Electronics in Advanced Research Industries E-Book

Alessandro Massaro

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 904

Veröffentlichungsjahr: 2021

Beschreibung

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.

Leseprobe

Table of Contents

Cover

Title Page

Copyright Page

Dedication Page

Preface

About the Author

1 State of the Art and Technology Innovation

1.1 State of the Art of Flexible Technologies in Industry

1.2 State of the Art of Scientific Approaches Oriented on Process Control and Automatisms

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

2.2 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

2.5 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

2.8 Examples of Advanced Infrastructures Implementing AI

2.9 Examples of Telemedicine Platforms Integrating Advanced Facilities

References

3 Human–Machine Interfaces

3.1 Mechatronic Machine Interface Architectures Integrating Sensor Systems

3.2 Machine‐to‐Machine Interfaces: New Concepts of Industry 5.0

3.3 Production Line Command and Actuation Interfaces in Upgraded Systems

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

4.2 IoT and External Artificial Intelligence Engines

4.3 Blockchain and IoT Data Storage Systems

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

5.2 Artificial Intelligence in Advanced Robotics and Auto‐Adaptive Movement

5.3 Human–Robot Self‐Learning Collaboration in Industrial Applications and Electronic Aspects

5.4 Robotics in Additive Manufacturing

References

6 Advanced Optoelectronic and Micro‐/Nanosensors

6.1 Nanotechnology Laboratories in Industries

6.2 Micro‐ and Nanosensors as Preliminary Prototypes for Industry Research

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

7.2 Image Vision Architectures and Electronic Design

7.3 Image Segmentation and Image Clustering

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

8.2 Working Processing and Adaptation

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

8.8 Advanced RE Processes in Industry 5.0

8.9 RE in Nanocomposite Production Processes

8.10 RE in Electronic Board Production

References

9 Rapid Prototyping

9.1 Rapid Prototyping Tools and Microscale Electronic Systems: Methodological Approaches

9.2 Examples of Antenna and Detection System Rapid Prototyping

9.3 Principles of Mechanical Piece Rapid Prototyping and Innovative Materials

9.4 Rapid Prototyping and Artificial Intelligence Upgrade

9.5 Rapid Prototyping Oriented Toward Patent Development

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

10.4 Innovation Process Projects: Example of a Smart Wine Factory

10.5 Guideline for Project Management

References

Abbreviations and Acronyms

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Spectral ranges of infrared technology.

Table 1.2 Advantages and disadvantages of network typologies.

Table 1.3 Main specifications of sensors used for traceability.

Table 1.4 Main specifications of sensor transmission protocols.

Table 1.5 Other protocols usable in industry.

Table 1.6 Classification of machine learning algorithms.

Table 1.7 Possible limits of advanced technologies and technical risks.

Table 1.8 Goals achievable in manufacturing industry.

Table 1.9 Example of a correlation matrix for a model with five attributes (

Table 1.10 Industrial sectors and advanced application fields.

Chapter 2

Table 2.1 Functions of objects constituting a data processing workflow.

Table 2.2 Algorithm performance indicators and related formulas.

Table 2.3 Functions of actors of the AR system.

Table 2.4 Limits of the AR system.

Table 2.5 Technologies and examples of implementable logics.

Table 2.6 Example of a 4M map.

Table 2.7 Types of control charts.

Table 2.8 Main specifications of the AR communication system.

Table 2.9 Maintenance definitions.

Table 2.10 Predictive actions for a polishing process.

Table 2.11 Best measured main parameters for locomotive brake tests.

Table 2.12 Example of advanced information platforms integrating artificial ...

Table 2.13 Main specification of the telecardiology network.

Chapter 3

Table 3.1 Risks arising from technological innovation.

Table 3.2 Examples of wireless sensor protocols.

Table 3.3 Main specifications of communication protocols.

Table 3.4 Logic conditions and actuation of the production system illustrate...

Table 3.5 Example of a dataset used for the automated classification of the ...

Table 3.6 Calculated bias and weight values for each neuron of the hidden la...

Chapter 4

Table 4.1 Application of basic sensors in industry.

Table 4.2 IoT sensors in a secure working environment.

Table 4.3 Requirement for intelligent load switching.

Table 4.4 Electrical abnormalities.

Table 4.5 Example of online configuration for a platform controlling machine...

Table 4.6 Example of online configuration for a platform controlling a railw...

Table 4.7 Example of online configuration for a platform controlling a road ...

Table 4.8 Example of online configuration for a platform controlling a quarr...

Table 4.9 Example of online configuration for a telemedicine platform.

Table 4.10 Blockchain class definition.

Chapter 5

Table 5.1 The main classifications for industrial robots.

Table 5.2 Robot task classification.

Table 5.3 Robot protocols.

Table 5.4 AI facilities applied in additive manufacturing.

Chapter 6

Table 6.1 Main properties of the PDMS polymer.

Table 6.2 Main properties of the PDMS‐Au material.

Table 6.3 Oil concentrations associated with the oil droplets.

Chapter 7

Table 7.1 Encoder 8×3 inputs and outputs.

Table 7.2 Data processed by the network model in Figure 7.9.

Table 7.3 Critical aspects of infrared thermography implementation in indust...

Chapter 8

Table 8.1 RE phases.

Table 8.2 Example of workstation characteristics implementing RE facilities ...

Table 8.3 Advantages of process simulations in RE.

Table 8.4 RE procedure for design optimization of a micrometric optical devi...

Table 8.5 Ring and helicoidal MEMS layout versus Mo top layer thickness.

Table 8.6 Possible automatisms in RE.

Chapter 9

Table 9.1 Overview of the main rapid prototyping tools.

Table 9.2 Description of UAV elements.

Table 9.3 Scheme of the patent.

Table 9.4 Scheme of the patent.

Table 9.5 Scheme of the patent.

Chapter 10

Table 10.1 Example of a scheme of the technical proposal.

Table 10.1.1 Code of classes or requirements.

Table 10.1.2 Use case template scheme.

Table 10.1.3 Example of a framework describing technological advantages.

Table 10.1.4 Comparison of patent and project facilities.

Table 10.1.5 Summary of the tasks assigned to professional units.

Table 10.1.6 Example of a training planning.

