Alessandro Massaro - Electronics in Advanced Research Industries

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A one-of-a-kind examination of the latest developments in machine control 
 
In 
, 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 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, 
 will also earn a place in libraries of technicians working in the process industry.

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7 Chapter 7Figure 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...

8 Chapter 8Figure 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 11responses 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.

9 Chapter 9Figure 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 11signals 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...

10 Chapter 10Figure 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

1 Cover Page

2 Title Page

3 Copyright Page

4 Dedication Page

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