Digital Twins in Industrial Production and Smart M Anufacturing: an Understanding of Principles, Enha Ncers, and Obstacles

Comprehensive reference exploring the benefits and implementation of digital twins in industrial production and manufacturing Digital Twins in Industrial Production and Smart Manufacturing provides an overview of digital twin theoretical concepts, techniques, and recent trends used to meet the requirements and challenges of industrial production and smart manufacturing. The text describes how to achieve industrial excellence through virtual factory simulation and digital modeling innovations for next-generation manufacturing system design. The contributing authors address the many possible technical advantages of major Industry 5.0 technological advancements, using illustrations to aid readers in practical implementation of concepts, along with existing scenarios, potential research gaps, adoption difficulties, case studies, and future research objectives. The text also presents many applications and use cases of Industry 5.0 and digital twins in a variety of industries, including the aerospace industry, pharmaceutical manufacturing and biotech, augmented reality, virtual reality, edge computing and blockchain-based Internet of Things (IoT), cobots, intelligent logistics and supply chain management, and more. Edited by a group of highly qualified academics with significant experience in the field, Digital Twins in Industrial Production and Smart Manufacturing covers additional topics such as: Hyper-automation technology, including specialized workflow procedures and particular sectors of solicitations linked to hyper-automation Digital twins in the context of smart cities, with attempts to draw comparisons with the use of digital twins in industrial IoT Virtual factories based on digital twins and corresponding architecture to facilitate modeling, simulation, and assessment of manufacturing systems Cognitive, interactive, and standardization aspects of digital twins, and the proper implementation of digital twin technology for safety critical systems Digital Twins in Industrial Production and Smart Manufacturing is a must-have reference for researchers, scholars, and professionals in fields related to digital twins in industrial production and manufacturing. It is also suitable as a hands-on resource for students interested in the fields of digital twins and smart manufacturing. Cover Chapter 1 Journey to Digital Twin Technology in Industrial Production: Evolution, Challenges, and Trends 1.1 Introduction 1.2 Systematic Review Analysis 1.2.1 Logistics Applications of Digital Twins 1.2.2 A Manufacturing Process 1.3 Development of Industry 1.4 Assessments of Models 1.4.1 Workload Balance and Task Distribution Using Digital Twins 1.4.2 An Outline for a Digital Shadow of Production 1.5 Technologies of DT 1.5.1 DT in Internet of Things (IoT) 1.5.2 How Does It Operate? 1.5.3 DT in Cloud Computing 1.5.4 DT in Artificial Intelligence (AI) 1.5.5 DT in Extended Reality (XR) 1.6 A Digital Twin System with a Modular Layout 1.6.1 Deep Transfer Learning for Fault Diagnosis with a Digital Twin 1.7 Automated Guided Vehicle Scheduling Based on Digital Twins 1.7.1 Fundamental Concepts DTDAS 1.8 Creating a Digital Twin 1.9 Digital Twins in Industry 5.0 1.10 Implementation Obstacles 1.11 Conclusion References Chapter 2 The State‐of‐the‐Art Digital Twin Components in Industrial IoT Production: Theoretical and Practical Applications 2.1 Introduction 2.1.1 Application Using