Electronics in Advanced Research Industries: Industry 4.0 to Industry 5.0 Advances(IEEE Press)

Electronics in Advanced Research Industries A one-of-a-kind examination of the latest developments in machine control In Electronics in Advanced Research Industries: Industry 4.0 to Industry 5.0 Advances, accomplished electronics researcher and engineer Alessandro Massaro delivers a comprehensive exploration of the latest ways in which people have achieved machine control, including automated vision technologies, advanced electronic and micro-nano sensors, advanced robotics, and more. The book is composed of nine chapters, each containing examples and diagrams designed to assist the reader in applying the concepts discussed within to common issues and problems in the real-world. Combining electronics and mechatronics to show how they can each be implemented in production line systems, the book presents insightful new ways to use artificial intelligence in production line machines. The author explains how facilities can upgrade their systems to an Industry 5.0 environment. Electronics in Advanced Research Industries: Industry 4.0 to Industry 5.0 Advances also provides: A thorough introduction to the state-of-the-art in a variety of technological areas, including flexible technologies, scientific approaches, and intelligent automatic systems Comprehensive explorations of information technology infrastructures that support Industry 5.0 facilities, including production process simulation Practical discussions of human-machine interfaces, including mechatronic machine interface architectures integrating sensor systems and machine-to-machine (M2M) interfaces In-depth examinations of Internet of Things (IoT) solutions in industry, including cloud computing IoT Perfect for professionals working in electrical industry sectors in manufacturing, production line manufacturers, engineers, and members of R&D industry teams, Electronics in Advanced Research Industries: Industry 4.0 to Industry 5.0 Advances will also earn a place in libraries of technicians working in the process industry.

Table of Contents:
Preface xiii About the Author xv 1 State of the Art and Technology Innovation 1 1.1 State of the Art of Flexible Technologies in Industry 2 1.1.1 Sensors and Actuators Layer: I/O Layer 3 1.1.2 Agent/Firmware Layer: User Interface Layer 9 1.1.3 Gateway and Enterprise Service Bus Layer 9 1.1.4 IoT Middleware 10 1.1.5 Processing Layer 11 1.1.6 Application Layer 11 1.1.7 File Transfer Protocols 11 1.2 State of the Art of Scientific Approaches Oriented on Process Control and Automatisms 14 1.2.1 Architectures Integrating AI 14 1.2.2 AI Supervised and Unsupersived Algorithms 15 1.2.3 AI Image Processing 18 1.2.4 Production Process Mapping 20 1.2.5 Technologies of Industry 4.0 and Industry 5.0: Interconnection and Main Limits 21 1.2.6 Infrared Thermography in Monitoring Process 26 1.2.7 Key Parameters in Supply Chain and AI Improving Manufacturing Processes 27 1.3 Intelligent Automatic Systems in Industries 30 1.4 Technological Approaches to Transform the Production in Auto-Adaptive Control and Actuation Systems 31 1.5 Basic Concepts of Artificial Intelligence 33 1.6 Knowledge Upgrading in Industries 41 References 45 2 Information Technology Infrastructures Supporting Industry 5.0 Facilities 51 2.1 Production Process Simulation and Object Design Approaches 52 2.1.1 Object Design of a Data Mining Algorithm: Block Functions and Parameter Setting 55 2.1.2 Example 1: BPM Modeling of Wheat Storage Process for Pasta Production 59 2.1.3 Example 2: Block Diagram Design of a Servo Valve Control and Actuation System 61 2.1.4 Example 3: Block Diagram of a Liquid Production System 61 2.1.5 Example 4: UML Design of a Programmable Logic Controller System 62 2.1.6 Example 5: