Chapter 8 Digital Twin Use Cases and Industries
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Yogini Borole
Abstract
Digital twin (DT) is among the most key techniques pushing digitalisation in a variety of sectors. The digital reproduction or model of any actual item is referred to as DT (physical twin). The automated bidirectional nature of DT distinguishes it from modelling as well as other digitised or AutoCAD models, and real-time data transmission between digital and corporeal twins. The advantages of applying DT in reduced operating expenses and time, higher productivity, and better decision-making are all advantages in any industry. Enhanced predictive/preventive maintenance, and so forth. As a consequence, its adoption is projected to increase. In the coming decades, as Industry 4.0 products and systems are introduced, they will develop quickly, relying on incremental data collection and storage. Connecting that data to DTs properly may open up numerous new opportunities, and this study investigates several industrial areas where DT implementation has access to these chances and how these prospects are propelling the industry ahead. The chapter discusses DT applications in 13 sectors, including industry, agricultural, educational, infrastructure, medicine, and retail, as well as industrial use cases in these areas.
Abstract
Digital twin (DT) is among the most key techniques pushing digitalisation in a variety of sectors. The digital reproduction or model of any actual item is referred to as DT (physical twin). The automated bidirectional nature of DT distinguishes it from modelling as well as other digitised or AutoCAD models, and real-time data transmission between digital and corporeal twins. The advantages of applying DT in reduced operating expenses and time, higher productivity, and better decision-making are all advantages in any industry. Enhanced predictive/preventive maintenance, and so forth. As a consequence, its adoption is projected to increase. In the coming decades, as Industry 4.0 products and systems are introduced, they will develop quickly, relying on incremental data collection and storage. Connecting that data to DTs properly may open up numerous new opportunities, and this study investigates several industrial areas where DT implementation has access to these chances and how these prospects are propelling the industry ahead. The chapter discusses DT applications in 13 sectors, including industry, agricultural, educational, infrastructure, medicine, and retail, as well as industrial use cases in these areas.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents IX
- Chapter 1 What Is Digital Twin? Digital Twin Concept and Architecture 1
- Chapter 2 Benefits of Digital Twin Modelling 17
- Chapter 3 Modelling of Digital Twin 27
- Chapter 4 Digital Twin and IoT 39
- Chapter 5 Machine Learning, AI, and IoT to Construct Digital Twin 49
- Chapter 6 Intelligent and Smart Manufacturing with AI Solution 61
- Chapter 7 Information and Data Fusion for Decision-Making 71
- Chapter 8 Digital Twin Use Cases and Industries 87
- Chapter 9 Security in Digital Twin 115
- Chapter 10 Implementation of Digital Twin 149
- Chapter 11 Digital Twin Simulator 185
- Chapter 12 Case Studies: Smart Cities Based on Digital Twin 215
- Index 237
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents IX
- Chapter 1 What Is Digital Twin? Digital Twin Concept and Architecture 1
- Chapter 2 Benefits of Digital Twin Modelling 17
- Chapter 3 Modelling of Digital Twin 27
- Chapter 4 Digital Twin and IoT 39
- Chapter 5 Machine Learning, AI, and IoT to Construct Digital Twin 49
- Chapter 6 Intelligent and Smart Manufacturing with AI Solution 61
- Chapter 7 Information and Data Fusion for Decision-Making 71
- Chapter 8 Digital Twin Use Cases and Industries 87
- Chapter 9 Security in Digital Twin 115
- Chapter 10 Implementation of Digital Twin 149
- Chapter 11 Digital Twin Simulator 185
- Chapter 12 Case Studies: Smart Cities Based on Digital Twin 215
- Index 237
