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Submit a ProposalArtificial Intelligence-Enabled Digital Twin for Smart Manufacturing
 | Edited by Amit Kumar Tyagi, Shrikant Tiwari, Senthil Kumar Arumugam, and Avinash Kumar Sharma Copyright: 2024 | Expected Pub Date:2024/10/30 ISBN: 9781394303571 | Hardcover | 611 pages |
One Line Description
An essential book on the applications of AI and digital twin technology in the smart manufacturing sector.
Audience
This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.
This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.
Description
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 technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities.
This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility.
This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance.
What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions).
Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0.
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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 technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities.
This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility.
This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance.
What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions).
Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0.
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Author / Editor Details
Amit Kumar Tyagi, PhD, is an assistant professor at the National Institute of Fashion Technology, New Delhi, India. He obtained his doctorate in 2018. He has published more than 200 papers in refereed international journals, conferences, and books, many of which are with the Wiley-Scrivener imprint. He has filed more than 25 national and international patents in deep learning, the Internet of Things, cyber-physical systems, and computer vision. His current research focuses on next-generation machine-based communications, blockchain technology, smart and secure computing, and privacy.
Amit Kumar Tyagi, PhD, is an assistant professor at the National Institute of Fashion Technology, New Delhi, India. He obtained his doctorate in 2018. He has published more than 200 papers in refereed international journals, conferences, and books, many of which are with the Wiley-Scrivener imprint. He has filed more than 25 national and international patents in deep learning, the Internet of Things, cyber-physical systems, and computer vision. His current research focuses on next-generation machine-based communications, blockchain technology, smart and secure computing, and privacy.
Shrikant Tiwari, PhD, is an associate professor in the Department of Computer Science & Engineering, School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh India. He obtained his doctorate in 2012. He has authored or co-authored more than 75 national and international journal publications, book chapters, and conference articles. He has five patents filed to his credit. His research interests include machine learning, deep learning, computer vision, medical image analysis, pattern recognition, and biometrics.
Senthil Kumar Arumugam, PhD, is an assistant professor in the Professional Studies Department, CHRIST (Deemed to be University), Bangalore Central Campus, Bengaluru, India. He obtained his doctorate in 2014. He has received 9 awards.
Avinash Kumar Sharma, PhD, is an associate professor in the Department of Computer Science and Engineering, Sharda School of Engineering and Technology, Sharda University, Greater Noida, India. He has published about 30 research articles in national/international conferences, journals, and book chapters, edited four books and has published four patents including one design patent.
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Table of Contents
Preface
Part 1: Fundamentals of AI-Based Smart Manufacturing
1. Machine Learning Fundamentals
Renugadevi A. S., R. Jayavadivel, Charanya J., Kaviya P. and Guhan R.
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
2. Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare
P. Nancy, S. Gnanavel, V. Sudha, G. Deepika and Mahmoud Elsisi
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
3. Data Analytics and Big Data Analytics
Neeta Nathani and Jagdeesh Kumar Ahirwar
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
4. Artificial Intelligence Empowered Smart Manufacturing for Modern Society:
A Review
Amit Kumar Tyagi, Pooja Bhatt, Chidambaram N. and Shabnam Kumari
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 Human–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
5. Use Cases of Digital Twin in Smart Manufacturing
Vijayakumar Ponnusamy, Dilliraj Ekambaram and Nemanja Zdravkovic
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
6. Distributed Systems and Distributed Ledger Technology - An Introduction
Amit Kumar Tyagi, Shrikant Tiwari and Kanchan Naithani
6.1 An Introduction
6.2 Related Work
6.3 Blockchain – 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
7. Digital Twins Tools and Technologies in Smart Manufacturing
K. Jayashree, S. Muralidharan, V. Sathya, M. Rajakumaran and C.M. Nalayini
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 Human–Machine Collaboration
7.7.9 Cybersecurity
7.7.10 Scalability
7.8 Conclusion
References
8. Blockchain Based Digital Twin for Smart Manufacturing
Amit Kumar Tyagi, Shabnam Kumari, Richa and Utkarsh Kumar
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
9. Blockchain for Internet of Things and Machine Learning-Based Automated Sectors
Amit Kumar Tyagi, Shabnam Kumari and Shrikant Tiwari
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
9.9 Physical, Network, Software Attacks in Internet of Things and Machine Learning-Based Applications
9.10 Countermeasures for Raised Challenges and Reliability of Blockchain in
Machine Learning/Artificial Intelligence
9.10.1 Reliability of Blockchain in Machine Learning/Artificial Intelligence
9.11 Countermeasures for Raised Challenges and Reliability of Blockchain in IoT
9.12 Adoption of Blockchain with Internet of Things Systems
9.13 Adoption of Blockchain with Machine Learning-Based Computing Environment
9.14 Blockchain- and Machine Learning-Based Solutions for Big Data Challenges
9.15 Blockchain-Enabled Internet of Things (IoTs) Platforms for Automation in
Intelligent Transportation Systems
9.16 Blockchain-Enabled Internet of Things (IoTs) Platforms for Automation in
Software Development
9.17 Blockchain-Enabled Internet of Things (IoTs) Platforms for Automation in
Protecting Systems
9.18 Issues and Challenges Towards Blockchain Based IoT–ML Applications
9.19 Open Challenges of Blockchain, Internet of Things, and Machine Learning
Integration
9.20 Future Research Opportunities for Blockchain, Internet of Things, and
Machine Learning Integration
9.21 Conclusion
References
10. An Enhanced Threat Detection Model to Assist Supply Chain Management
Using Artificial Intelligence
Ambika N.
