Оглавление
Alessandro Massaro. Electronics in Advanced Research Industries
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Electronics in Advanced Research Industries. Industry 4.0 to Industry 5.0 Advances
Preface
About the Author
1 State of the Art and Technology Innovation
1.1 State of the Art of Flexible Technologies in Industry
1.1.1 Sensors and Actuators Layer: I/O Layer
1.1.2 Agent/Firmware Layer: User Interface Layer
1.1.3 Gateway and Enterprise Service Bus Layer
1.1.4 IoT Middleware
1.1.5 Processing Layer
1.1.6 Application Layer
1.1.7 File Transfer Protocols
1.2 State of the Art of Scientific Approaches Oriented on Process Control and Automatisms
1.2.1 Architectures Integrating AI
1.2.2 AI Supervised and Unsupersived Algorithms
1.2.3 AI Image Processing
1.2.4 Production Process Mapping
1.2.5 Technologies of Industry 4.0 and Industry 5.0: Interconnection and Main Limits
1.2.6 Infrared Thermography in Monitoring Process
1.2.7 Key Parameters in Supply Chain and AI Improving Manufacturing Processes
1.3 Intelligent Automatic Systems in Industries
1.4 Technological Approaches to Transform the Production in Auto‐Adaptive Control and Actuation Systems
1.5 Basic Concepts of Artificial Intelligence
1.6 Knowledge Upgrading in Industries
References
2 Information Technology Infrastructures Supporting Industry 5.0 Facilities
2.1 Production Process Simulation and Object Design Approaches
2.1.1 Object Design of a Data Mining Algorithm: Block Functions and Parameter Setting
2.1.2 Example 1: BPM Modeling of Wheat Storage Process for Pasta Production
2.1.3 Example 2: Block Diagram Design of a Servo Valve Control and Actuation System
2.1.4 Example 3: Block Diagram of a Liquid Production System
2.1.5 Example 4: UML Design of a Programmable Logic Controller System
2.1.6 Example 5: Electronic Logic Timing Diagram
2.1.7 Example 6: AR System in Kitchen Production Process
2.1.8 Example 7: Intelligent Canned Food Production Line
2.2 Electronic Logic Design Oriented on Information Infrastructure of Industry 5.0
2.3 Predictive Maintenance: Artificial Intelligence Failure Predictions and Information Infrastructure Layout in the Temperature Monitoring Process
2.4 Defect Estimation and Prediction by Artificial Neural Network
2.4.1 Other Methodologies to Map and Read Production Failures and Defects
2.5 Defect Clustering and Classification: Combined Use of the K‐Means Algorithm with Infrared Thermography for Predictive Maintenance
2.6 Facilities of a Prototype Network Implementing Advanced Technology: Example of an Advanced Platform Suitable for Industry 5.0 Integrating Predictive Maintenance
2.7 Predictive Maintenance Approaches
2.7.1 Preventive Maintenance and Predictive Maintenance Operations in the Railway Industry
2.8 Examples of Advanced Infrastructures Implementing AI
2.9 Examples of Telemedicine Platforms Integrating Advanced Facilities
2.9.1 Advanced Telecardiology Platform
2.9.2 Advanced Teleoncology Platform
2.9.3 Multipurpose E‐Health Platform
References
3 Human–Machine Interfaces
3.1 Mechatronic Machine Interface Architectures Integrating Sensor Systems
3.1.1 Multiple Mechatronic Boards Managing Different Production Stages
3.1.2 Mechatronic Boards Managing Component Processing
3.2 Machine‐to‐Machine Interfaces: New Concepts of Industry 5.0
3.3 Production Line Command and Actuation Interfaces in Upgraded Systems
3.3.1 PLC, PAC, Industrial PC, and Improvements
3.3.2 SCADA Systems for Centralization of Data Production
3.4 McCulloch–Pitts Neurons and Logic Port for Automatic Decision‐Making Setting Thresholds
3.5 Programmable Logic Controller I/O Ports Interfacing with AI Engine
3.6 Human–Machine Interface for Data Transfer and AI Data Processing
3.7 Example of Interface Configuration of Temperature Control
3.8 AI Interfaces Oriented on Cybersecurity Attack Detection
3.9 AI Interfaces Oriented on Database Security
3.10 Cybersecurity Platform and AI Control Interface
References
4 Internet of Things Solutions in Industry
4.1 Cloud Computing IoT
4.1.1 IoT Agent
4.1.2 IoT Gateway in Smart Environments
4.1.3 Basic Elements of a Smart Industry Environment Controlling Production