Table 10.1.7 Technology transfer hours for the specific modules.

Table 10.1.8 Example of a Gantt diagram.

Table 10.1.9 Project milestones.

Table 10.2 Example of a business plan.

Table 10.2.1 Basic scheme of SWOT analysis.

Table 10.2.2 Template of the investment plan.

Table 10.2.3 Template of the revenue plan.

Table 10.3 Example of a simplified use case table.

Table 10.4 Example of a scheme of the business plan.

Table 10.5 Example of SWOT analysis for a water leakage detections system.

Table 10.6 Project setting approach.

Table 10.6.1 Summary of the main partner information.

Table 10.6.2 Contact information of the partners involved in the project.

Table 10.6.3 Project financial prospect.

Table 10.6.4 Relationships between project goals and associated technology.

Table 10.6.5 Interactions between actors involved in the project.

Table 10.6.6 Example of a testing plan.

Table 10.7 Classified results.

Table 10.8 Outputs and deliverables related to each work package.

Table 10.9 Milestone plan.

Table 10.10 Research and development project form.

Table 10.11 Model risk example.

Table 10.12 Work package typologies.

Table 10.13 Description of deliverables.

Table 10.14 Milestone definitions.

Table 10.15 Modules constituting a generic prototype.

Table 10.16 Modules of a generic production line.

Table 10.17 Activities of results dissemination.

Table 10.18 TRL objectives.

Table 10.19 Complementary partners.

Table 10.20 NPV and IRR indicators estimated in five years: business plan mo...

Table 10.21 Subproject template.

Table 10.22 Information report template.

Table 10.23 Budget template for work package.

Table 10.24 Cost/benefits template.

Table 10.25 Risk management template.

Table 10.26 Issues template.

Table 10.27 Action template.

Table 10.28 Deliverable template.

Table 10.29 Milestone template.

Table 10.30 Work breakdown structure template.

Table 10.31 Work breakdown structure weekly structured template.

Table 10.32 Contact information template.

Table 10.33 Roles and responsibilities template.

Table 10.34 Resource assignment template.

Table 10.35 Human resource time sheet template.

Table 10.36 Constraints template.

Table 10.37 Decision template.

Table 10.38 Communication plan template.

Table 10.39 Stakeholder template.

Table 10.40 Opportunities template.

Table 10.41 Rule change template.

Table 10.42 Deliverable acceptance template.

Table 10.43 Pert analysis template.

Table 10.44 Delphi template.

Table 10.45 Initial project evaluation template.

Table 10.46 Human resource loading template.

Table 10.47 Project quality metrics template.

List of Illustrations

Chapter 1

Figure 1.1 Example of network configurations: (a) point to point connection;...

Figure 1.2 Example of hybrid extended star network and failure system reconf...

Figure 1.3 Layers of technologies related to an advanced technology.

Figure 1.4 Hierarchical scheme of the software in Industry 5.0.

Figure 1.5 Advanced PLC architecture in Industry 5.0: central processing uni...

Figure 1.6 Scheme of a pick and place automated system for defect removal, b...

Figure 1.7 Image processing and intelligent system in production processes....

Figure 1.8 Interoperability of different technologies involved in Industry 4...

Figure 1.9 Information system interconnecting enabling technologies.

Figure 1.10 (a) Infrared thermal camera signals. (b) AOV and FOV simplified ...

Figure 1.11 Artificial intelligence integrated into the supply chain.

Figure 1.12 Multilevel structure of manufacturing industry processes integra...

Figure 1.13 Artificial intelligence feedback system in manufacturing process...

Figure 1.14 Block diagram of adaptive solutions in advanced manufacturing.

Figure 1.15 Scheme of horizontal, vertical and end to end integration in Ind...

Figure 1.16 CAD and CNC interconnected by a feedback system.

Figure 1.17 (a) Simple ANN. (b) DL neural network.

Figure 1.18 (a) Feedback system minimizing calculation error in the training...

Figure 1.19 Basic mathematical functions defining activation functions.

Figure 1.20 Supervised artificial network model: partitioning of the availab...

Figure 1.21 Algorithm classification and Industry 5.0 facilities.

Figure 1.22 (a) Regression analysis, (b) data classification, and (c) data c...

Figure 1.23 Ensemble method and classification.

Figure 1.24 Ensemble method and classification.

Figure 1.25 (a) LSTM unit cell. (b) LSTM network and its memory.

Figure 1.26 Knowledge gain in industry.

Chapter 2

Figure 2.1 Main BPM symbols.

Figure 2.2 Main UML symbols.

Figure 2.3 Block diagram: digital production process control integrating the...

Figure 2.4 Workflow implementing a data mining algorithm with objects (block...

Figure 2.5 (a) ROC curve. (b) Threshold criterion.

Figure 2.6 Loss function: overfitting and underfitting conditions and perfor...

Figure 2.7 Examples of MAE theoretical trends versus neural network paramete...

Figure 2.8 (a) Layout controlling silo loading. (b) Implemented workflow of ...

Figure 2.9 Feedback system controlling and actuating a load by means of a se...

Figure 2.10 Block diagram model of a liquid production system implementing A...

Figure 2.11 UML class diagram implementing AI for a PLC system.

Figure 2.12 (a) D flip‐flop symbol. (b) Equivalent circuit adopting logic po...

Figure 2.13 UML use case diagram of AR technology adopted in kitchen product...

Figure 2.14 HoQ diagram matching a laser scanner with AR technologies.

Figure 2.15 AI switching production lines for canned food production. The pr...

Figure 2.16 Symbols, truth tables, and events characterizing logic ports. A ...

Figure 2.17 Equivalent circuits of (a) AND, (b) OR, (c) NOT, (d) NOR, (e) NA...

Figure 2.18 (a) McCulloch–Pitts neuron model with binary input

b

i

, weights

w

Figure 2.19 Infrastructure of predictive maintenance applied to milk pasteur...

Figure 2.20 (a) KNIME workflow implementing MLP neural network. (b) MLP neur...

Figure 2.21 (a) KNIME network implementing an artificial neural network. (b)...

Figure 2.22 Defect chart reading procedure. Unstable regions are provided by...

Figure 2.23 Clusters and related centroids.

