Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing

Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing

An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these tech...

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Detalles Bibliográficos
Otros Autores: Tyagi, Amit Kumar, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, NJ : Wiley [2024]
Edición:First edition
Materias:
Computer integrated manufacturing systems.
Digital twins (Computer simulation)
Artificial intelligence.
Manufacturing processes > Automation.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009852336006719
Tabla de Contenidos:
  • Cover
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Part 1: Fundamentals of AI-Based Smart Manufacturing
  • Chapter 1: Machine Learning Fundamentals
  • 1.1 Introduction
  • 1.2 Classification
  • 1.2.1 Linear Model
  • 1.2.1.1 Logistic Regression
  • 1.2.1.2 Support Vector Machine
  • 1.2.2 Nonlinear Model
  • 1.2.2.1 K-Nearest Neighbor
  • 1.2.2.2 Naive Bayes
  • 1.2.2.3 Decision Tree
  • 1.2.2.4 Random Forest
  • 1.3 Regression
  • 1.3.1 Linear Regression
  • 1.3.2 Multiple Linear Regression
  • 1.3.3 Logistic Regression
  • 1.3.3.1 Three Types of Logistic Regression
  • 1.3.4 Polynomial Regression
  • 1.3.5 Support Vector Regression
  • 1.3.6 Decision Tree Regression
  • 1.3.7 Random Forest Regression
  • 1.3.7.1 Random Forest Algorithm in Practice
  • 1.3.7.2 Random Forest Algorithm Application Examples
  • 1.3.8 Lasso Regression
  • 1.4 Clustering
  • 1.4.1 Clustering Algorithms
  • 1.4.2 K-Means Clustering Algorithm
  • 1.4.3 Mean Shift Clustering Algorithm
  • 1.4.4 DBSCAN Clustering
  • 1.4.4.1 Two Elements Required by DBSCAN
  • 1.4.4.2 Steps followed to do DBSCAN Algorithm
  • 1.5 Conclusion
  • References
  • Chapter 2: Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare
  • 2.1 Introduction
  • 2.1.1 The Significance of Industry 4.0 Across Multiple Domains
  • 2.2 Industry 4.0 in Manufacturing: Overview
  • 2.2.1 Benefits of Industry 4.0 in Manufacturing
  • 2.2.2 Examples of Industry 4.0 in Manufacturing
  • 2.2.3 Challenges in Implementation of Industry 4.0 in Manufacturing
  • 2.3 Industry 4.0 in Communication: Overview
  • 2.3.1 Benefits of Industry 4.0 in Communication
  • 2.3.2 Examples of Industry 4.0 in Communication
  • 2.3.3 Challenges of Implementing Industry 4.0 in Communication
  • 2.4 Industry 4.0 in Transportation: Overview
  • 2.4.1 Industry 4.0’s Advantages for Transportation.
  • 2.4.2 Application of Industry 4.0 in Transportation
  • 2.4.3 Challenges of Implementing Industry 4.0 in Transportation
  • 2.5 Industry 4.0 in Healthcare: Overview
  • 2.5.1 Benefits of Industry 4.0 in Healthcare
  • 2.5.2 Healthcare Professional Applications
  • 2.5.3 Challenges of Implementing Industry 4.0 in Healthcare
  • 2.6 Future of Industry 4.0 in Terms of Emerging Trends and Technologies
  • 2.7 Implications of Industry 4.0 on Various Sectors
  • 2.8 Opportunities for Businesses and Industries
  • 2.8.1 Challenges for Businesses and Industries
  • 2.9 Conclusion
  • References
  • Chapter 3: Data Analytics and Big Data Analytics
  • 3.1 Introduction to Data Analytics
  • 3.1.1 Types of Data Analytics Techniques
  • 3.1.2 Distinction between Big Data, Data Analytics, and Data Science
  • 3.2 Literature Survey
  • 3.3 An Overview of Big Data Analytics
  • 3.3.1 Difference between Big Data and Data Analytics
  • 3.3.2 The Three Vs of Big Data
  • 3.3.3 Big Data Analytics’ Advantages and Benefits
  • 3.3.4 Steps for Big Data Analytics
  • 3.4 Process of Generation of Big Data Analytics for Manufacturing
  • 3.4.1 Big Data Analysis’s Function in Manufacturing
  • 3.4.2 Several of the Most Well-Known Actual Applications of Big Data in Manufacturing
  • 3.4.3 Utilizing Big Data in the Manufacturing Industry
  • 3.5 Utilizing Big Data Analytics for Manufacturing Market Analysis
  • 3.6 Global Big Data Insights for the Manufacturing Sector
  • 3.7 Conclusion
  • References
  • Chapter 4: Artificial Intelligence Empowered Smart Manufacturing for Modern Society: A Review
  • 4.1 Introduction to AI, Smart Manufacturing
  • 4.1.1 Challenges and Opportunities
  • 4.2 AI Applications in Manufacturing
  • 4.3 Benefits and Challenges with AI
  • 4.4 Emerging Technologies Enabling Smart Manufacturing
  • 4.4.1 Internet of Things (IoT) in Smart Manufacturing.