Digital Twin 2.1.2 Technologies Involved in Digital Twin 2.1.3 Survey Organization 2.2 Literature Survey 2.2.1 An Overview of IT Network Architecture 2.2.2 Data Interpretation 2.2.3 Privacy and Security of Data 2.2.4 Trustworthiness 2.2.5 Predictions 2.2.6 Standardized Simulation 2.2.7 Domain Model 2.3 Digital Twin Integrated IIoT Application 2.3.1 Design‐phase Applications 2.3.1.1 Optimization Through Iteration 2.3.1.2 Provide Integrity of Data 2.3.1.3 Virtual Assessment and Verification 2.3.2 An Overview of Manufacturing Applications 2.3.2.1 Real‐time Surveillance 2.3.2.2 Production Planning 2.3.2.3 Prediction of Workpiece Performance 2.3.2.4 Humans and Robots Working Together and Interacting 2.3.2.5 Analyzing and Bettering a Process 2.3.2.6 Asset Management 2.3.2.7 Planned Manufacturing 2.3.3 Applications in the Service Phase 2.3.3.1 Predictive Maintenance 2.3.3.2 Finding and Fixing Problems 2.3.3.3 State Monitoring 2.3.3.4 Performance Prediction 2.3.3.5 Virtual Test 2.3.4 Retiring Applications Phase 2.4 IIoT‐DT Combination with Blockchain‐based Approach 2.4.1 Framework for Trust and Security Based on the Blockchain 2.4.2 IIoT‐DT with Blockchain Layer Architecture 2.4.2.1 Network Layer 2.4.2.2 Data Layer 2.4.2.3 Consensus Layer 2.4.2.4 Control Layer 2.5 The Risk of Digital Twin Security 2.5.1 Physical Attacks 2.5.2 Data Modification Attack 2.5.3 System Attack 2.5.4 Software Attack 2.5.5 Data Communication Attacks 2.5.6 Man in the Middle Attack 2.5.7 DoS/DDoS Attack 2.5.8 Eavesdropping Attack 2.5.9 Spoofing Attack 2.5.10 Replay Attack (RA) 2.5.11 Data Storage Attacks 2.6 Countermeasure 2.7 Conclusion References Chapter 3 Decision Support System for Digital Twin‐based Smart Manufacturing Systems and Design in Industry 5.0 3.1 Introduction 3.2 State of the Art 3.2.1 Applying Ontology to Risk Management 3.2.2 Computer Vision for Risk Management 3.3 The Suggested Design 3.3.1 A Real‐World Workshop 3.3.2 A Digital Workshop 3.3.3 Risky Circumstances of the Workshop's Ontology Approach 3.3.4 Acquiring Instances Using a Hybrid Dataset of Digital and Physical Data 3.4 Inference Engine Framework 3.5 A Digital‐Original Dataset Amalgamation 3.5.1 Recreation of Digital Workshop for Risky States 3.5.2 Digital Workshop Lighting and Texture Modeling 3.5.3 Analyzing Risky Conditions in a Workshop 3.5.4 Creating a Hybrid Original‐Virtual Dataset 3.5.4.1 Digital Dataset 3.5.4.2 Genuine Dataset 3.5.4.3 Assigning an Ontology Term to a Detected Object 3.6 Discussion and Demo (DD) 3.6.1 Ontology Construction Using eNanoMapper Slimmer 3.6.2 Construction of a Photorealistic Simulated Environment for DTW 3.6.3 Generation of Hybrid Synthetic‐Real Datasets 3.6.4 Instruction and Assessment of Models 3.6.4.1 In‐depth Instruction of Models 3.6.4.2 Assessing Models 3.6.4.3 Verdicts from Model Training 3.6.5 Insights into Semantics Reasoning 3.6.6 Discussions 3.7 Conclusion and Future Prospects References Chapter 4 Industrial Internet of Things: Enhancement of Industries with Hyperautomation for Smart Manufacturing Machines 4.1 Introduction 4.2 Related Work on Hyperautomation 4.2.1 Primary Requisites of Hyperautomation 4.2.2 Technological Perspective of Hyperautomation 4.2.2.1 Robotic Process Automation (RPA) 4.2.2.2 Natural Language Processing (NLP) 4.2.2.3 Artificial Intelligence and Machine Learning 4.2.2.4 AI and ML in IIOT 4.2.2.5 Low‐Code Platforms 4.3 Standardization of Industrial IoT and its Initiatives 4.3.1 Industrial Internet Reference Architecture (IIRA) 4.3.2 RAMI 4.0 4.3.3 One M2M 4.3.4 