Figure 2.24 Parameters for the estimation of the Euclidean distance.

Figure 2.25 Architecture implementing Industry 4.0 and Industry 5.0 faciliti...

Figure 2.26 Maintenance plan variation based on prediction results.

Figure 2.27 Examples of typical alerting signals relating to the failure con...

Figure 2.28 Workflow defining predictive maintenance for pieces in the railw...

Figure 2.29 IQ trends: initial curve, real measured curve, and updated curve...

Figure 2.30 System architecture: upgrade of a telecardiology network.

Figure 2.31 System architecture of a teleoncology platform: functional schem...

Figure 2.32 E‐health system architecture of a multipurpose health platform....

Figure 2.33 Workflow of combined assistance services.

Chapter 3

Figure 3.1 Human–machine interface managing multiple mechatronic boards; mod...

Figure 3.2 HMIs managing multiple mechatronic boards.

Figure 3.3 M2M concept in Industry 5.0.

Figure 3.4 Advanced PLC integrated system: feedback system by ANN.

Figure 3.5 Standalone decision maker based on an ANN programming PLC system....

Figure 3.6 Client–server connection scheme via OPC‐UA protocol (HW, hardware...

Figure 3.7 Architecture of a SCADA upgraded system.

Figure 3.8 CIM pyramid upgraded by AI.

Figure 3.9 Scheme of the connection between the PLC and temperature transduc...

Figure 3.10 (a) EtherCAT protocol specifications (HDR, header). (b) Sercos I...

Figure 3.11 Industrial oven temperature control system and automated thermal...

Figure 3.12 SCADA central system interconnected with OPC, RTU, PLC, and SCAD...

Figure 3.13 Modeling of McCulloch–Pitts neurons.

Figure 3.14 Modeling of McCulloch–Pitts neuron by bias signal.

Figure 3.15 (a) Single‐layer perceptron network. (b) Multilayered perceptron...

Figure 3.16 (a) NOT logic and its implementation (b) AND port and its implem...

Figure 3.17 (a) NAND Logic and its implementation. (b) XOR logic and its imp...

Figure 3.18 Threshold condition of the AND port (a), OR port (b), NAND port ...

Figure 3.19 General decision boundaries due to a data input classification....

Figure 3.20 Data input mapping and correct production or production failure ...

Figure 3.21 KNIME workflow implementing MLP model predicting correct product...

Figure 3.22 MLP network adopted for the temperature classification (the numb...

Figure 3.23 Generic class defined by two input neurons and one bias input.

Figure 3.24 Multidimension error surface.

Figure 3.25 (a) Production line composed of two main stages monitored by sen...

Figure 3.26 Parallel to serial register.

Figure 3.27 Digital I/O PLC values using synchronized shift registers storin...

Figure 3.28 (a) Scheme of flash converter. (b) Example of a 3‐bit quantized ...

Figure 3.29 GUI implementing delay modules for data migration from SQL to No...

Figure 3.30 Network integrating NoSQL and AI technologies.

Figure 3.31 Data flow inherent data recovery from an ambient temperature sen...

Figure 3.32 Advanced architecture linking the cloud environment with local c...

Figure 3.33 (a) KNIME workflow implementing the Tree Ensemble algorithm clas...

Figure 3.34 Priority levels of information.

Figure 3.35 Access control interface model.

Figure 3.36 IDS architecture based on a CNN classifier.

Figure 3.37 DB access intrusion system based on attack signature (SDB, signa...

Figure 3.38 DB security model based on an AI interface.

Figure 3.39 Architecture of a DB security system constituted by an inference...

Figure 3.40 Inference detection system coupled with the AI engine.

Figure 3.41 (a) Cross platform architecture oriented on cybersecurity, virtu...

Figure 3.42 Platform functions involved in Figure 3.41a.

Chapter 4

Figure 4.1 Architecture model of a company information system integrating Io...

Figure 4.2 Domain expert and production manager roles and relationships in a...

Figure 4.3 Relationship between production manager, message broker, IoT agen...

Figure 4.4 Architecture of a IoT smart manufacturing framework integrating a...

Figure 4.5 (a) Rotary encoder detection system. (b) Front view of a basic en...

Figure 4.6 Basic principle of potentiometer and AI wiper control.

Figure 4.7 (a) LVDT 3D configuration. (b) Structure and basic principle of t...

Figure 4.8 Metallic strain gauge.

Figure 4.9 Load cell with a Roberval mechanism.

Figure 4.10 Wheatstone bridge for load cell.

Figure 4.11 Basic scheme of a laser detector.

Figure 4.12 Measured accelerations in the (

x,y

) plane of a vibrating product...

Figure 4.13 Measured accelerations along the

z‐

axis of a vibrating pro...

Figure 4.14

Konstanz Information Miner

(

KNIME

) workflow predicting accelerat...

Figure 4.15 Application of the acceleration signal processing for production...

Figure 4.16 (a) Feedback control and AI corrective action. (b) Trajectory of...

Figure 4.17 Basic feedback control system.

Figure 4.18 Desirable gain characteristic (

ω

gc

, desirable gain crossove...

Figure 4.19 Architecture of a multivisor AR architecture.

Figure 4.20 Load balancing of the workload by parallel data flow involving t...

Figure 4.21 Model representing quasi real‐time data processing involving sen...

Figure 4.22 Automatism in sensor detection: (a) nanocomposite optical probe ...

Figure 4.23 Quasi real‐time data processing of a sensing/actuation process m...

Figure 4.24 Architecture of quasi real‐time data processing involving cloud ...

Figure 4.25 UML sequence diagram describing the sensing and actuation proces...

Figure 4.26 (a) Production line layout and drone monitoring in dangerous are...

Figure 4.27 (a) CPU structure. (b) GPU structure and (c) related functions (...

Figure 4.28 Execution time versus TPB for the exponentiation of a binary dat...

Figure 4.29 Execution time versus the exponentiation

n

of a Float64 data mat...

Figure 4.30 Execution time versus the binary matrix dimension.

Figure 4.31 Comparison of GPU and CPU execution time versus the power of the...

Figure 4.32 Comparison of GPU and CPU execution time versus the size of the ...

Figure 4.33 Architecture integrating AI cloud server and IoT device: Industr...