4.1.3.1 Feedback Control: Basic Concepts
4.1.4 Augmented Reality Hardware and Cloud Computing Processing
4.1.5 Real‐Time Control and Actuation
4.1.6 Localization Technologies in an Industrial Environment
4.1.7 GPU Processing Units
4.1.7.1 Performance of GPUs by Processing Binary Matrices
4.2 IoT and External Artificial Intelligence Engines
4.2.1 Artificial Engines and Server Location: Artificial Intelligence and Adaptive Production
4.2.2 IoT Security Systems in the Working Environment and Implementation Aspects
4.2.3 Example of Energy Power Control and Actuation: Energy Routing and Priority Load Management for Energy Efficiency
4.2.4 Online Configurators: Cloud DSS
4.3 Blockchain and IoT Data Storage Systems
4.3.1 Blockchain Implementation Rules
4.3.2 Blockchain and IoT Production Traceability
4.4 Mechatronic Machine Interface Architectures Integrating Sensor Systems
4.5 Multiple Mechatronic Boards Managing Different Production Stages
References
5 Advanced Robotics
5.1 Collaborative Robotics in Industry and Protocols
5.1.1 Data Protocols
5.1.2 Basic Concepts of Robotic Arms and Control Improvement
5.1.3 Collaborative Exoskeleton Communication System Protocols
5.1.4 Advanced Robotics and Intelligent Automation in Manufacturing: Logic Conditions and PLC Programming
5.2 Artificial Intelligence in Advanced Robotics and Auto‐Adaptive Movement
5.2.1 General Technological Aspects about Auto‐Adaptive Motion in Advanced Robotics
5.2.1.1 Main Aspects of Electrostatic Actuators
5.2.1.2 Microelectromechanical System Electrostatic Actuators
5.2.1.3 Piezoelectric Actuators
5.2.1.4 DC Motor Actuation
5.2.1.5 Intelligent Control Integrating AI: Speed Regulation
5.2.2 Improvement of Collaborative Exoskeletons by Auto‐Adaptive Solutions Implementing Artificial Intelligence
5.3 Human–Robot Self‐Learning Collaboration in Industrial Applications and Electronic Aspects
5.3.1 DC–DC Converter
5.3.2 Voltage Source Inverter
5.3.3 Current‐Source Inverter
5.3.4 DC Voltage Source
5.3.5 Capacitor and Reactor Effects on Signal Control
5.3.6 Human‐Robot System and Learning Approaches
5.3.6.1 Example of PID Implementation of Self‐Adapting Gains
5.3.7 Unsupervised Learning Approaches
5.3.8 Soft Robotics for Intelligent Collaborative Robotics
5.4 Robotics in Additive Manufacturing
5.4.1 Additive Manufacturing in Industrial Production and Spray Technique
5.4.2 Artificial Intelligence Applications in Additive Manufacturing
5.4.3 Advanced Electronic for Design‐to‐Product Transformation: Laser Texturing Manufacturing and Artificial Intelligence
References
6 Advanced Optoelectronic and Micro‐/Nanosensors
6.1 Nanotechnology Laboratories in Industries
6.1.1 Facilities for Micro‐/Nanosensor Fabrication and Characterization
6.2 Micro‐ and Nanosensors as Preliminary Prototypes for Industry Research
6.2.1 Nanocomposite Optoelectronic Sensors and Optoelectronic Circuits for Pressure Sensors
6.2.1.1 Optical Fiber Nanocomposite Tip
6.2.2 Plasmonic Probes
6.2.3 Nanocomposite Pressure Sensor
6.2.4 Nanocomposite Sensor for Liquid Detection Systems and Fluid Loss Systems
6.2.4.1 Nanocomposite Sensor for Liquid Detection Systems Based on a Pillar‐Type Layout
6.2.4.2 Micro‐ and Nanosensors in the Monitoring of Production Processes: Leakage Monitoring
6.2.5 Examples of Digital MEMS/NEMS Sensors: Technological Aspects and Applications
6.2.5.1 Thin Film MEMS
6.2.5.2 Nanoprobes for Medical Imaging
6.2.5.3 Diamond Thin Film Devices: Sensing Improvements
6.3 Multisensor Systems and Big Data Synchronization of Micro‐/Nanoprobes
References
7 Image Vision Advances
7.1 Defect Classification by Artificial Intelligence and Data Processor Units
7.1.1 Artificial Intelligence Algorithms and Automatism for Defect Classification: Case Study of Tire Production
7.1.2 Welding Classification and Nondestructive Testing Suitable for the Quality Check
7.1.2.1 Watershed Image Segmentation and Automatic Welding Defect Classification
7.1.3 Encoding and Decoding Circuits in Artificial Intelligence Data Processing
7.1.4 Electronic Logic Port Implementations: Pixel Matrix Logic Condition
7.2 Image Vision Architectures and Electronic Design