10.1 Introduction
10.2 Background
10.3 Literature Survey
10.4 Methodology Adopted
10.5 Analysis of the Work
10.6 Future Work
10.7 Conclusion
References
11. Role of AI and Digital Twin in Smart Manufacturing
M. Anand, T. M. Sheeba and C. Fancy
11.1 Introduction
11.2 Digital Twins
11.3 Digital Twin for Smart Manufacturing
11.3.1 Networks to Define Problems
11.3.2 Production Monitoring and Modification Using Digital Twin
11.3.3 Smart Manufacturing Policies
11.4 Artificial Intelligence Enabled with Digital Twin
11.5 AI and DT Lifecycle
11.6 AI-Enabled Digital Twins in Manufacturing
11.7 Digital Twins Used in Manufacturing
11.8 AI Impacting Digital Twins
11.9 Organizations Succeed Deploying Digital Twins
Summary
References
12. Data Analytics and Visualization in Smart Manufacturing Using AI-Based
Digital Twins
M. Sivakumar, M. Maranco, N. Krishnaraj and U. Srinivasulu Reddy
12.1 Introduction
12.2 Smart Manufacturing and Digital Twins
12.2.1 Key Concepts of Smart Manufacturing
12.2.2 Role of Digital Twins in Optimizing Manufacturing Operations
12.2.3 Benefits of Digital Twins
12.3 Data Collection and Integration
12.3.1 Sensor Data Acquisition
12.3.2 Types of Sensors
12.3.3 Data Transmission Protocols
12.3.4 Challenges Associated with Sensor Data Acquisition
12.3.5 Data Integration and Preprocessing
12.3.6 Techniques Commonly Used for each of these Preprocessing Tasks
12.4 AI-Based Analytics
12.4.1 Predictive Maintenance
12.4.2 Machine Learning and Deep Learning Techniques
12.4.3 Process Optimization
12.4.4 Optimization Algorithms
12.5 Visualization Techniques
12.5.1 Dashboard Design
12.5.2 Key Performance Indicators (KPIs)
12.5.3 Data Visualization Techniques
12.5.4 Interactive Dashboard Features
12.5.5 Augmented Reality (AR) and Virtual Reality (VR)
12.6 Case Studies and Applications
12.6.1 Case Study 1: Siemens Digital Twin for Automotive Manufacturing
12.6.2 Case Study 2: GE Aviation’s Predix Platform for Aircraft Engines
12.6.3 Case Study 3: ABB Ability™ Digital Twin for Industrial Plants
12.6.4 Case Study 4: Bosch Rexroth’s IoT Gateway and Analytics Solution
12.6.5 Insights Based on the Use Cases and Industry Trends
12.7 Challenges and Future Directions
12.7.1 Challenges
12.7.2 Future Direction
12.7.3 Emerging Trends
12.8 Conclusion
References
Part 3: Issues and Challenges Towards AI and Digital Twin-Based Smart Manufacturing
13. The IoT of Robotics: The Frontier of Automation
G. Rithik and A. Kathirvel
13.1 Internet of Robotic Things Automation
13.1.1 Artificial Intelligence
13.2 Converging Sensing/Actuating Information Network
13.2.1 Sensors and Actuators
13.2.2 Emerging IORT Technologies
13.2.3 Communication Technologies
13.2.4 Voice Recognition, Voice Control
13.2.5 Machine Learning as Enabler for Adaptive
13.3 Marketplace for an IORT Ecosystem
13.3.1 Orchestration
13.3.2 The Applications in Warehouse and E-Commerce
13.4 IORT Practical Applications in Commerce
13.4.1 Both Amusement and Wellbeing
13.4.2 Coordination
13.5 Healthcare Robotics Process Automation Paradigm
13.6 Operative Robotics
13.6.1 Radiographer Robotics
13.6.2 Rehabilitating Robotics
13.6.3 Software in Smooth Robots and Prosthetics Smooth Grippers
13.7 Vibrotactile Stimulation
13.7.1 Estimated Usage and Success Rate of Robotics in Healthcare
13.8 IoT in Transportation
13.9 Applications of IoT and AI in Agriculture Automation
13.9.1 Smart Farm
13.9.2 UAV or Drones
13.9.3 Irrigation
13.9.4 Fertilizers Application
13.9.5 Weed and Pest Control
13.9.6 Storage of Farm Products
13.10 Sustainable Agriculture
13.11 Machine Intelligence
13.12 Virtual and Augmented Reality
13.13 Integration of Digital Twins with IoT
13.14 Biomedical Application