Figure 4.34 Architecture of an energy router system.

Figure 4.35 (a) Linear prediction of energy consumption by linear regression...

Figure 4.36 Thermogram of a bridge acquired by UAV. Inset: UAV adopted for t...

Figure 4.37 Thermogram of Figure 4.36 setting over a threshold of 32 °C (DSS...

Figure 4.38 Thermogram of a railway infrastructure.

Figure 4.39 (a) Radargram of part of a bridge detected by a UAV equipped wit...

Figure 4.40 Blockchain model and transactions integrating an AI controller....

Figure 4.41 Blockchain architecture model.

Figure 4.42 Blockchain implementation concerning a full production process d...

Figure 4.43 Example of architecture implementing facilities improving dynami...

Figure 4.44 Mechatronic interface board controlling three motor axes of a ro...

Figure 4.45 Multiple mechatronic boards managing a production line layout ch...

Chapter 5

Figure 5.1 (a) Robotic arm with joints connected relatively to each other. (...

Figure 5.2 (a) Coordinate system determining the rotation matrix in the (

x,y

Figure 5.3 (a) 3D coordinate system describing 3D translation. (b) 3D coordi...

Figure 5.4 Functional scheme of a robotic arm controlled by sensors, image v...

Figure 5.5 Exoskeleton configurations in industry and applied forces: (a) ar...

Figure 5.6 Exoskeleton communication model integrating AI (S, pressure and t...

Figure 5.7 PLC scheme enabling AI instructions (HW, hardware).

Figure 5.8 PLC basic program and related table description: AND logic implem...

Figure 5.9 PLC basic program and related table description: OR logic impleme...

Figure 5.10 PLC basic program and related table description: hybrid AND/OR l...

Figure 5.11 PLC basic program and related table description: hybrid AND/OR l...

Figure 5.12 Block diagram of an electrical actuator.

Figure 5.13 (a) Polarization charge of a capacitor. (b) Implementation of th...

Figure 5.14 Implementation of the electrostatic actuator.

Figure 5.15 Electrostatic actuator in MEMS configuration.

Figure 5.16 (a) Basic principle of piezoelectric actuation. (b) Strain cause...

Figure 5.17 Schematic of a piezoelectric actuator for large strain effect.

Figure 5.18 Multi‐layer plate piezoelectric actuator model.

Figure 5.19 DC motor by magnetic field: (a) basic principle of electromagnet...

Figure 5.20 Equivalent circuit of a DC motor.

Figure 5.21 Operation mode of a DC motor in four quadrants.

Figure 5.22 Induction motor: (a) squirrel cage type conductor; (b)–(e) furth...

Figure 5.23 (a) Mechanical scheme and (b) electrical model of a DC motor.

Figure 5.24 Advanced controlled system of a DC motor by AI algorithm impleme...

Figure 5.25 Systemic model of the DC motor feedback controlled by the AI mod...

Figure 5.26 (a) Shunt motor and AI controlling electrical current. (b) Theor...

Figure 5.27 (a) Series motor and AI controlling resistance. (b) Theoretical ...

Figure 5.28 (a) DC shunt motor: modeling of the field flux control method. (...

Figure 5.29 (a) DC series motor modeling. (b) Theoretical trend of the motor...

Figure 5.30 Adaptive‐control diagram to automatically adapt to worker's desi...

Figure 5.31 (a) Step down chopper circuit. (b) Voltage and current. (c) Step...

Figure 5.32 (a) Example of IGBT and of

n

‐channel MOSFET switch equivalence; ...

Figure 5.33 Three‐phase VSI.

Figure 5.34 (a) Scheme representing the basic principle of electrical curren...

Figure 5.35 (a) SCR configuration and equivalences. (b) GTO symbols and circ...

Figure 5.36 On‐state caused by gate current: (a) equivalent circuit implemen...

Figure 5.37 (a) SCR and (b) GTO

I

–

V

characteristics.

Figure 5.38 (a) Normal switching configuration; (b) PWM signal modulation co...

Figure 5.39 Example of PWM signal modulation.

Figure 5.40 (a) Current‐source inverter circuit; (b) signals of the reversal...

Figure 5.41 Scheme of a three‐phase CSI.

Figure 5.42 (a) Uncontrolled converter configuration; (b) signal processing ...

Figure 5.43 (a) Controlled converter; (b) signal processing of the converter...

Figure 5.44 (a) Half‐wave rectifier circuit basic scheme; (b) half‐wave rect...

Figure 5.45 (a) Voltage‐source inverter; (b) current‐source inverter.

Figure 5.46 (a) Main scheme of an advanced robotic control by combining a PI...

Figure 5.47 Example of an AI controlled system adjusting an arrow trajectory...

Figure 5.48 (a) PID implementation circuit layout tuned by AI commands; (b) ...

Figure 5.49 (a) Unsupervised process by selecting the object inline in two s...

Figure 5.50 (a) Feedback loop systems. (b) Feedback system including AI feed...

Figure 5.51 Pulsed spray technique in smart additive manufacturing controlle...

Figure 5.52 Laser texturing technique controlled by AI.

Chapter 6

Figure 6.1 (a) Scheme of the AFM‐SCM circuital approach. (b) Sketch of the e...

Figure 6.2 (a) Microscope image of NDs deposited on a glass layer. TEM image...

Figure 6.3 Post‐processed TEM images: (a) 3D image processing of silica NPs;...

Figure 6.4 MWPECVD reactor and plasma generated during diamond film growth....

Figure 6.5 Enhanced light of an optical fiber end embedded in a PDMS‐Au tip ...

Figure 6.6 PDMS‐Au scattering efficiency versus the working wavelength for d...

Figure 6.7 (a) Unit cell: modeling of PDMS with monodisperse GNs. (b) Zoomed...

Figure 6.8 FEM simulations: (a) light propagating in the PDMS material; (b) ...

Figure 6.9 (a) Metallic wedge in dielectric materials. (b) Transmission line...

Figure 6.10 (a) Cylindrical coordinate system. (b) Spherical coordinate syst...

Figure 6.11 Metallic permittivity theoretical trend: (a) gold; (b) silver; (...

