 An Example of Skin Integration into an Existing Robotic Platform 57523.3 Tactile Information Processing 57923.4 Computational Requirements 58223.4.1 Electrical Impedance Tomography 58223.4.2 Tensorial Kernel 58323.5 Conclusions 585References 58524 Intelligent Materials in Machine Tool Applications: A Review 595Hans-Christian Möhring24.1 Applications of Shape Memory Alloys (SMA) 59624.2 Applications of Piezoelectric Ceramics 59624.3 Applications of Magnetostrictive Materials 59824.4 Applications of Electro- and MagnetorheologicalFluids 60024.5 Intelligent Structures and Components 60124.6 Summary and Conclusion 603References 60425 New Markets/Opportunities through Availability of Product Life Cycle Data 613Thorsten Wuest, Karl Hribernik, and Klaus-Dieter Thoben25.1 Product Life Cycle Management 61325.1.1 Closed-Loop and Item-Level PLM 61525.1.2 Data and Information in PLM 61525.1.3 Supporting Concepts for Data and Information Integration in PLM 61625.2 Case Studies 61725.2.1 Case Study 1: Life Cycle of Leisure Boats 61725.2.1.1 Sensors Used 61825.2.1.2 Potential Application of Sensorial Materials 61925.2.1.3 Limitations and Opportunities of Sensorial Materials 61925.2.2 Case Study 2: PROMISE – Product Life Cycle Management and Information Using Smart Embedded Systems 62025.2.2.1 Sensors Used 62025.2.2.2 Potential Application of Sensorial Materials 62125.2.2.3 Limitations and Opportunities of Sensorial Materials 62125.2.3 Case Study 3: Composite Bridge 62225.2.3.1 Sensors Used 62325.2.3.2 Potential Application of Sensorial Materials 62325.2.3.3 Limitations and Opportunities of Sensorial Materials 62325.3 Potential of Sensorial Materials in PLM Application 623Acknowledgment 624References 62426 Human–Computer Interaction with Novel and Advanced Materials 629Tanja Döring, Robert Porzel, and Rainer Malaka26.1 Introduction 62926.2 New Forms of Human–Computer Interaction 63026.3 Applications and Scenarios 63326.3.1 Domestic and Personal Devices 63326.3.1.1 The Marble Answering Machine 63326.3.1.2 Living Wall: An Interactive Wallpaper 63426.3.1.3 Sprout I/O and Shutters: Ambient Textile Information Displays 63426.3.1.4 FlexCase: A Flexible Sensing and Display Cover 63526.3.2 Learning, Collaboration, and Entertainment 63526.3.2.1 Tangibles for Learning and Creativity 63526.3.2.2 inFORM: Supporting Remote Collaboration through Shape Capture and Actuation 63626.3.2.3 The Soap Bubble Interface 63726.4 Opportunities and Challenges 63726.5 Conclusions 639References 639Index 645