13.15 Smart Cities
13.16 Energy Management
13.17 Intelligent Connectivity
13.18 Continual Adaptation with Safety Guarantees
13.19 Multimodal Dialogue
13.20 Industrial IoT
13.21 IoT Fire Forecast Detectors
13.22 IoT-Based Greenhouse Management
13.23 IoT Architecture Domain
13.23.1 Centralized Smart Home Architecture
13.23.2 Distributed Smart Home Architecture
13.24 Emergent Interfaces
References
14. Real-Time Monitoring and Predictive Maintenance
R. Patel, S. Shah, S. Lella and A. Gajbahar
14.1 Introduction
14.2 Fundamentals
14.2.1 Reliability
14.2.2 Availability
14.2.3 Maintainability
14.2.4 Serviceability
14.3 Type of Predictive Maintenance
14.3.1 Corrective Maintenance
14.3.2 Risk-Based Maintenance
14.3.2.1 Assessment of the Risk
14.3.2.2 Evaluation of the Risk
14.3.2.3 Risk-Based Maintenance Planning
14.3.3 Preventive Maintenance
14.3.3.1 Cost-Based Preventive Maintenance
14.3.3.2 Time-Based Preventive Maintenance
14.3.3.3 Failure-Based Preventive Maintenance
14.3.4 Condition-Based Maintenance
14.3.4.1 Process for Condition-Based Maintenance
14.3.4.2 Implementation of Condition-Based Maintenance
14.3.5 Predetermined Maintenance
14.4 Real-Time Monitoring of Industrial Components
14.4.1 Real-Time Sensing
14.4.1.1 Significance of Real-Time Sensing
14.4.1.2 Sensual Virtuosity
14.4.1.3 Dynamic Data Elevation
14.4.1.4 Environment Harmonization
14.4.1.5 Futuristic Precision
14.4.2 Real-Time Controlling
14.4.2.1 Pioneering Proactivity
14.4.2.2 Orchestrating the Symphony of Proactive Maintenance
14.4.2.3 Integration with Real-World Applications
14.4.3 Real-Time Monitoring
14.4.3.1 Importance of Real-Time Monitoring
14.4.3.2 Infrastructure and Data Processing
14.4.3.3 Fault Detection and Prediction
14.4.3.4 Practical Application in Automotive Manufacturing
14.4.4 Real-Time Predicting
14.4.4.1 The Essence of Real-Time Predicting
14.4.4.2 The Technological Backbone
14.4.4.3 Applications Across Industries
14.4.5 Real-Time Alerting
14.5 Predictive Maintenance with Real-Time Actions for Industrial Components
14.5.1 Decision-Making
14.5.2 Machine Learning for Better Prediction
14.6 Challenges and Ethical Considerations
14.7 Conclusion
References
15. Advanced Topics for Blockchain-Based Applications: Open Issues, Technical, Legal, and Research Challenges
Amit Kumar Tyagi, Shabnam Kumari and Tanuj Surve
15.1 Introduction
15.2 Open Issues Towards Blockchain-Based Applications
15.3 Critical Challenges Towards Blockchain-Based Applications
15.3.1 Technical
15.3.2 Non-Technical
15.3.3 Legal
15.3.4 Research
15.4 Future Work
15.5 Conclusion
References
16. Issues and Challenges in Implementing Smart Manufacturing in the Current Scenario
Charanya J., Shrisudhan B., Sathruba P., Sharun P. and Prakash Duraisamy
16.1 Introduction
16.1.1 The Revolution of Industry
16.1.2 Trends in Industry 4.0 Development
16.2 Challenges in Smart Manufacturing
16.2.1 Technological Challenges
16.2.2 Infrastructure Challenges
16.2 3 Regulatory and Compliance Challenges
16.2.4 Human Factors
16.3 Difficulties and Prospects for Further Research on Blockchain Application
16.3.1 Unresolved Concerns about Blockchain Technology in Industry 4.0
16.3.2 Obstacles to Blockchain Implementation in Industries and Social Structures
16.3.3 Possible Applications of Blockchain Technology in Smart Manufacturing
16.4 Obstacles in Industry 4.0
16.4.1 Challenges and Responses
16.4.2 Industry 4.0 Sustainability and Resilience
16.5 The Internet of Things: Security Threats and Challenges (IoT)
16.5.1 Difficulties within the Smart Environment Based on IoT
16.6 Conclusion
References
Part 4: Near-Future Developments Towards AI and Digital Twin-Based Smart Manufacturing
