John Wiley & Sons Electronics in Advanced Research Industries Cover Electronics in Advanced Research Industries A one-of-a-kind examination of the latest developments .. Product #: 978-1-119-71687-7 Regular price: $135.51 $135.51 Auf Lager

Electronics in Advanced Research Industries

Industry 4.0 to Industry 5.0 Advances

Massaro, Alessandro


1. Auflage Oktober 2021
544 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-71687-7
John Wiley & Sons

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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.


About the Author

Chapter 1 Introduction: 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 ESB 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

Chapter 2 Introduction: 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 PLC 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 failures predictions and information infrastructure layout in temperature monitoring process

2.4 Defect estimation and prediction by artificial neural network

2.5 Defect clustering and classification: combined use of 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

Chapter 3 Introduction: 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 components processing

3.2 Machine to Machine M2M interfaces: new concepts of Industry 5.0

3.3 Production line command and actuation interfaces in upgraded systems

3.3.1 PLC, PAC, IPC 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 PLC 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

Chapter 4 Introduction: IoT 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 smart industry environment controlling production 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 Localisation technologies in an industrial environment

4.1.7 GPU processing units 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 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 board managing different production stages

Chapter 5 Introduction: advanced robotics

5.1 Collaborative robotics in industry and protocols IoT agent

5.1.1 Data protocols

5.1.2 Basic concepts of robotic arms and control improvement

5.1.3 Collaborative exoskeletons 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 Main aspects of electrostatic actuators MEMS electrostatic actuators Piezoelectric actuators DC motor actuation 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 Example of PID implementation 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

Chapter 6: Introduction: advanced opto-electronic and micro-nano sensors

6.1 Nanotechnology laboratories in industries

6.1.1 Facilities for micro-nanosensors fabrication and characterization

6.2 Micro and nano-sensors as preliminary prototypes for industry research

6.2.1 Nanocomposite optoelectronic sensors and optoelectronic circuits for pressure sensors 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 Nanocomposite sensor for liquid detection systems based on pillar type layout Micro and nano sensors in production processes monitoring: leakage monitoring

6.2.5 Examples of digital MEMS/NEMS sensors: technological aspects and applications Thin film MEMS Nanoprobes for medical imaging Diamond thin film devices: sensing improvements

6.3 Multi-sensor systems and big data synchronization of micro/nano probes

Chapter 7: Introduction: image vision advances

7.1 Defects classification by artificial intelligence and data processor units

7.1.1 Artificial intelligence algorithms and automatism for defects classification: the case of study of tires production

7.1.2 Welding classification and non-destructive suitable for the quality check 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 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 Profilometer for image vision Inline 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 inline monitoring systems: camera connection

7.3.2 Image segmentation and clustering techniques: automated inline monitoring systems

7.3.3 Circuit timing inline 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

Chapter 8: Introduction: electronic and reverse engineering

8.1 Reverse engineering systems and mechanical of 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 RE in precision manufacturing process for thin film devices

8.6 RE for production of pipeline components

8.7 RE in 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

Chapter 9: Introduction: rapid prototyping

9.1 Rapid prototyping tools and micro scale electronic systems: methodological approaches

9.1.1 Photonic crystal pillars for filtering and optical resonance

9.1.2 Thin film MEMS prototyping and photolithography approach

9.1.3 Thin film GHz microstructures by 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 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 on patent development

9.5.1 Prototyping of devices implementing nanoparticles

9.5.2 Prototyping of an optoelectronic device based on nanocomposite tip

9.5.3 DNA labonchip

9.6 Nanocomposite artificial skin rapid prototyping process

Chapter 10: Introduction: Introduction: scientific research in industry

10.1 Guidelines to construct an advanced research unit in industry in 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 a research project of underground water leakage

10.3.2 Setting a research project in mechatronic: production of 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


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.