Electronic Logic Timing Diagram 64 2.1.7 Example 6: AR System in Kitchen Production Process 64 2.1.8 Example 7: Intelligent Canned Food Production Line 70 2.2 Electronic Logic Design Oriented on Information Infrastructure of Industry 5.0 71 2.3 Predictive Maintenance: Artificial Intelligence Failure Predictions and Information Infrastructure Layout in the Temperature Monitoring Process 74 2.4 Defect Estimation and Prediction by Artificial Neural Network 77 2.4.1 Other Methodologies to Map and Read Production Failures and Defects 79 2.5 Defect Clustering and Classification: Combined Use of the K-Means Algorithm with Infrared Thermography for Predictive Maintenance 82 2.6 Facilities of a Prototype Network Implementing Advanced Technology: Example of an Advanced Platform Suitable for Industry 5.0 Integrating Predictive Maintenance 84 2.7 Predictive Maintenance Approaches 86 2.7.1 Preventive Maintenance and Predictive Maintenance Operations in the Railway Industry 90 2.8 Examples of Advanced Infrastructures Implementing AI 93 2.9 Examples of Telemedicine Platforms Integrating Advanced Facilities 94 2.9.1 Advanced Telecardiology Platform 94 2.9.2 Advanced Teleoncology Platform 96 2.9.3 Multipurpose E-Health Platform 97 References 99 3 Human–Machine Interfaces 103 3.1 Mechatronic Machine Interface Architectures Integrating Sensor Systems 104 3.1.1 Multiple Mechatronic Boards Managing Different Production Stages 104 3.1.2 Mechatronic Boards Managing Component Processing 104 3.2 Machine-to-Machine Interfaces: New Concepts of Industry 5.0 106 3.3 Production Line Command and Actuation Interfaces in Upgraded Systems 111 3.3.1 PLC, PAC, Industrial PC, and Improvements 111 3.3.2 SCADA Systems for Centralization of Data Production 115 3.4 McCulloch–Pitts Neurons and Logic Port for Automatic Decision-Making Setting Thresholds 123 3.5 Programmable Logic Controller I/O Ports Interfacing with AI Engine 132 3.6 Human–Machine Interface for Data Transfer and AI Data Processing 134 3.7 Example of Interface Configuration of Temperature Control 135 3.8 AI Interfaces Oriented on Cybersecurity Attack Detection 136 3.9 AI Interfaces Oriented on Database Security 139 3.10 Cybersecurity Platform and AI Control Interface 148 References 151 4 Internet of Things Solutions in Industry 155 4.1 Cloud Computing IoT 156 4.1.1 IoT Agent 156 4.1.2 IoT Gateway in Smart Environments 158 4.1.3 Basic Elements of a Smart Industry Environment Controlling Production 160 4.1.3.1 Feedback Control: Basic Concepts 167 4.1.4 Augmented Reality Hardware and Cloud Computing Processing 169 4.1.5 Real-Time Control and Actuation 171 4.1.6 Localization Technologies in an Industrial Environment 175 4.1.7 GPU Processing Units 176 4.1.7.1 Performance of GPUs by Processing Binary Matrices 176 4.2 IoT and External Artificial Intelligence Engines 180 4.2.1 Artificial Engines and Server Location: Artificial Intelligence and Adaptive Production 180 4.2.2 IoT Security Systems in the Working Environment and Implementation Aspects 182 4.2.3 Example of Energy Power Control and Actuation: Energy Routing and Priority Load Management for Energy Efficiency 182 4.2.4 Online Configurators: Cloud DSS 186 4.3 Blockchain and IoT Data Storage Systems 194 4.3.1 Blockchain Implementation Rules 194 4.3.2 Blockchain and IoT Production Traceability 197 4.4 Mechatronic Machine Interface Architectures Integrating Sensor Systems 199 4.5 Multiple Mechatronic Boards Managing Different Production Stages 200 References 202 5 Advanced Robotics 203 5.1 Collaborative Robotics in Industry and Protocols 204 5.1.1 Data Protocols 206 5.1.2 Basic Concepts of Robotic Arms and Control Improvement 206 5.1.3 Collaborative Exoskeleton Communication System Protocols 212 5.1.4 Advanced Robotics and Intelligent Automation in Manufacturing: Logic