17. Artificial Intelligence for Malware Analysis: A Systematic Study
Amit Kumar Tyagi and Santosh Reddy Addula
17.1 Introduction to AI, Malware and Cybersecurity Fundamentals
17.1.1 The Importance of AI and Malware Analysis in Cybersecurity
17.1.2 Organization of the Work
17.2 AI Applications in Cybersecurity
17.2.1 Types of Malware and Attack Vectors
17.3 Existed Techniques for Malware Analysis
17.3.1 The Need for Advanced Malware Analysis in this Smart Era
17.3.2 Adversarial Attacks and Evasion Techniques
17.4 AI-Powered Malware Analysis
17.4.1 Natural Language Processing for Threat Analysis
17.5 Available Malware Datasets and Benchmarks for Malware Analysis
17.6 Open Issues and Challenges in Dataset Creation in Malware Analysis
17.7 Metrics for Evaluating AI-Based Malware Detection
17.8 Technical, Legal Challenges and Issues Towards AI-Based Malware Analysis
17.9 Future Trends and Innovations Towards AI-Based Malware Analysis
17.9.1 The Future of Cybersecurity
17.9.2 Research Gaps and Opportunities Towards AI-Based Malware Analysis
17.10 Conclusion
References
18. Artificial Intelligence-Based Cyber Security and Digital Forensics: A Review
Amit Kumar Tyagi, Shabanm Kumari and Richa
18.1 Introduction to Cybersecurity Fundamentals and Digital Forensics Basics
18.1.1 The Role of AI in Cybersecurity
18.1.2 Threat Landscape and Challenges
18.1.3 Importance of Digital Forensics in Cybersecurity
18.1.4 AI-Driven Digital Forensics
18.1.5 Organization of the Work
18.2 Background and Motivation
18.2.1 Motivation
18.3 Digital Forensics and AI
18.3.1 Automating Digital Forensic Investigations
18.3.2 AI-Powered Evidence Analysis in Cyber Security
18.3.3 Benefits, Limitation, Issues, and Challenges of AI-Based Cyber Security and Digital Forensics
18.4 AI for Threat Detection, and Prevention
18.4.1 Simulators, Algorithms/Methods for Threat Intelligence in Cybersecurity
18.4.2 AI-Driven Cybersecurity Solutions for Next Generation Society
18.5 Important Challenges and Ethical Issues for AI-Powered Cyber Security and Digital Forensics
18.6 Future Trends and Innovations AI-Powered Cyber Security and Digital Forensics
18.6.1 Emerging Technologies in AI-Driven Cybersecurity
18.6.2 Research Gaps and Future Opportunities Towards AI-Powered Cyber Security and Digital Forensics
18.6.3 Unexplored Areas in AI-Based Cybersecurity
18.7 Conclusion
References
19. Blockchain Application in Industry 5.0
D. Saveetha, Vijayakumar Ponnusamy, Nemanja Zdravković and Nandini S.M.
19.1 Introduction
19.2 Blockchain for Industry 5.0 Transformative Potential
19.3 Applications of Blockchain in Industry 5.0
19.4 Supply Chain Management and Traceability
19.4.1 Transparency and Accountability in Supply Chains
19.4.2 Tracking and Authentication of Products
19.4.3 Reduction of Counterfeit Goods
19.5 Decentralized Manufacturing
19.5.1 Distributed Manufacturing Networks
19.5.2 Intellectual Property Protection
19.5.3 Quality Control and Certification
19.6 Energy Sector Transformation
19.6.1 Peer-to-Peer Energy Trading
19.6.2 Grid Management and Efficiency
19.6.3 Renewable Energy Certificates
19.7 Intelligent Health Care and Medical Records
19.7.1 Secure and Immutable Medical Data
19.7.2 Interoperability and Data Sharing
19.7.3 Drug Supply Chain Integrity
19.8 Financial Services and Payments
19.8.1 Cross-Border Payments
19.8.2 Financial Inclusion and Access
19.8.3 Identity Verification and KYC Verification
19.9 Case Studies
19.9.1 Case Study 1 Blockchain in Supply Chain Management
19.9.2 Case Study 2 Decentralized Manufacturing with Blockchain
19.9.3 Case Study 3 Blockchain-Based Energy Trading Platform
19.9.4 Case Study 4 Blockchain for Healthcare Data Management
19.9.5 Case Study 5 Blockchain in Financial Services
19.10 Open Issue and Critical Challenges
19.10.1 Scalability and Performance
19.10.2 Regulatory and Legal Frameworks
19.10.3 Interoperability and Standardization
19.10.4 Privacy and Data Protection
19.10.5 Governance and Trust
19.11 Conclusion
References
20. Blockchain-Empowered Internet of Things (IoTs) Platforms for Automation
in Various Sectors
Santosh Reddy Addula, Amit Kumar Tyagi, Kanchan Naithani and Shabnam Kumari
20.1 Introduction
20.2 Emerging Trends and Visions Towards Blockchain and Internet of Things
20.3 Usage of Blockchain in 5G-Enabled Internet of Things
20.4 Internet of Things and Industrial Internet of Things
20.6 Blockchain Platform for Industrial Internet of Things
20.7 Benefits and Features of Industrial Internet of Things
20.8 Application and Future Work of Blockchain Platform for Industrial Internet of Things
20.9 Future of Blockchain-Based Internet of Things
20.10 Blockchain-Enabled Internet of Things (IoTs) Platforms for Supply Chain
Functions
20.11 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Energy
and Smart Grids
20.12 Blockchain-Enabled Internet of Things (IoTs) Platforms for Industrial Control Systems
20.13 Blockchain-Enabled Internet of Things (IoTs) Platforms for IoT-Based
Customer Relationship Management (CRM) and Logistics CRM
20.14 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Healthcare
20.15 Blockchain-Enabled Internet of Things (IoTs) Platforms for Digital Marketing and Online Social Networking
20.16 Blockchain-Enabled Internet of Things (IoTs) Platforms for Online Social Networking
20.17 Blockchain-Enabled Internet of Things (IoTs) Platforms for Textile Industry
20.18 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Banking and Financial Services