Figure 6.12 STRD theoretical near field radiation pattern for a gold metalli...

Figure 6.13 90° metallic wedge: total TE

z

electric field theoretical trend v...

Figure 6.14 Optical antenna as micrometric aperture in a parallel‐plate wave...

Figure 6.15 Basic scheme of pressure sensor (longitudinal section): (a) tape...

Figure 6.16 (a) PDMS‐Au robotic finger (tactile pressure sensor). (b) Light ...

Figure 6.17 (a) PDMS‐Au tip of the optical pressure sensor for robotic finge...

Figure 6.18 Small notch sensing approach and signal detection principle and

Figure 6.19 PDMS‐Au used for surveillance security systems. (a) Schematic co...

Figure 6.20 PDMS‐Au 2×2 matrix pillar‐type sensor layout for liquid detectio...

Figure 6.21 PDMS‐Au 2×2 matrix pillar‐type layout. The height of a single pi...

Figure 6.22 (a) AFM image: topography indicating gold fillers in a PDMS matr...

Figure 6.23 Absorbance trend of a PDMS/PDMS‐Au bulk type sample. Inset: radi...

Figure 6.24 Total electric field confinement: sketch of a simulation for a P...

Figure 6.25 2 × 2 PDMS‐Au matrix: variation of the transmitted intensity by ...

Figure 6.26 Basic principle of light scattering.

Figure 6.27 Sensor optical spectra: transmitted optical intensities for diff...

Figure 6.28 Implementation of the sensor in a robotic finger.

Figure 6.29 Microwave MEMS pressure sensor: (a) masks used for photolithogra...

Figure 6.30 (a) Ring MEMS and (b) zooming of the base of the antenna.

Figure 6.31 Electromagnetic absorbing material for antenna measurements.

Figure 6.32 Three‐dimensional model of silica NPs embedded in a lymph node....

Figure 6.33 (a) Multilayer model of an ultrasound wave propagated in human t...

Figure 6.34 Unit cell model of in the mediastinum lymph node.

Figure 6.35 (a) Theoretical normalized power spectrum of the diffracted wave...

Figure 6.36 TEM image of synthetized silica NPs (inset: zooming of some sili...

Figure 6.37 Example of functionalized silica NP synthesis by MPTS.

Figure 6.38 Example of functionalized silica NP synthesis by TEOS.

Figure 6.39 Current density

J

calculation characterizing insulation behavior...

Figure 6.40 Measured current of diamond film growth on a silicon substrate....

Figure 6.41 (a) A piece of commercial substrate. (b) Example of a ND layer d...

Figure 6.42

I

–

V

characteristic of a ND sprayed layer deposited on a silicon ...

Chapter 7

Figure 7.1 (a) Setup for defect monitoring of tire assembly. (b) K‐means alg...

Figure 7.2 Architecture of the stack of the neural network used for defect d...

Figure 7.3 Flowchart of a full approach for tire quality check and defect mo...

Figure 7.4 (a) Watershed and geometrical analogy of local minima and local m...

Figure 7.5 Flowchart modeling for welding defect check.

Figure 7.6 Sequence diagram of the image vision system based on image segmen...

Figure 7.7 Encoding and decoding enabling AI algorithm processing.

Figure 7.8 (a) Digital circuit configuration of an encoder. (b) Black box mo...

Figure 7.9 Algorithm running into an AI processor (AI engine).

Figure 7.10 (a) Digital circuit configuration of a decoder. (b) Black box mo...

Figure 7.11 Pixel matrix of an image subset.

Figure 7.12 AND logic ports implementing the feature in Figure 7.11.

Figure 7.13 3D image processing enhancing welding defect. (a) Infrared therm...

Figure 7.14 Architecture model of the adopted techniques used for quality pr...

Figure 7.15 (a) Image vision architecture of a system checking hole precisio...

Figure 7.16 (a) Block diagram modeling the profilometer image vision system....

Figure 7.17 Architecture of detection system integrating AI and 3D technolog...

Figure 7.18 AR architecture improving production processes.

Figure 7.19 Infrastructure integrating infrared camera circuits.

Figure 7.20 Thermal image of conveyor belt rollers showing areas of anomalou...

Figure 7.21 (a) Thermal image of conveyor belt rollers in gray scalebar. (b)...

Figure 7.22 Ideal dynamic extraction of the active contour snake method.

Figure 7.23 Snake contour plot applied to a radiometric image: dynamic conve...

Figure 7.24 Snake contour plot applied to the image of a processed metallic ...

Figure 7.25 (a) K‐means image processing calculated for a cluster number of ...

Figure 7.26 Theoretical areas of extracted contours versus the defect number...

Figure 7.27 (a) 3D image reconstruction of vegetables with defects. (b) 3D i...

Figure 7.28 (a) Thermogram of a meat product (inset: original image of the p...

Figure 7.29 Theoretical trend of minimum temperature indicating risk regions...

Figure 7.30 Image processing procedure based on pixel features training and ...

Figure 7.31 (a) Original image of a micrometric aperture with the definition...

Figure 7.32 (a) TEM image of silica nanoparticles. (b) Class definition iden...

Figure 7.33 (a) Thermogram of a conveyor belt carpet. (b) Feature extraction...

Chapter 8

Figure 8.1 Example of a full RE platform.

Figure 8.2 DFX model defining RE advanced production.

Figure 8.3 Example of a flexible production line oriented toward Industry 5....

Figure 8.4 (a) Process mining and a fully integrated DT data flow selecting ...

Figure 8.5 Measured, theoretical and predicted values of hole diameters.

Figure 8.6 Feedback system updating tolerances and machine parameters during...

Figure 8.7 Architecture of a smart visor used for piece measurement with the...

Figure 8.8 RE applied to PC structures: (a) 3D reconstruction of a SEM image...

Figure 8.9 Classification and automatization of the best choice of a pipelin...

Figure 8.10 SEM images of damaged ring MEMS: (a–c) effects of a high voltage...

Figure 8.11 Technological phase for freestanding ring MEMS: (a–h) phases of ...

Figure 8.12 (a) Torsion allowing helicoidal layout. (b) SEM image of an heli...

Figure 8.13 Via hole connection of the top metallic layer.