7.2.1 Infrared Thermography Monitoring Industrial Processes
7.2.1.1 Welding Image Vision Processing and Architecture Design: Radiometric Post Processing
7.2.2 Electronic and Firmware for Inline Image Monitoring Systems: Hole Precision in Milling Quality Processes
7.2.3 Image Vision and Predictive Maintenance by Artificial Intelligence
7.2.3.1 Profilometer for Image Vision
7.2.3.2 In‐Line 3D Image Vision AI System Integrating Profilometer and Image Processing
7.2.4 Augmented Reality Systems and Artificial Neural Networks: Image Vision Supporting Production Processes
7.2.5 Infrared Thermography Circuit Design and Automated System
7.3 Image Segmentation and Image Clustering
7.3.1 Electronic and Firmware for In‐Line Monitoring Systems: Camera Connection
7.3.2 Image Segmentation and Clustering Techniques: Automated In‐Line Monitoring Systems
7.3.3 Circuit Timing In‐Line Monitoring and Data Storage Systems
7.3.4 Image Segmentation in Product Quality Monitoring: Snake Contour Approach
7.3.5 Advanced Image Clustering: K‐Means Applied to Radiometric Images
7.4 Image Segmentation for Food Defect Detection
7.5 Random Forest Pixel Classification
References
8 Electronic and Reverse Engineering
8.1 Reverse Engineering Systems and Mechanical Precision
8.1.1 Reverse Engineering Platform: Tools, Approaches, and Facilities
8.2 Working Processing and Adaptation
8.2.1 Process Simulations
8.2.2 Process Mining Actuation and Digital Aspects Concerning Decision Support Systems Implemented by Data Mining Algorithms
8.3 Reverse Engineering and Self‐Learning Automatic Working Piece Classification
8.4 Tools Supporting RE: AR and Image Processing for Size Measurement
8.5 RE in Micrometric Scale: RE Approach for Photonic Crystals
8.6 RE for the Production of Pipeline Components
8.7 RE in the Precision Manufacturing Process for Thin Film Devices
8.7.1 Ring MEMS Manufacturing
8.7.2 Thin Film Diamond Antenna
8.8 Advanced RE Processes in Industry 5.0
8.9 RE in Nanocomposite Production Processes
8.10 RE in Electronic Board Production
8.10.1 Transfer of the Master to the Copper Plate
8.10.2 Chemical Attack of Copper
8.10.3 Drilling and Finishing Processes
References
9 Rapid Prototyping
9.1 Rapid Prototyping Tools and Microscale Electronic Systems: Methodological Approaches
9.1.1 Photonic Crystal Pillars for Filtering and Optical Resonance
9.1.2 Thin Film Microelectromechanical System Prototyping and Photolithography Approach
9.1.3 Thin Film GHz Microstructures by the Photolithography Approach
9.1.4 Gas Sensing Homemade Experimental Setup for Rapid Prototyping
9.2 Examples of Antenna and Detection System Rapid Prototyping
9.2.1 GPR Antenna Design for UAV Integration System
9.2.2 Example of an Underground Water Leakage Detection System Integrating GPR, UAV, and Infrared Thermal Imaging: System Prototyping
9.2.3 Integrated Diamond Patch‐Type Antennas and Applications
9.3 Principles of Mechanical Piece Rapid Prototyping and Innovative Materials
9.3.1 Example of Diamond Material Implementations
9.4 Rapid Prototyping and Artificial Intelligence Upgrade
9.5 Rapid Prototyping Oriented Toward Patent Development
9.5.1 Prototyping of Devices Implementing Nanoparticles
9.5.2 Prototyping of an Optoelectronic Device Based on a Nanocomposite Tip
9.5.3 DNA Lab‐on‐Chip
9.6 Nanocomposite Artificial Skin Rapid Prototyping Process
References
10 Scientific Research in Industry
10.1 Guidelines to Construct an Advanced Research Unit in Industry in the Electronic and Mechatronic Field
10.2 Guidelines to Formulate a Patent
10.3 Guideline to Propose Technological Advances for Public Entities and in Industry 5.0 Research Project
10.3.1 Setting of a Research Project of Underground Water Leakage
10.3.2 Setting a Research Project Involving Technologies for Hydrogeological Risk Monitoring
10.3.3 Setting a Research Project in Mechatronics: Production of a Diagnostic Machine by Means of Industry 5.0 Facilities
10.4 Innovation Process Projects: Example of a Smart Wine Factory
10.5 Guideline for Project Management
References
Abbreviations and Acronyms
Index. a
b
c
d
e
f
g
h
i
j
k
l
m
n
o
p
q
r
s
t
u
v
w
x
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