20.19 Blockchain-Enabled Internet of Things (IoTs) Platforms for Military Services
20.20 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Agricultural Services
20.21 Blockchain Deployment in 5G-Enabled Smart Industrial Automation
20.22 Open Issues in Blockchain-Empowered Internet of Things
20.23 Critical Challenges Towards Using Blockchain-Empowered Internet of Things
20.24 Conclusion
References
21. Digital Twin-Enabled Smart Manufacturing: Challenges and Future Directions
M. Maranco, M. Sivakumar, N. Krishnaraj, Kashyapa Abhiram Ivaturi and Nidhya R.
21.1 Introduction
21.2 Related Works
21.3 Current Landscape of Digital Twin Adoption
21.3.1 Present Used Locations
21.3.2 Benefits Associated with Adopting Digital Twins in Manufacturing Processes
21.3.2.1 Enhanced Product Quality
21.3.2.2 Increased Flexibility and Agility
21.3.2.3 Reduced Downtime and Maintenance Costs
21.3.2.4 Optimized Supply Chain Management
21.3.2.5 Enhanced Collaboration and Knowledge Sharing
21.3.3 Case Studies Illustrating Real-Life Examples of Digital Twin-Enabled Smart Manufacturing Initiatives
21.4 Challenges in Digital Twin Implementations
21.4.1 Key Challenges in Implementing Digital Twin Technologies
21.4.1.1 Data Integration and Quality
21.4.1.2 Complexity and Scalability
21.4.1.3 Interoperability
21.4.1.4 Security and Privacy
21.4.1.5 Cost and ROI Justification
21.4.1.6 Skill Gap and Change Management
21.4.1.7 Regulatory Compliance and Standards
21.4.2 Technical Challenges in Implementing Digital Twin Technologies
21.4.2.1 Data Integration
21.4.2.2 Interoperability
21.4.2.3 Scalability
21.4.2.4 Security and Privacy
21.4.2.5 Modeling and Simulation Accuracy
21.4.3 Organizational Challenges
21.4.3.1 Cultural Barriers
21.4.3.2 Skill Gaps
21.4.3.3 Change Management Issues
21.5 Future Work
21.5.1 Emerging Trends
21.5.1.1 Integration with IoT and Sensor Networks
21.5.1.2 AI and Machine Learning
21.5.1.3 Edge Computing
21.5.1.4 Digital Twin Lifecycle Management
21.5.1.5 Simulation and Virtual Testing
21.5.1.6 Blockchain Integration for Data Security
21.5.1.7 Cross-Domain Integration
21.5.1.8 Human-Digital Twin Interaction
21.5.1.9 Ethical and Regulatory Considerations
21.5.1.10 Quantum Computing
21.5.2 Prediction of Future Applications and Potential Impact on Smart Manufacturing
21.5.2.1 AI-Driven Predictive Maintenance
21.5.2.2 Digital Twins
21.5.2.3 IoT-Enabled Supply Chain Management
21.5.2.4 Robotics and Automation
21.5.2.5 Additive Manufacturing (3D Printing)
21.5.2.6 Blockchain for Supply Chain Transparency
21.5.2.7 Augmented Reality (AR) for Maintenance and Training
21.5.3 Discussion on the Role of Artificial Intelligence, Machine Learning, and Edge Computing in Enhancing Digital Twin Capabilities
21.5.3.1 Artificial Intelligence (AI)
21.5.3.2 Machine Learning (ML)
21.5.3.3 Edge Computing
21.6 Conclusion
21.6.1 Quick Recap
21.6.1.1 Opportunities
21.6.1.2 Challenges
21.6.2 Key Takeaways and Further Researches
21.6.2.1 Key Takeaways
21.6.3 Suggestions for Further Research
References
22. Future of Computer Vision and Industrial Robotics in Smart Manufacturing
Santosh Reddy Addula and Amit Kumar Tyagi
22.1 Introduction to Computer Vision and Industrial Robotics, and Smart Manufacturing
22.1.1 Evolution of Manufacturing and Automation
22.1.2 The Role of Computer Vision and Robotics in Smart Manufacturing