Figure 8.14 (a) Cross section of the coaxial feeding layout exciting by THz ...

Figure 8.15 (a) Front view and (b) side view of a three‐point connection lay...

Figure 8.16 Photograph of the diamond planar antenna sensor. Inset: optical ...

Figure 8.17 Deformation displacement along the

x‐

and

y‐

directi...

Figure 8.18 Measured

S

11

responses of different antenna prototypes. Inset: p...

Figure 8.19 Advanced RE processes in Industry 5.0: production of a new senso...

Figure 8.20 First prototype of PDMS‐Au pressure sensor. (a) side view; (b) s...

Figure 8.21 Second prototype of PDMS‐Au pressure sensor. (a) side view; (b) ...

Figure 8.22 RE: jumper solution.

Figure 8.23 Thickness trend versus electrical current.

Chapter 9

Figure 9.1 Example of a 3D CAD modeling of an optoelectronic device based on...

Figure 9.2 Absorbance enhancing the filtering behavior of the pillar type st...

Figure 9.3 Measured transmittivity of the periodic pillar structure (inset: ...

Figure 9.4 (a) Top view of the pillar layout (2D plane distribution of pilla...

Figure 9.5 Example of 3D CAD modeling of a MEMS pressure sensor: (a) perspec...

Figure 9.6 Simplified modeling and FEM simulation of the periodic structure ...

Figure 9.7 (a) Printed mask on a plastic paper to be applied for bromograph ...

Figure 9.8 Microantenna patch‐type layouts: (a) layout with holes; (b) layou...

Figure 9.9 (a–d) Diffraction effect: preliminary profiles of integrated gold...

Figure 9.10 Basic experimental setup for gas sensing detection (inset: photo...

Figure 9.11 (a) Scheme experimental setup for gas mixing sensing detection (...

Figure 9.12 (a) Example of a layout of Vivaldi antennas and (b) reflection c...

Figure 9.13 (a) UAV equipped with sensors. (b) Electronic scheme of the comp...

Figure 9.14 Geometrical model for GPR signal transmission and reception, det...

Figure 9.15 Scheme of ready to use prototype technology for underground wate...

Figure 9.16 Engineered processes for aqueduct inspection activities.

Figure 9.17 (a) Radargram detecting water losses in a pipeline, indicating l...

Figure 9.18 (a) 3D perspective of the designed diamond antenna. (b) Side vie...

Figure 9.19 (a) Dimensions in millimeter scale of the antenna layout; (b) eq...

Figure 9.20 (a) Comparison of the

S

11

signals between numerical and experime...

Figure 9.21 2D FEM normalized radiation patterns calculated by fixing

θ

Figure 9.22 2D FEM normalized radiation patterns calculated by fixing

θ

Figure 9.23 Examples of technologies for the realization of diamond antenna....

Figure 9.24 (a) NDs sprayed on the antenna layout and (b) microscope image o...

Figure 9.25 (a) Antenna sample; (b)

I

(

V

) characteristic measured in position...

Figure 9.26 Array of patch antennas allocated on a pipeline or engine part w...

Figure 9.27 (a) Optoelectronic circuits for encryption and decryption of an ...

Figure 9.28 Different layouts and perspectives of diamond patch antennas: (a...

Figure 9.29 Diamond material: properties and possible implementations.

Figure 9.30 (a) PMMA with ND fillers and forces acting during a material cra...

Figure 9.31 AI self‐adaptive rapid prototype model oriented toward technolog...

Figure 9.32 Scheme of the first prototype: (a) Principle of photocurrent sti...

Figure 9.33 Experimental trend: comparison of the current photoelectric effi...

Figure 9.34 (a) 3D endoscope optical fiber system with nanocomposite tip. (b...

Figure 9.35 (a) 3D endoscope probe controlled by a robotic arm. (b) Zooming ...

Figure 9.36 (a) Clark level model (skin melanoma detection). (b) Voltage int...

Figure 9.37 Endoscope system: grayscale reflectivity response measured as vo...

Figure 9.38 (a) 3D endoscope system detecting a sphere. (b) Frontal perspect...

Figure 9.39 (a) 3D PC cavity resonator. (b–d) Basic principle of DNA detecti...

Figure 9.40 (a) Example of the emission signal of the fluorophore, laser sou...

Figure 9.41 (a) First prototype of an artificial skin sensor. (b) Plasmonic ...

Figure 9.42 First prototype of a fiber bundle matrix emitting light from dif...

Figure 9.43 Photograph of the lighted embedded prototype connected to a phot...

Figure 9.44 (a) Experimental setup scheme. (b) Theoretical trend of pressure...

Chapter 10

Figure 10.1 Matrix correlating risk impact versus probability of risk occurr...

Figure 10.2 Wine production in Industry 4.0 with the upgrade of AI and block...

Guide

Cover Page

Title Page

Copyright Page

Dedication Page

Preface

About the Author

Table of Contents

Begin Reading

Abbreviations and Acronyms

Index

Wiley End User License Agreement

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Electronics in Advanced Research Industries

Industry 4.0 to Industry 5.0 Advances

Alessandro Massaro

This edition first published 2022© 2022 John Wiley & Sons Ltd

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Library of Congress Cataloging‐in‐Publication Data

Names: Massaro, Alessandro, 1974– author.Title: Electronics in advanced research industries : industry 4.0 to industry 5.0 advances / Alessandro Massaro, Dyrecta Lab, Research Institute, Conversano (Ba), Italy.Description: Hoboken, NJ, USA : Wiley, 2022. | Includes bibliographical references and index.Identifiers: LCCN 2021028944 (print) | LCCN 2021028945 (ebook) | ISBN 9781119716877 (hardback) | ISBN 9781119716884 (adobe pdf) | ISBN 9781119716891 (epub)Subjects: LCSH: Industry 4.0. | Automation.Classification: LCC T59.6 .M37 2022 (print) | LCC T59.6 (ebook) | DDC 658.4/038028563–dc23LC record available at https://lccn.loc.gov/2021028944LC ebook record available at https://lccn.loc.gov/2021028945