Framework Using Arrowhead 4.4 Customized Hyperautomation Workflows Approach 4.4.1 Use Case Scenarios of Automation 4.4.1.1 Health Care 4.4.1.2 Supply Chain 4.4.1.3 Banking and Finance 4.4.1.4 Retail 4.4.1.5 Lending Operations in Hyperautomation 4.5 Advantages of Hyperautomation for Security 4.5.1 Human Error Deduction 4.5.2 Improvement in Response Time 4.5.3 Increased Visibility 4.6 Future Work Prospects and Barriers 4.6.1 Connectivity and Interoperability 4.6.2 Scalability 4.6.3 Fault Tolerance 4.6.4 Flexibility 4.6.5 Security and Safety 4.7 Conclusion References Chapter 5 Digital Twins Model of Industrial Production Control Management Using Deep Learning Techniques 5.1 Introduction 5.2 Literature Review 5.3 Analysis of the Field's Current State 5.3.1 Data Modeling 5.3.2 Machine Learning 5.4 Identification of Gaps and Research Opportunities 5.4.1 Data Quality 5.4.2 Interoperability 5.4.3 Model Explainability 5.4.4 Scalability 5.4.5 Ethics and Privacy 5.4.6 Real‐time Decision‐making 5.5 Methodology 5.5.1 Data Acquisition 5.5.2 Data Preprocessing 5.6 Deep Learning Techniques for Predictive Modeling 5.6.1 Recurrent Neural Networks (RNNs) 5.6.2 Convolutional Neural Networks (CNNs) 5.6.3 Auto Encoders 5.6.4 Generative Adversarial Networks (GANs) 5.6.5 Long Short‐Term Memory (LSTM) Networks 5.7 Performance and Evaluation Metrics 5.7.1 Mean Absolute Error (MAE) 5.7.2 Root Mean Squared Error (RMSE) 5.7.3 Coefficient of Determination (R‐squared or R2) 5.7.4 Precision, Recall, and F1‐Score 5.7.5 Curve and Area Under the Curve (AUC) 5.8 Background Study 5.8.1 Predictive Maintenance in Industrial Systems 5.8.2 Industrial Systems with Digital Twins 5.8.3 Predictive Maintenance Digital Twins 5.8.4 Deep Learning and Digital Twins 5.8.5 Related Research 5.8.6 Research Gap 5.8.7 Discussions and Analysis of Digital Twins 5.9 Limitations and Future Research 5.9.1 Data Availability and Quality 5.9.2 Interpretable Predictions 5.9.3 Model Complexity and Training Time 5.10 Identification of Future Research Opportunities and Directions 5.10.1 Integration of Multiple Data Sources 5.10.2 Transfer Learning 5.10.3 Explainable AI 5.10.4 Human‐in‐the‐loop Approaches 5.10.5 Interoperability and Standardization 5.11 Applications and Benefits 5.11.1 Use of Digital Twins for Business Management Using Deep Learning 5.12 Results and Discussions 5.13 Potential Domains for Digital Twins 5.13.1 Manufacturing 5.13.2 Energy 5.13.3 Healthcare 5.13.4 Supply Chain 5.13.5 Automotive 5.13.5.1 Product Testing 5.13.5.2 Adding Manufacturing Capacity 5.13.5.3 Employee Training 5.13.5.4 Predictive Maintenance 5.13.5.5 Sales 5.13.5.6 3D: Car Design and Product Development 5.13.5.7 Human–machine Interfaces (HMI) 5.14 Conclusion References Chapter 6 Digital Twin for Sustainable Development of Intelligent Manufacturing 6.1 Introduction to Digital Twin Environment 6.1.1 Digital Twin and Its Role in Intelligent Manufacturing 6.1.2 Sustainable Development in Manufacturing 6.1.3 Measurable Examination Approach in Evaluating the Effect of Computerized Twin on Reasonable Turn of Events 6.2 Statistical Analysis Methods 6.2.1 Selection of Appropriate Statistical Techniques for the Analysis 6.2.2 Description of the Variables Considered in the Analysis 6.2.3 Statistical Models Used to Evaluate the Relationship Between Digital Twin and Sustainable Development in Intelligent Manufacturing 6.3 Impact on Resource Efficiency 6.3.1 Statistical Assessment of Resource Consumption Reduction Achieved Through the Implementation of a Digital Twin 6.3.2 