22.1.3 Limitations of Traditional Manufacturing Processes
22.1.4 The Need for Digital Transformation in Manufacturing
22.1.5 Labor Shortages and Efficiency Demands
22.1.6 Organization of the Work
22.2 Role of Computer Vision in Smart Manufacturing
22.2.1 Computer Vision: Definitions, Key Components, Benefits, Disadvantages
22.2.2 Applications of Computer Vision in Manufacturing
22.3 Industrial Robotics in Smart Manufacturing
22.3.1 Industrial Robots: Introduction, Types, Benefits and Key Applications
22.3.2 Robotic Automation and Collaborative Robots (Cobots) in Manufacturing
22.4 Convergence of Computer Vision and Robotics in Today’s Smart Era: Applications and Opportunities
22.5 Emerging Technologies for Computer Vision and Robotics in Smart Manufacturing
22.6 Applications of Computer Vision and Robotics in Smart Manufacturing
22.7 Security and Data Privacy Towards Computer Vision and Robotics-Based
Smart Manufacturing
22.8 Case Studies in Computer Vision and Robotics in Manufacturing
22.8.1 Computer Vision for Quality Control in Manufacturing
22.8.2 Robotics in Automotive Manufacturing
22.8.3 Collaborative Robots in Electronics Assembly in Manufacturing
22.9 Future Opportunities Towards Computer Vision and Robotics-Based Smart Manufacturing
22.10 Sustainability and Environmental Impact Towards Computer Vision and
Robotics-Based Smart Manufacturing
22.10.1 Overcoming Implementation Challenges Towards Computer Vision and Robotics in Smart Manufacturing
22.11 Conclusion
References
23. The Future of Manufacturing with AI and Data Analytics
Neel Shah, Sneh Shah, Janvi Bhanushali, Nirav Bhatt, Nikita Bhatt and Hiren Mewada
23.1 Introduction
23.2 Different Types of Maintenance Strategies
23.3 New Research Trends in Manufacturing
23.3.1 Usage of IoT in Modern Manufacturing
23.3.2 Usage of Big Data in Modern Manufacturing
23.3.3 Development of AI Technologies in Manufacturing
23.3.4 Digital Twin
23.4 Conception of Different AI Technologies
23.4.1 Artificial Intelligence and Machine Learning
23.4.2 Deep Learning
23.4.2.1 Convolution Layers (CNNs)
23.4.2.2 Transformer Architecture
23.4.3 Reinforcement Learning
23.4.4 Unsupervised Learning
23.4.5 Transfer Learning
23.4.6 State-of-the-Art Models
23.5 Digital Twins
23.6 Role of Artificial Intelligence in Predictive Maintenance
23.6.1 Types of Data Used for Predictive Maintenance
23.6.2 Synthetic Data Generation
23.6.2.1 Generative Adversarial Net (GAN)
23.6.2.2 Diffusion Models
23.6.3 Prognostic and Health Management (PHM)
23.6.3.1 Remaining Useful Time (RUL)
23.6.3.2 End of Life (EOL)
23.6.4 Anomaly Detection
23.6.5 Maintenance Strategy Optimization
23.7 Limitations and Challenges
23.8 Opportunities and Future Scope
23.9 Concluding Remarks
References
24. Artificial Intelligence Techniques in Predictive Maintenance, Their Applications, Challenges, and Prospects
Akriti Rai, Jyotika shastri and Hina Bansal
24.1 Introduction
24.2 Techniques of Predictive Maintenance
24.2.1 Time Series Analysis
24.2.2 Probability Forecasting
24.2.2.1 Steps in Probability Forecasting
24.2.3 AI Technologies Used in Predictive Maintenance
24.2.3.1 Natural Language Processing (NLP)
24.2.3.2 Machine Learning
24.2.3.3 Compute Vision
24.2.3.4 Deep Learning
24.2.3.5 Intelligent Teaching
24.2.4 Implementation of Predictive Maintenance in AI
24.2.4.1 Manufacturing
24.2.4.2 Health Care
24.2.4.3 Oil and Gas
