Digital Twin – Fundamental Concepts to Applications in Advanced Manufacturing (Springer Series in Advanced Manufacturing)

✍ Scribed by Surjya Kanta Pal, Debasish Mishra, Arpan Pal, Samik Dutta, Debashish Chakravarty, Srikanta Pal

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 495
• Category: Library

✦ Synopsis

This book provides readers with a guide to the use of Digital Twin in manufacturing. It presents a collection of fundamental ideas about sensor electronics and data acquisition, signal and image processing techniques, seamless data communications, artificial intelligence and machine learning for decision making, and explains their necessity for the practical application of Digital Twin in Industry.

Providing case studies relevant to the manufacturing processes, systems, and sub-systems, this book is beneficial for both academics and industry professionals within the field of Industry 4.0 and digital manufacturing.

✦ Table of Contents

Preface
Acknowledgements
From the Authors
Contents
About the Authors
Abbreviations
Chapter—1
Chapter—2
Chapter—3
Chapter—4
Chapter—5
Chapter—6
Chapter—7
Symbols
Chapter—2
Chapter—3
Chapter—4
Chapter—6
Chapter—7
1 Evolution of Manufacturing and Its Journey Towards Digital Twin
1.1 Overview
1.2 A Few Fascinating Facts
1.3 Manufacturing and the Industrial Revolution
1.3.1 The First Industrial Revolution
1.3.2 The Second Industrial Revolution
1.3.3 The Third Industrial Revolution
1.3.4 A Summary of the Three Industrial Revolutions
1.3.5 The Fourth Industrial Revolution
1.3.6 Advancement of Industry 4.0 Over the Third Industrial Revolution
1.4 Introduction to Digital Twin
1.5 Key-Aspects of a Digital Twin Model
1.6 Components for Envisaging the Digital Twin
1.6.1 Internet of Things
1.6.2 Cyber-Physical Systems
1.6.3 Cloud Computing
1.6.4 Simulation
1.6.5 Big Data Analytics
1.7 Utilities of the Digital Twin in Manufacturing
1.8 Summary
1.9 Questions for Practice
References
2 Sensor Electronics for Digital Twin
2.1 Overview
2.2 The Need for Electronics in Manufacturing
2.3 Introduction to Sensor and Transducer
2.4 Role of a Sensor in Manufacturing
2.5 Types of Sensors
2.5.1 Position and Displacement Sensors
2.5.2 Proximity Sensors
2.5.3 Velocity and Motion
2.5.4 Force
2.5.5 Temperature
2.5.6 Vibration Sensor
2.5.7 Current and Power Sensors
2.5.8 Pressure Sensor
2.5.9 Flow Rate Sensor
2.6 A Guide on Sensors for Digital Twin
2.7 Performance Indices of a Sensor
2.8 Summary
2.9 Questions for Practice
References
3 Signal Processing for Digital Twin
3.1 Overview
3.2 Introduction to Signal
3.2.1 Meaning of the Term ‘Signal’
3.3 Signal as Indirect Means of Monitoring
3.4 Importance of Signal Processing in Digital Twin
3.5 Signal Acquisition and Its Features
3.5.1 Signal Conditioning
3.5.2 Analogue-To-Digital Converter
3.5.3 The Sampling Rate of a Signal
3.5.4 Signal Acquisition
3.6 Arduino Microcontroller
3.7 Input/Output Module and PLC for Industrial Applications
3.8 Noise in Signal
3.8.1 Median Filter
3.8.2 Gaussian Filter
3.8.3 Wavelet-Based Filtering
3.9 Methods of Signal Processing
3.9.1 Time Domain Analysis
3.9.2 Frequency Domain Analysis
3.9.3 Time-Frequncy Domain Analysis
3.10 Summary
3.11 Questions for Practice
References
4 Image Processing for Digital Twin
4.1 Overview of This Chapter
4.2 Selection of Process Zone or Application Zone
4.2.1 Real-Time Process Control
4.2.2 Real-Time Inspection Using Machine Vision
4.3 Image Acquisition
4.3.1 Digital Camera
4.3.2 Lens
4.3.3 Illumination