Analysis of Energy Efficiency Improvements in Manufacturing Processes 6.3.3 Quantification of Waste Reduction and Improved Recycling Rates 6.4 Environmental Impact Assessment 6.4.1 Statistical Evaluation of the Digital Twin's Impact on Greenhouse Gas Emissions 6.4.2 Evaluation of Water and Air Contamination Decrease Accomplished Through Wise Assembling with a Computerized Twin 6.4.3 Analysis of the Carbon Footprint and Life Cycle Assessments (LCAs) of Products Manufactured Using the Digital Twin Environment 6.5 Economic Benefits 6.5.1 Statistical Analysis of Cost Savings Achieved Through the Implementation of a Digital Twin 6.5.2 Evaluation of Productivity Improvements and Reduced Downtime 6.6 Social and Human Factors 6.6.1 Analysis of the Impact of Digital Twin on Workforce Safety and Ergonomics 6.6.2 Assessment of Job Creation and Employment Opportunities Resulting from the Adoption of the Digital Twin Environment 6.7 Case Studies and Results 6.7.1 Presentation of Real‐world Case Studies Demonstrating the Statistical Analysis Outcomes 6.7.2 Conversation of the Discoveries and Their Suggestions for Practical Advancement in Wise Assembling 6.7.3 Comparison of Results with Industry Benchmarks and Standards 6.8 Limitations and Future Directions 6.8.1 Identification of Limitations in the Statistical Analysis and Data Collection Process 6.8.2 Suggestions for Future Research and Improvements in the Assessment Methods 6.8.3 Challenges and Opportunities for the Digital Twin Environment 6.8.3.1 Challenges 6.8.3.2 Opportunities 6.9 Conclusion References Chapter 7 Digital Twins in Flexible Industrial Production and Smart Manufacturing: Case Study on Intelligent Logistics and Supply Chain Management 7.1 Introduction 7.2 Related Works 7.3 Case Study 7.3.1 Digital Twin 7.3.2 Flexible Production Line 7.3.3 Smart Manufacturing 7.3.4 Supply Chain Management 7.3.5 Predictive Analytics for Industry 4.0 7.3.6 The Technology Enabling DT in Our Industrial Production 7.4 Conclusion References Chapter 8 Applications and Use Cases of Digital Twins in Industry: 3D Graphics, Visualization, Modeling, Printing, and Reality Platforms 8.1 Introduction 8.2 Digital Twins in Microelectronic Manufacturing 8.2.1 System Development Life Cycle 8.2.2 Digital Twins in Semiconductor Industry 8.2.3 Aiding Production Decisions 8.2.4 Achieving More Balance and Security in the Semiconductor Supply Chain 8.2.5 Digital Twins for Semiconductor Designing 8.3 Digital Twins in Food Products Manufacturing Industry 8.3.1 Digital Twin Technology for Data Handling 8.3.2 Creation of Digital Footprint in Food Production 8.3.3 Digital Twin of the Complete Food Factory 8.3.4 Connectivity Establishment 8.3.5 Utilization of IIoT Information 8.3.6 Identification of Discrepancies and Anomalies 8.3.7 Enhancement of Quality and Productivity 8.3.8 Production Parameters Mapped with Quality Outcomes 8.3.9 Optimized Supply Chain and Manufacturing Network 8.3.10 Closure of Lack of Information in the Supply Chain 8.4 Digital Twins for Process Optimization in Textile Industry 8.4.1 Blockchain 8.4.2 Radio‐frequency Identification (RFID) 8.4.3 Artificial Intelligence (AI) 8.4.4 3D Design Software 8.5 Digital Twin for Building Smart Systems 8.5.1 Smart Water Grids (SWGs) 8.5.2 Technology for Smart Water Management (SWM) 8.5.3 Method of Working 8.6 Applications of DT Smart Systems 8.7 Conclusion References Chapter 9 Cobots in Smart Manufacturing and Production for Industry 5.0 9.1 Introduction 9.2 Industry 5.0 Revolutionizing Industry 9.3 Cobots