  • 4.4.2 Big Data and Analytics in Smart Manufacturing
  • 4.4.3 Robotics and Automation in Smart Manufacturing
  • 4.4.4 AI-IoT-Cloud in Smart Manufacturing
  • 4.4.5 AI Blockchain-Based Smart Manufacturing
  • 4.5 AI-Driven Smart Manufacturing
  • 4.5.1 Predictive Maintenance and Quality Control in Smart Manufacturing
  • 4.5.2 Humanâ€"Machine Collaboration
  • 4.6 Popular Challenges and Issues Towards AI-Based Smart Manufacturing Systems
  • 4.7 AI-Based Smart Manufacturing Systems for the Future
  • 4.7.1 AI-Enhanced Supply Chain Management for the Future
  • 4.7.2 Smart Factory Concepts for Next Generation
  • 4.7.3 Quality Assurance and Inspection in Real Time
  • 4.8 Improving Operational Efficiency and Environmental Sustainability in AI Based Smart Manufacturing
  • 4.8.1 Cost Reduction and Resource Optimization in AI-Based Smart Manufacturing
  • 4.9 Future Research Opportunities and Research Gaps Towards AI-Empowered Smart Manufacturing
  • 4.10 The Evolution of Industry 5.0 and Industry 6.0
  • 4.11 Conclusion
  • References
  • Chapter 5: Use Cases of Digital Twin in Smart Manufacturing
  • 5.1 Introduction
  • 5.1.1 Smart Manufacturing Streams Utilizing Digital Twin Technology
  • 5.1.2 Technological Advancement That Makes Digital Twins Ideal for Smart Manufacturing System Design
  • 5.2 Review of Relevant Literature
  • 5.3 Various Use Cases of Digital Twin in Smart Manufacturing
  • 5.3.1 Data-Driven Smart Factory
  • 5.3.2 Cyber-Physical System (CPS) Integration
  • 5.3.3 Human-Robot Collaboration
  • 5.3.4 Adaptive Federated Learning for Industrial IoT
  • 5.4 Information Management System-Based Digital Twins and Big Data for Sustainable Smart Manufacturing
  • 5.5 Challenges and Future Avenues
  • 5.6 Conclusion
  • References
  • Part 2: Methods and Applications
  • Chapter 6: Distributed Systems and Distributed Ledger Technology - An Introduction.