4.3.4 Optical Filters and Special Optics
4.4 Image Enhancement
4.4.1 Image Enhancement in the Spatial Domain
4.4.2 Image Enhancement in the Frequency Domain
4.5 Image Segmentation
4.5.1 Edge Detection
4.5.2 Thresholding
4.5.3 Morphological Operations
4.6 Feature Extraction and Object Recognition
4.6.1 Texture Analyses
4.7 Summary of Various Image Processing Techniques Used for Real-Time Process Control and Inspection in Manufacturing
4.7.1 Image Enhancement
4.7.2 Image Segmentation
4.7.3 Feature Extraction and Decision Making
4.8 Summary
4.9 Questions for Practice
References
5 Data Communication-Edge, Fog, and Cloud Computing
5.1 Overview
5.2 IoT and Network
5.3 IoT Framework
5.4 Introduction to the Edge, Fog, and Cloud Computing
5.5 The Necessity of Cloud and Edge Computing in Industry 4.0 Perspective
5.6 Edge Versus Cloud Computing
5.6.1 Real-Time Response/Latency
5.6.2 Multi-sensor Synchronisation
5.6.3 Network Data Volume/Competency
5.6.4 Privacy/Security
5.7 Application Classification
5.8 Data Communication Technologies
5.8.1 Personal/Local Area Networks
5.8.2 Technologies for Wide Area Networks (WAN)
5.8.3 Applications Level Protocols
5.9 Network Architectures for Edge/cloud Computing
5.9.1 Cloud Sub-System
5.9.2 Edge Considerations for IIoT Systems
5.9.3 Utilizing the Edge Devices for Artificial Intelligence
5.10 Real-Life Example in Manufacturing
5.11 Futuristic Concept—5G in Manufacturing
5.11.1 Problems with the Existing Network Technology
5.11.2 Introduction to 5G Network
5.11.3 Applications of 5G in Manufacturing
5.11.4 Challenges to Be Addressed for Implementation of 5G in Manufacturing
5.12 Summary
5.13 Questions for Practice
References
6 Artificial Intelligence and Machine Learning in Manufacturing
6.1 Overview of This Chapter
6.2 Introduction to Artificial Intelligence
6.3 Foundation of Artificial Intelligence
6.4 Requirement of Artificial Intelligence in a Digital Twin
6.5 Deep into Artificial intelligence—The Knowledge Pyramid
6.6 Sensor Signal Processing
6.6.1 Signal Acquisition and Conditioning
6.6.2 Signal Representation
6.6.3 Feature Extraction
6.6.4 Machine Learning and Inferencing
6.6.5 Reasoning and Modelling
6.6.6 Modelling and Simulation
6.6.7 Optimization and Planning
6.6.8 Image and Video Processing
6.7 Analytics Pipeline Optimization Strategies
6.7.1 Machine Learning
6.7.2 Artificial Neural Network
6.7.3 Adaptive Filtering
6.7.4 AutoML
6.7.5 Deep Learning
6.7.6 Transfer Learning
6.7.7 Automation of Sensor Data Fusion and Sensor Selection
6.7.8 Automation of Compression and Partitioning for Edge Devices
6.8 Summary
6.9 Questions for Practice
References
7 Digital Twin Application
7.1 Overview of This Chapter
7.2 The History Behind the Conceptualisation of Digital Twin
7.3 Concept of Digital Twin
7.4 Realizing a Digital Twin Model
7.5 Digital Twin as a Tool Throughout the Life Cycle
7.6 Digital Twins for Design and Manufacturing
7.6.1 Digital Twin for Iron and Steel Product Life Cycle
7.6.2 Digital Twin for Weld-Joint Life Cycle
7.7 Digital Twins for Service
7.7.1 Digital Twin for Critical Component—A Gearbox
7.7.2 Digital Twin for Critical Component—A Voltage Converter Station
7.8 Challenges and Intelligence
7.9 Real-Life Examples in Manufacturing
7.9.1 Process Twin Using Signal Processing
7.9.2 Process Twin Using Image Processing
7.9.3 Machine Twin
7.10 Futuristic Concepts
7.10.1 Federated Learning in Digital Twin
7.10.2 Blockchain in Digital Twin
7.11 Summary
7.12 Questions for Practice
References