in Smart Manufacturing and Production 9.4 Architecture of a Cobot 9.5 Cobots and Digital Twinning 9.6 Cognitive Digital Twins and Cobots for Collaboration 9.6.1 Cyber‐physical System 9.6.1.1 Types of Digital Twins 9.6.2 Machine Learning Layer 9.6.3 Service Layer 9.6.4 Actual Data Collection Layer 9.7 Cobots and Artificial Intelligence 9.8 Frontiers Uncovered with Cobots 9.8.1 Cobots in Agriculture 9.8.2 Cobots in Education 9.8.3 Cobots in Space Technology 9.8.4 Cobots in Quality Control 9.8.5 Cobots in Warehouse 9.8.6 Cobots in Logistics 9.8.7 Cobots in Healthcare 9.8.8 Cobots in Hospitality 9.8.9 Cobots and Retail Industry 9.8.10 Cobots in Food Production and Delivery 9.9 Cobots Safety Standards 9.10 Conclusion References Chapter 10 Edge Computing and Artificial Intelligence‐Based Internet of Things for Industry 5.0: Framework, Challenges, Use Cases, and Research Directions 10.1 Introduction 10.1.1 Edge Computing Technology 10.1.2 Artificial Intelligence 10.1.3 Importance of Edge Computing 10.2 The Motivation for Edge Computing and Artificial Intelligence Combination 10.2.1 Advantages of Edge Computing with Artificial Intelligence 10.2.2 Advantages of Artificial Intelligence with Edge Computing 10.2.3 Mutual Beneficial Relationship Between AI and EC 10.3 Algorithms of AI in Edge Computing 10.3.1 Basics of Machine Learning 10.3.2 Federated Learning 10.3.2.1 Edge Intelligence Collaborative Learning 10.3.3 Evolutionary Algorithms 10.3.4 Edge Computing for Optimization Solutions with AI 10.3.4.1 Optimization Solutions Through AI 10.3.4.2 Optimization Solutions Through Computing Offloading 10.3.4.3 Other Solutions Without Computing Offloading 10.3.4.4 Optimization for Resource Allocation 10.4 An Overview of the Symbolic Connection Between Industry Capabilities and Edge Computing 10.4.1 Existing Industrial Internet of Things (IIoT) 10.4.2 Challenges of Edge Intelligence‐based IIoT 10.4.3 Difficulties in Developing an Edge Intelligence‐based IIoT 10.4.3.1 General Industry End‐point Components with Intelligence 10.4.4 How Edge Intelligence Supports Industrial Devices? 10.5 Use Cases of IIoT with Edge Intelligence Technologies 10.5.1 Real Time Applications of Industrial Internet of Things with Edge Intelligence 10.6 Research Directions of IIoT 10.6.1 Research Directions of Industrial Internet of Things with Edge Intelligence 10.7 Summary References Chapter 11 Smart Manufacturing with a Digital Twin–Driven Cyber‐physical System: Case Study and Application Scenario 11.1 Introduction 11.2 Smart Manufacturing 11.2.1 Smart Manufacturing in Medical Field 11.2.2 Steps Taken by Government 11.3 Cyber‐physical System 11.3.1 Requirements of Cyber‐physical Systems in Production Systems 11.3.2 Benefits of Cyber‐physical System 11.3.3 Components of Cyber‐physical System 11.3.4 Cyber‐physical System in Industrial Manufacturing 11.3.5 Applications of Cyber‐physical System in Industrial Manufacturing 11.4 Overview of Cyber‐Physical System 11.5 Digital Twin 11.5.1 To Create Digital Twin 11.5.2 Digital Twin in Smart Manufacturing 11.5.3 Cyber‐physical System Versus Digital Twin 11.5.4 Digital and Cyber‐physical System Relation 11.5.5 Digital Framework 11.5.6 Digital Twin in Health Care 11.5.7 Developing a Digital Twin of the Heart 11.5.8 Human Digital Twin 11.6 Case Studies 11.6.1 Case Study of a Mine Digital Twin 11.6.2 Case Study of a Hollis Offshore Installation 11.6.3 Case Study of Digital Twin in Offshore Industry 11.6.4 Case Study of Integrating a Digital Twin in DCMS Framework References