Conditions and PLC Programming 213 5.2 Artificial Intelligence in Advanced Robotics and Auto-Adaptive Movement 218 5.2.1 General Technological Aspects about Auto-Adaptive Motion in Advanced Robotics 218 5.2.1.1 Main Aspects of Electrostatic Actuators 219 5.2.1.2 Microelectromechanical System Electrostatic Actuators 220 5.2.1.3 Piezoelectric Actuators 221 5.2.1.4 DC Motor Actuation 222 5.2.1.5 Intelligent Control Integrating AI: Speed Regulation 227 5.2.2 Improvement of Collaborative Exoskeletons by Auto-Adaptive Solutions Implementing Artificial Intelligence 231 5.3 Human–Robot Self-Learning Collaboration in Industrial Applications and Electronic Aspects 232 5.3.1 DC–DC Converter 232 5.3.2 Voltage Source Inverter 233 5.3.3 Current-Source Inverter 237 5.3.4 DC Voltage Source 238 5.3.5 Capacitor and Reactor Effects on Signal Control 238 5.3.6 Human-Robot System and Learning Approaches 239 5.3.6.1 Example of PID Implementation of Self-Adapting Gains 243 5.3.7 Unsupervised Learning Approaches 244 5.3.8 Soft Robotics for Intelligent Collaborative Robotics 245 5.4 Robotics in Additive Manufacturing 246 5.4.1 Additive Manufacturing in Industrial Production and Spray Technique 246 5.4.2 Artificial Intelligence Applications in Additive Manufacturing 247 5.4.3 Advanced Electronic for Design-to-Product Transformation: Laser Texturing Manufacturing and Artificial Intelligence 248 References 249 6 Advanced Optoelectronic and Micro-/Nanosensors 253 6.1 Nanotechnology Laboratories in Industries 254 6.1.1 Facilities for Micro-/Nanosensor Fabrication and Characterization 254 6.2 Micro- and Nanosensors as Preliminary Prototypes for Industry Research 260 6.2.1 Nanocomposite Optoelectronic Sensors and Optoelectronic Circuits for Pressure Sensors 260 6.2.1.1 Optical Fiber Nanocomposite Tip 260 6.2.2 Plasmonic Probes 266 6.2.3 Nanocomposite Pressure Sensor 273 6.2.4 Nanocomposite Sensor for Liquid Detection Systems and Fluid Loss Systems 277 6.2.4.1 Nanocomposite Sensor for Liquid Detection Systems Based on a Pillar-Type Layout 278 6.2.4.2 Micro- and Nanosensors in the Monitoring of Production Processes: Leakage Monitoring 285 6.2.5 Examples of Digital MEMS/NEMS Sensors: Technological Aspects and Applications 286 6.2.5.1 Thin Film MEMS 286 6.2.5.2 Nanoprobes for Medical Imaging 288 6.2.5.3 Diamond Thin Film Devices: Sensing Improvements 293 6.3 Multisensor Systems and Big Data Synchronization of Micro-/Nanoprobes 295 References 296 7 Image Vision Advances 301 7.1 Defect Classification by Artificial Intelligence and Data Processor Units 302 7.1.1 Artificial Intelligence Algorithms and Automatism for Defect Classification: Case Study of Tire Production 302 7.1.2 Welding Classification and Nondestructive Testing Suitable for the Quality Check 304 7.1.2.1 Watershed Image Segmentation and Automatic Welding Defect Classification 307 7.1.3 Encoding and Decoding Circuits in Artificial Intelligence Data Processing 309 7.1.4 Electronic Logic Port Implementations: Pixel Matrix Logic Condition 314 7.2 Image Vision Architectures and Electronic Design 314 7.2.1 Infrared Thermography Monitoring Industrial Processes 315 7.2.1.1 Welding Image Vision Processing and Architecture Design: Radiometric Post Processing 315 7.2.2 Electronic and Firmware for Inline Image Monitoring Systems: Hole Precision in Milling Quality Processes 316 7.2.3 Image Vision and Predictive Maintenance by Artificial Intelligence 319 7.2.3.1 Profilometer for Image Vision 319 7.2.3.2 In-Line 3D Image Vision AI System Integrating Profilometer and Image Processing 321 7.2.4 Augmented Reality Systems and Artificial Neural Networks: Image Vision Supporting Production Processes 323 7.2.5 Infrared Thermography Circuit Design and Automated System 324 7.3 Image Segmentation and Image Clustering 327 7.3.1 Electronic