Cover Design: WileyCover Image: © raigvi/Shutterstock

To my family: Magda, Andrea, Adriano, and Peggy

Preface

Modern technologies in production systems open new approaches and concepts of industrial production. The digital Industry 4.0 upgrade provides new elements to control and manage production in all industry sectors. This upgrade allows to improve product quality, and in general the whole supply chain. The new digital technologies include hardware and software tools integrated in infrastructure oriented on the gain of digital knowledge. The fast dynamicity of the markets, the increase of the global competition between companies, and the unpredictable social and health events, imposes the need to think of a new concept of a production system based on full automatisms and self‐adaptive processes, predicting production failures and product defects. In this context, the Industry 4.0 facilities can be furthermore upscaled to an intelligent control and actuation system of the production, characterizing the new Industry 5.0 scenario. The new facilities which contribute to Industry 5.0 passage are mainly based on artificial intelligence (AI) implementations in production and information systems, accomplishing predictive maintenance, failure prediction, defect classification, efficient robotic control and actuation, design optimization, testing improvements, and in general technological advances due to the possibility to quickly process data in each production stage. This book analyzes innovative production approaches, and the integration aspects of the AI in different industrial digital technologies, by enhancing specific functionalities. In innovative production systems, AI is fully integrated in information systems and covers cybersecurity, quality processes, business intelligence and intelligent production management. The innovative production is also related to new services associated with the introduction in the market of new technologies such as for the telemedicine sector, and in general for industrial diagnostics, where AI is also adopted for the improvement of inspection services. The main advantage of AI is the self‐learning of the algorithms able to learn automatically from the same production data of companies. In an industrial upgrade, the implementation of sensor control and actuation based on intelligent feedback systems is especially important. In this scenario, AI algorithms can accomplish robotic movement, by automatically optimizing the machine parameter setting, by means of image and data processing. The correct use of AI is mainly based on the formulation of the algorithm, and on the dataset adopted to learn the related model. For each application there is an associated AI learning dataset which can be improved by big data systems. In particular, image processing and image segmentation approaches can be improved by AI, enhancing hidden information as defects, or adding new information about the performed production process. Another tool supporting the assembly in supply chains and the coordination of activities is augmented reality, which can be fully integrated in the information company infrastructure. An important step for a new concept of production is the upgrade of the information technology (IT) infrastructure automatically gaining the knowledge. Different IT architectures are proposed for different application fields to enhance technologies more suitable for a self‐adaptive production providing decision support systems. A particular interesting topic for the innovative IT is the Internet of Things. The design and the development of an advanced IT infrastructure is the primary action to add for the upgrade in Industry 5.0. The AI concept is extended to the logic condition implementation, acting on signal processing, and on the use of simply electronic circuits representing these logics. The discussed methodologies allow to comprehend how it is possible to move on a competitive production based on the concept of “flexible” production and on new products based on advanced technologies on a micro‐ and nanoscale. In this scenario, companies working in manufacturing can switch dynamically the production on the new products, thus converting the production in innovative components, machines, materials, sensors, or devices. Following this orientation, this book proposes important approaches to automatize efficiently the new production, by analyzing highly advanced production tools based on nanotechnology. In this direction useful methodologies are analyzed to implement the production of high technology devices, such as reverse engineering and rapid prototyping by showing different examples useful to comprehend the methodologies to apply for an innovative production based on scientific and industrial research development. Particular attention is paid to the procedures to follow to produce a new device, to increase the company capacity to accelerate the industrialization process starting a new innovative prototype fabrication, and to basic approaches for the design modeling and testing. The optimization of the pre‐industrialization process to perform is accompanied by a quick check of the basic properties of the new product to fabricate, and by the simultaneous support of the AI application improving analysis. In order to start and to develop a new research activity, also concerning advanced technologies, precise schemes must be followed. The discussed topics facilitate the understanding of the directions of the research for the production upscaling, just to apply the research activity. The last part of the book provides different elements useful for writing an industrial research project, and for the project management. This book deals with multidisciplinary topics including electronics, mechatronics, mechanics, and informatics. All the analyzed topics are useful to know; the key elements are indispensable and useful to move the production from Industry 4.0 to Industry 5.0.

Alessandro MassaroBari, 28 December 2021

About the Author

Professor Alessandro Massaro (ING/INF/01, FIS/01, FIS/03) carried out scientific research at the Polytechnic University of Marche, at CNR, and at Italian Institute of Technology (IIT) as Team Leader by activating laboratories for nanocomposite sensors for industrial robotics. He was head of the Research and Development section and scientific director of MIUR Research Institute Dyrecta Lab Srl. Actually, he carries out research activities in LUM Enterprise at LUM University ‐Libera Università Mediterranea‐ (Casamassima‐BA‐, Italy), he is in MIUR register as scientific expert in competitive Industrial Research and Social Development, and he is currently Member of the International Scientific Committee of Measurers IMEKO and IEEE Senior Member. He received an award from the National Council of Engineers as Best Engineer of Italy 2018 (Top Young Engineer 2018).

1State of the Art and Technology Innovation

The chapter is focused on the technological and scientific state of the art about information technology (IT) advances. Starting with Industry 4.0 enabling technologies, the scientific improvements transforming the production lines and machines in intelligent systems following the logic of Industry 5.0 are discussed. The new facilities and the new technologies are oriented on the design of flexible and dynamical production processes, taking into account the market demand which is increasingly unpredictable. Starting with the enabling technologies of Industry 4.0, the specifications of the hardware and software technologies for advances in Industry 5.0 manufacturing industries are introduced. Communication protocols able to improve sensing and actuation in production processes are also discussed. Moreover, the analysis describes the Internet of Things (IoT) protocols, IoT upgrade processes and technological improvements, where of particular interest in monitoring industrial processing is infrared thermography (IRT) for improving thermal measurements in the production environment. The chapter is also focused on the description of different levels of the company information system, where sensors monitoring production constitute the field layer. The discussion is then oriented to provide an overview about sensors communicating with the local network by protocols, and achieving intelligent and efficient sensing and actuation. All the analyzed topics are addressed for integration into an upgraded information infrastructure implementing advanced tools. The analysis is then moved to the production processes in industries by highlighting main interconnections and architectures interfacing different tools. The study also enhances the scientific approaches consolidated in Industry 4.0, by providing limits of the actual technologies and perspectives for future production upscaling. Furthermore, the chapter discusses mainly intelligent information infrastructure suitable for manufacturing industries. The chapter goal is to introduce technological elements such as artificial intelligence (AI), augmented reality (AR) and big data systems, providing knowledge gain (KG). Other important aspects are the horizontal and vertical integrations of the technologies, considering bus‐based networks and automatisms in data processing which is significant for the production advances. The chapter provides elements useful to comprehend how technologies can be implemented in flexible information architectures for innovative industrialization processes.