24.2.4.4 Automotive
24.2.5 Challenges
24.2.5.1 Data Security
24.2.6 Devices
24.2.6.1 EMR’s (Electronic Medical Records)
24.2.6.2 Smart Devices, Signals, and In Vitro Diagnosis
24.3 Conclusion
References
Index
Back to Top
Preface
Part 1: Fundamentals of AI-Based Smart Manufacturing
1. Machine Learning Fundamentals
Renugadevi A. S., R. Jayavadivel, Charanya J., Kaviya P. and Guhan R.
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
2. Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare
P. Nancy, S. Gnanavel, V. Sudha, G. Deepika and Mahmoud Elsisi
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
3. Data Analytics and Big Data Analytics
Neeta Nathani and Jagdeesh Kumar Ahirwar
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
4. Artificial Intelligence Empowered Smart Manufacturing for Modern Society:
A Review
Amit Kumar Tyagi, Pooja Bhatt, Chidambaram N. and Shabnam Kumari
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 Human–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
5. Use Cases of Digital Twin in Smart Manufacturing
Vijayakumar Ponnusamy, Dilliraj Ekambaram and Nemanja Zdravkovic
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
6. Distributed Systems and Distributed Ledger Technology - An Introduction
Amit Kumar Tyagi, Shrikant Tiwari and Kanchan Naithani
6.1 An Introduction
6.2 Related Work
6.3 Blockchain – 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
7. Digital Twins Tools and Technologies in Smart Manufacturing
K. Jayashree, S. Muralidharan, V. Sathya, M. Rajakumaran and C.M. Nalayini
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 Human–Machine Collaboration
7.7.9 Cybersecurity
7.7.10 Scalability
7.8 Conclusion
References
8. Blockchain Based Digital Twin for Smart Manufacturing
Amit Kumar Tyagi, Shabnam Kumari, Richa and Utkarsh Kumar
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
9. Blockchain for Internet of Things and Machine Learning-Based Automated Sectors
Amit Kumar Tyagi, Shabnam Kumari and Shrikant Tiwari
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
9.9 Physical, Network, Software Attacks in Internet of Things and Machine Learning-Based Applications
9.10 Countermeasures for Raised Challenges and Reliability of Blockchain in
Machine Learning/Artificial Intelligence
9.10.1 Reliability of Blockchain in Machine Learning/Artificial Intelligence
9.11 Countermeasures for Raised Challenges and Reliability of Blockchain in IoT
9.12 Adoption of Blockchain with Internet of Things Systems
9.13 Adoption of Blockchain with Machine Learning-Based Computing Environment
9.14 Blockchain- and Machine Learning-Based Solutions for Big Data Challenges
9.15 Blockchain-Enabled Internet of Things (IoTs) Platforms for Automation in
Intelligent Transportation Systems
9.16 Blockchain-Enabled Internet of Things (IoTs) Platforms for Automation in
Software Development
9.17 Blockchain-Enabled Internet of Things (IoTs) Platforms for Automation in
Protecting Systems
9.18 Issues and Challenges Towards Blockchain Based IoT–ML Applications
9.19 Open Challenges of Blockchain, Internet of Things, and Machine Learning
Integration
9.20 Future Research Opportunities for Blockchain, Internet of Things, and
Machine Learning Integration
9.21 Conclusion
References
10. An Enhanced Threat Detection Model to Assist Supply Chain Management
Using Artificial Intelligence
Ambika N.