and Firmware for In-Line Monitoring Systems: Camera Connection 327 7.3.2 Image Segmentation and Clustering Techniques: Automated In-Line Monitoring Systems 327 7.3.3 Circuit Timing In-Line Monitoring and Data Storage Systems 328 7.3.4 Image Segmentation in Product Quality Monitoring: Snake Contour Approach 329 7.3.5 Advanced Image Clustering: K-Means Applied to Radiometric Images 331 7.4 Image Segmentation for Food Defect Detection 333 7.5 Random Forest Pixel Classification 335 References 339 8 Electronic and Reverse Engineering 341 8.1 Reverse Engineering Systems and Mechanical Precision 342 8.1.1 Reverse Engineering Platform: Tools, Approaches, and Facilities 344 8.2 Working Processing and Adaptation 349 8.2.1 Process Simulations 349 8.2.2 Process Mining Actuation and Digital Aspects Concerning Decision Support Systems Implemented by Data Mining Algorithms 350 8.3 Reverse Engineering and Self-Learning Automatic Working Piece Classification 354 8.4 Tools Supporting RE: AR and Image Processing for Size Measurement 356 8.5 RE in Micrometric Scale: RE Approach for Photonic Crystals 357 8.6 RE for the Production of Pipeline Components 361 8.7 RE in the Precision Manufacturing Process for Thin Film Devices 363 8.7.1 Ring MEMS Manufacturing 363 8.7.2 Thin Film Diamond Antenna 369 8.8 Advanced RE Processes in Industry 5.0 372 8.9 RE in Nanocomposite Production Processes 374 8.10 RE in Electronic Board Production 376 8.10.1 Transfer of the Master to the Copper Plate 378 8.10.2 Chemical Attack of Copper 378 8.10.3 Drilling and Finishing Processes 379 References 379 9 Rapid Prototyping 381 9.1 Rapid Prototyping Tools and Microscale Electronic Systems: Methodological Approaches 382 9.1.1 Photonic Crystal Pillars for Filtering and Optical Resonance 382 9.1.2 Thin Film Microelectromechanical System Prototyping and Photolithography Approach 387 9.1.3 Thin Film GHz Microstructures by the Photolithography Approach 387 9.1.4 Gas Sensing Homemade Experimental Setup for Rapid Prototyping 390 9.2 Examples of Antenna and Detection System Rapid Prototyping 392 9.2.1 GPR Antenna Design for UAV Integration System 392 9.2.2 Example of an Underground Water Leakage Detection System Integrating GPR, UAV, and Infrared Thermal Imaging: System Prototyping 397 9.2.3 Integrated Diamond Patch-Type Antennas and Applications 400 9.3 Principles of Mechanical Piece Rapid Prototyping and Innovative Materials 411 9.3.1 Example of Diamond Material Implementations 413 9.4 Rapid Prototyping and Artificial Intelligence Upgrade 415 9.5 Rapid Prototyping Oriented Toward Patent Development 418 9.5.1 Prototyping of Devices Implementing Nanoparticles 418 9.5.2 Prototyping of an Optoelectronic Device Based on a Nanocomposite Tip 418 9.5.3 DNA Lab-on-Chip 418 9.6 Nanocomposite Artificial Skin Rapid Prototyping Process 437 References 439 10 Scientific Research in Industry 445 10.1 Guidelines to Construct an Advanced Research Unit in Industry in the Electronic and Mechatronic Field 446 10.2 Guidelines to Formulate a Patent 448 10.3 Guideline to Propose Technological Advances for Public Entities and in Industry 5.0 Research Project 449 10.3.1 Setting of a Research Project of Underground Water Leakage 449 10.3.2 Setting a Research Project Involving Technologies for Hydrogeological Risk Monitoring 456 10.3.3 Setting a Research Project in Mechatronics: Production of a Diagnostic Machine by Means of Industry 5.0 Facilities 468 10.4 Innovation Process Projects: Example of a Smart Wine Factory 483 10.5 Guideline for Project Management 485 References 506 Abbreviations and Acronyms 507 Index 515

About the Author :
Alessandro Massaro is qualified as Associate Professor in Electronics and Physics. He was the winner of the Best Engineer award from the National Council of Engineers in Italy in 2018.