1.1 State of the Art of Flexible Technologies in Industry

Industry 4.0 introduced digital technologies improving industry productivity and different facilities supporting processes. The main enabling technologies introduced by Industry 4.0 are [1–3]:

Three‐dimensional

(

3D

) printers connected to production software.

AR oriented on production processes.

Simulation tools able to optimize production processes by simulating production of different interconnected machines of different production lines.

Horizontal integration of supply chain elements, such as human resources, supplies, products, transports, logistics, etc., and vertical integration of different production functions including product design, production processes, production quality, and end to end combination of horizontal and vertical functions.

Cloud computing, cloud data storage, and data management in open data and big data systems.

Cybersecurity improving security during network operations and in open systems, managing network interconnections.

These main facilities enable smart manufacturing (SM) and computer integrated manufacturing (CIM) industry processes in the fourth industrial revolution. In this scenario of enabling technologies, the information network architecture of companies plays a fundamental rule in production upgrade and in production engineering. The information digitalization is the first step for Industry 4.0 implementation, where the production machines transfer data in the local area network (LAN) and in general in the cloud environment. A particular function in Industry 4.0 improvement is the production monitoring, automated by IoT sensors [4], reading in real time the operation conditions of the whole production lines and allowing intelligent manufacturing. The control performed by sensors is more efficient for in‐line monitoring procedures, where all sensors are synchronized in order to provide the best production setting of the whole supply chain. All the phases of the supply chain are important to trace. The main parts to trace in the production processes are: (i) warehouse, (ii) production lines, and (iii) logistics. In all these parts, robotics in general improves the processes, by increasing production volumes and by assisting human work. This kind of “joint collaboration” decreases the production errors and consequently the waste materials and related costs. Other technologies such as AR [5, 6] are used for human resources training during production processing, by increasing the worker skills and supporting workers to follow interactively and continuously the production. Augmented reality aided manufacturing (ARAM) is another important topic supporting production quality [5] by means of the programming of machines, robots and production tools, by managing logistics, and by checking assembled products in the whole supply chain. AR is adopted also in manufacturing as a dynamic authoring tool monitoring simultaneously the production activities of several workstations [6], for telerobotics controlling robots from a distance, for waste reduction in production activities, for assembly support, for remote maintenance, and for computer‐aided design (CAD) applications [7]. In the Industry 4.0 scenario, AI can furthermore improve the industry production efficiency. AI algorithms are mainly indicated for machine predictive maintenance [8, 9] and for assisted production, where machine working operations are properly and automatically set in order to avoid failures [10], by decreasing or stopping machine in cases of alerting conditions. IoT sensors are very important for control and actuation thus enabling totally automated processes. A broad use of IoT sensing is related to image vision [11, 12] including IRT [13], and temperature and humidity sensors. Moreover, accelerometers provide supplementary information about anomalous vibrations indicating a possible system failure, and other sensors can be applied depending on the manufacturing process to be controlled. IoT signals are processed by AI algorithms to predict the machine status in self learning modality: by analyzing historical data, the AI algorithms create the training models to test for prediction. The AI improvements represent mainly the passage from Industry 4.0 to Industry 5.0 facilities adapting automatically the production with high level efficiency, and optimizing the production processes which are previously simulated. The flexibility of the production is due to the correct choice of the sensor network architecture, of protocols and the possibility to optimize the different layers of the whole communication system of the company. A correct design of the information system allows the disposal of a modular network open to vertical and horizontal integrations introducing innovative tools and algorithms addressing the automatic production control. The layers where it is possible to operate for a flexible production are the input/output (I/O) layer, the user interface layer, the gateway layer, the IoT middleware, the processing layer, and the application layer.

1.1.1 Sensors and Actuators Layer: I/O Layer

The I/O layer is the first layer related to the production field controlled by sensors. The process of machines can be changed by actuation commands provided by the processing layer. The actuation commands must ensure the production synchronization of the whole production lines managing different production steps. In this layer, IoT devices are very important for the accuracy and reliability of the performed measurements. The data sampling is essential for a correct monitoring procedure. When the sensors control different production process steps, it is fundamental to configure and to synchronize all the sensors of the same production line. The IoT technologies are defined for the specific production process to monitor. For example, if the process is fast, it is important to select an image vision technology having a high frame rate, or sensors having a sampling time “following” the production velocity. The technologies for industrial image vision converting light into electrons are charge‐coupled device (CCD), complementary metal oxide semiconductor (CMOS), indium antimonide (InSb) infrared (IR) detectors, indium‐gallium‐arsenide (InGaAs), germanium (Ge), and mercury cadmium telluride (HgCdTe) sensors. Table 1.1 shows the working wavelengths of the IR technology.

Table 1.1 Spectral ranges of infrared technology.

Infrared technology

Spectral range (μm)

References

InSb

0.6–5

[14]

InGaAs

0.9–1.7

[14]

Ge

0.8–1.6

[15]

HgCdTe

1–9.5

[14]

Sensor networks are designed after an accurate analysis of the production processes, thus suggesting the correct configurations and connections of possible gateways, routers, and of device combinations. Sensor networks are implemented for point to point, star, extended star, bus, or mesh configuration. In Figure 1.1 are shown the different main network configurations. The design of the network is an important step for the realization of the correct network. The spatial allocation of the production machines and the workflow of the production define the best configuration. The network layout changes with the sensor system: the star or mesh network is typically adopted for sensors, besides the bus layout is suitable for production line connections and for the information system. By considering for example a photovoltaic camp with a high number of panels, it is preferable to realize a ring type fiber optic network linking all electrical string panels. The network also assumes a hybrid configuration, especially when a new network is added and linked to an old one. Table 1.2 lists the main advantages and disadvantages of the different network layouts.

Figure 1.1