Chapter 12 Industrial Excellence Through Virtual Factory Simulation and Digital Modeling Innovations for Next‐Generation Manufacturing System Design 12.1 Introduction to the Chapter 12.2 Virtual Factory Simulation 12.3 Digital Modeling Innovations 12.4 Industrial Excellence in Virtual Factory Simulation 12.5 Industrial Excellence in Digital Modeling Innovations 12.5.1 Involving VR for Preparing and Treatment Purposes 12.6 A Virtual Factory Data Model 12.7 Manufacturing Plan 12.8 Viability of Product Design 12.9 Digital Twin‐based Factory Simulation 12.10 Optimize Product Design and Procedures 12.11 Challenges and Future Research Directions in Manufacturing System Design 12.12 Summary References Chapter 13 Digital Transformation in the Pharmaceutical and Biotech Industry: Challenges and Research Directions 13.1 Introduction 13.2 Personalized Medicine and Precision Health 13.3 Automation and AI for Drug Discovery 13.3.1 The Drug Discovery Process: A Long and Arduous Journey 13.3.1.1 AI Accelerates Target Identification 13.3.2 Automated Screening of Compound Libraries 13.3.2.1 Screening Large Compound Libraries 13.3.2.2 Bioassays and Reporter Genes 13.3.2.3 In Silico Screening 13.3.3 Virtual Patient Models and Simulations for Clinical Trials 13.3.3.1 Reducing Costs and Speeding Up Trials 13.3.3.2 Improving Trial Design 13.3.3.3 Personalized Treatments 13.3.4 Reducing Costs and Increasing Efficiency with Automation 13.3.4.1 Accelerating the Analysis of Research Data 13.3.4.2 Optimizing the Drug Discovery Process 13.3.5 The Future of AI and Automation for Drug Discovery 13.3.5.1 More Sophisticated Algorithms and Computing Power 13.3.5.2 Integration of Multiple Data Sources 13.3.5.3 More Advanced Simulation and Modeling 13.3.5.4 Personalized and Precision Medicine 13.3.5.5 Continuous Learning and Optimization 13.4 Robotic Process Automation for Clinical Trials 13.4.1 Automating Clinical Trial Processes 13.4.2 Improving Data Quality and Consistency 13.4.3 Streamlining Document Management 13.4.4 Monitoring Trial Progress 13.4.5 Challenges Faced in RPA 13.5 Virtual and Augmented Reality for Medical Education 13.5.1 Enhanced Learning Through Immersion 13.5.2 Access to Rare or Complex Cases 13.5.3 Reduced Costs and Improved Safety 13.5.4 Challenges in Augmented Reality 13.6 3D Printing of Drugs and Medical Devices 13.6.1 Cost Reduction and Improved Accessibility 13.6.2 Personalized and Precision Medicine 13.6.3 Reduced Side Effects and Improved Compliance 13.6.4 Challenges Faced in 3D Printing of Drugs and Devices 13.7 IoT and Connected Health 13.7.1 Connected Health and Clinical Trials 13.7.2 Challenges and Concerns 13.8 Predictive Analytics and Real‐World Evidence 13.8.1 The Promise of Predictive Analytics 13.8.2 Challenges Around Data Quality and Governance 13.8.3 The Need for Advanced Analytics Skills 13.8.4 Real‐world Evidence and AI Improving Clinical Trials 13.8.5 Optimizing Patient Recruitment 13.8.6 Choosing Ideal Trial Locations 13.8.7 Refining Trial Protocols 13.9 Digital Medicine 13.9.1 Challenges Implementing in Digital Transformation 13.9.2 Vast Amounts of Data and Data Integration 13.9.3 Data Privacy Regulations and Compliance 13.9.4 Research Directions for Data Management and Privacy 13.9.5 Complex Regulatory Landscape 13.9.6 Adapting to Changing Regulations 13.9.7 Research Directions for Regulatory Compliance 13.9.8 Talent Acquisition and Upskilling Challenges 13.9.9 Cybersecurity Challenges – Threats to Sensitive Data and Intellectual Property 13.9.9.1 Threats to