  • 6.1 An Introduction
  • 6.2 Related Work
  • 6.3 Blockchain â€" In General
  • 6.4 Evolution of Blockchain
  • 6.4.1 Progress of Blockchain 1.0 to Blockchain 4.0
  • 6.5 Generic Elements of a Blockchain
  • 6.6 Benefits and Limitations of Blockchain
  • 6.7 Tiers of Blockchain Technology
  • 6.8 Features of a Blockchain
  • 6.9 Types of Blockchain
  • 6.10 Open Issues in Blockchain Technology
  • 6.11 Important Challenges with Blockchain Technology
  • 6.12 Conclusion
  • References
  • Chapter 7: Digital Twins Tools and Technologies in Smart Manufacturing
  • 7.1 Introduction
  • 7.2 Applications and Characteristics of DT
  • 7.3 DT in Manufacturing
  • 7.4 Related Work
  • 7.5 Case Study: Challenge Advisory
  • 7.5.1 Case Study: Whirlpool
  • 7.5.2 Case Study: Woodward
  • 7.6 Challenges to Implement DT
  • 7.6.1 Innovation of Technology
  • 7.6.2 Time and Cost
  • 7.6.3 Lack of Standards and Regulations
  • 7.6.4 Data Related Issues
  • 7.6.5 Life-Cycle Mismatching
  • 7.7 Open Research
  • 7.7.1 Integration with the Internet of Things (IoT)
  • 7.7.2 Integration with Industry 4.0 Technologies
  • 7.7.3 Multi-Scale DTs
  • 7.7.4 Integration with Advanced Analytics
  • 7.7.5 Real-Time Optimization
  • 7.7.6 Collaboration and Communication
  • 7.7.7 Optimization of Supply Chain Processes
  • 7.7.8 Humanâ€"Machine Collaboration
  • 7.7.9 Cybersecurity
  • 7.7.10 Scalability
  • 7.8 Conclusion
  • References
  • Chapter 8: Blockchain Based Digital Twin for Smart Manufacturing
  • 8.1 Introduction to Blockchain, Digital Twin, and Smart Manufacturing
  • 8.1.1 Background of Blockchain, Digital Twin, and Smart Manufacturing
  • 8.1.2 Role of Digital Twins in Manufacturing
  • 8.1.3 The Role of Blockchain in Smart Manufacturing
  • 8.1.4 Organization of the Work
  • 8.2 Issues and Challenges in Conventional Manufacturing Processes
  • 8.2.1 Need for Digital Transformation in Manufacturing.
  • 8.3 Digital Twins and Blockchain in Manufacturing
  • 8.3.1 Digital Twin, Types, and Applications of Digital Twins in Manufacturing
  • 8.3.2 Benefits and Key Use Cases of Digital Twin in Manufacturing
  • 8.3.3 Blockchain Components: Blocks, Transactions, and Smart Contracts in Smart Manufacturing
  • 8.3.4 Security and Data Integrity Issues in Implementing Digital Twin and Blockchain in Smart Manufacturing
  • 8.4 Synergy of Blockchain and Digital Twins for Smart Manufacturing
  • 8.5 AI, Blockchain, IoT and Other Emerging Technologies: Role in Smart Manufacturing
  • 8.6 Key Technologies for Blockchain-Based Digital Twins
  • 8.6.1 Integration of Emerging Technologies with Existing Manufacturing Systems
  • 8.7 Applications of Blockchain-Based Digital Twins in Smart Manufacturing
  • 8.8 Security and Data Privacy in Smart Manufacturing - In General
  • 8.8.1 Cyber Security in the Smart Factory
  • 8.9 Case Studies
  • 8.9.1 Blockchain for Smart Manufacturing in the Automotive Industry
  • 8.9.2 Digital Twins in Pharmaceutical Manufacturing
  • 8.9.3 Blockchain and Digital Twin in Aerospace Manufacturing
  • 8.10 Future Research Towards Integration of AI and Blockchain for Autonomous Manufacturing
  • 8.11 Sustainability and Environmental Impact via Smart Manufacturing
  • 8.12 Conclusion
  • References
  • Chapter 9: Blockchain for Internet of Things and Machine Learning-Based Automated Sectors
  • 9.1 Introduction
  • 9.2 Evolution Variants and Architecture of Internet of Things
  • 9.3 Evolution, Variants, and Architecture Machine Learning
  • 9.4 Blockchain for Internet of Things and Machine Learning
  • 9.5 Blockchain-Based Learning Automated Analytics Platforms
  • 9.6 Blockchain Inclusion in Internet of Things Architecture and Machine Learning
  • 9.7 Features Benefits Limitations Applications and Challenges of Internet of Things.
  • 9.8 Features Benefits Limitations Applications and Challenges of Machine Learning.

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