Sensitive Data and Intellectual Property 13.9.10 Research Directions for Regulatory Compliance 13.9.11 Deep Learning Techniques 13.10 Conclusion 13.11 Summary References Chapter 14 Overcoming the Obstacles of Motion Sickness in the Metaverse's Digital Twins 14.1 Introduction 14.2 Motion Sickness, Cognitive Principles 14.3 Gender and Motion Sickness 14.4 VR Sickness 14.5 VR Sickness Measurement 14.6 Guidelines for VR Digital Twins Development to Avoid VR Sickness 14.7 Hardware Challenges 14.8 Content Specificities 14.9 Human Factors 14.10 Best Practices 14.11 Conclusion References Chapter 15 Smart Cities Using Digital Twins and Industrial IoT Based Technologies: Tools and Products from the Industry Sector 15.1 Introduction 15.2 Background Knowledge of Digital Twin 15.2.1 The First DT 15.2.2 Key Properties 15.2.3 Beyond Computer Representations and Dealing with Uncertainty 15.2.4 Types of DT 15.2.5 Integration Levels 15.3 DT in Industrial IoT 15.3.1 Predict the Future 15.3.2 Proliferation of Precision 15.3.3 Producing Intricate Digital Twins 15.3.4 Lessen Expenses 15.3.5 Evading Failure 15.4 DT in Smart Cities 15.4.1 The Advantages of Having a DT for Smart Cities 15.4.1.1 Introducing DT Citizens 15.5 Industrial Requests of Digital Twins 15.5.1 Product Design 15.5.1.1 Design a Novel Product 15.5.1.2 Redesign a Creation 15.5.1.3 Analyze the Product Flaws 15.5.1.4 Manufacturing 15.5.1.5 Manufacturing Agendas and Organization 15.5.1.6 Manufacturing Control Optimization 15.5.1.7 Cyber‐physical Production System 15.5.1.8 Layout of Industrial Lines 15.6 Literature Review on Digital Twin Progress in Smart Cities 15.7 Discussion 15.8 Open Challenges for IIoT and Smart City Development 15.8.1 IoT/IIoT Challenges 15.8.1.1 Data, Confidentiality, and Trust 15.8.1.2 Infrastructure 15.8.1.3 Connectivity 15.8.1.4 Expectations 15.8.2 Open Trials for DT in Smart Cities 15.8.2.1 Datasets and Data Sources 15.8.2.2 Data Values and Interoperability 15.8.2.3 Brand Data Expressive and Nearby to City Investors 15.8.2.4 Privacy, Security, and Ethics 15.8.2.5 Cost and Benefits of DT's Application 15.8.2.6 5G Technologies and DTs 15.9 Conclusions References Chapter 16 Digital Twin in Aerospace Industry and Aerospace Transformation Through Industry 4.0 Technologies 16.1 Introduction 16.1.1 Introduction to Industry 4.0 Technologies for Aerospace Transformation 16.2 The Concept, Components, and Applications of Digital Twins 16.2.1 Concept 16.2.2 Major Component of DTs 16.2.2.1 Physical Side: Infrastructure with Working Sensors 16.2.2.2 Virtual Side: Analytical Models and AI 16.2.3 Digital Twins and Their Industrial Use Case Studies 16.3 DTs Roadmap: A Path Toward the Future 16.3.1 Basic DT 16.3.2 Interactive DT 16.3.3 Standardized DT 16.3.4 Intelligent DT 16.3.5 Industrial Engineering in the Age of Industry 4.0: Defining the Future Role of Digital Twins 16.3.6 The Digital Twin: From Its Core Ideas to Real‐world Applications in High‐tech Production 16.4 Industry 4.0 16.4.1 What is Defense and Aerospace 4.0? 16.4.2 Describe Industry 4.0 16.4.3 Transformational Shifts in Technology Use, With an Emphasis on Aerospace and Defense Priorities 4.0 16.4.4 Getting on the Right Track for Industry 4.0 Success 16.4.5 An All‐encompassing Plan for Digital Innovation 16.4.6 A Quickening Rate of Change 16.4.7 Transforming a Traditional Industry 16.4.8 Aerospace Digital Twin Navigation 16.4.9 Why Are Aerospace and Defense Undergoing Digital Transformation? 16.5 The Twins Are Driven by Technology 16.6 Conclusion References Index