10.1 Introduction
10.2 Background
10.3 Literature Survey
10.4 Methodology Adopted
10.5 Analysis of the Work
10.6 Future Work
10.7 Conclusion
References
11. Role of AI and Digital Twin in Smart Manufacturing
M. Anand, T. M. Sheeba and C. Fancy
11.1 Introduction
11.2 Digital Twins
11.3 Digital Twin for Smart Manufacturing
11.3.1 Networks to Define Problems
11.3.2 Production Monitoring and Modification Using Digital Twin
11.3.3 Smart Manufacturing Policies
11.4 Artificial Intelligence Enabled with Digital Twin
11.5 AI and DT Lifecycle
11.6 AI-Enabled Digital Twins in Manufacturing
11.7 Digital Twins Used in Manufacturing
11.8 AI Impacting Digital Twins
11.9 Organizations Succeed Deploying Digital Twins
Summary
References
12. Data Analytics and Visualization in Smart Manufacturing Using AI-Based
Digital Twins
M. Sivakumar, M. Maranco, N. Krishnaraj and U. Srinivasulu Reddy
12.1 Introduction
12.2 Smart Manufacturing and Digital Twins
12.2.1 Key Concepts of Smart Manufacturing
12.2.2 Role of Digital Twins in Optimizing Manufacturing Operations
12.2.3 Benefits of Digital Twins
12.3 Data Collection and Integration
12.3.1 Sensor Data Acquisition
12.3.2 Types of Sensors
12.3.3 Data Transmission Protocols
12.3.4 Challenges Associated with Sensor Data Acquisition
12.3.5 Data Integration and Preprocessing
12.3.6 Techniques Commonly Used for each of these Preprocessing Tasks
12.4 AI-Based Analytics
12.4.1 Predictive Maintenance
12.4.2 Machine Learning and Deep Learning Techniques
12.4.3 Process Optimization
12.4.4 Optimization Algorithms
12.5 Visualization Techniques
12.5.1 Dashboard Design
12.5.2 Key Performance Indicators (KPIs)
12.5.3 Data Visualization Techniques
12.5.4 Interactive Dashboard Features
12.5.5 Augmented Reality (AR) and Virtual Reality (VR)
12.6 Case Studies and Applications
12.6.1 Case Study 1: Siemens Digital Twin for Automotive Manufacturing
12.6.2 Case Study 2: GE Aviation’s Predix Platform for Aircraft Engines
12.6.3 Case Study 3: ABB Ability™ Digital Twin for Industrial Plants
12.6.4 Case Study 4: Bosch Rexroth’s IoT Gateway and Analytics Solution
12.6.5 Insights Based on the Use Cases and Industry Trends
12.7 Challenges and Future Directions
12.7.1 Challenges
12.7.2 Future Direction
12.7.3 Emerging Trends
12.8 Conclusion
References
Part 3: Issues and Challenges Towards AI and Digital Twin-Based Smart Manufacturing
13. The IoT of Robotics: The Frontier of Automation
G. Rithik and A. Kathirvel
13.1 Internet of Robotic Things Automation
13.1.1 Artificial Intelligence
13.2 Converging Sensing/Actuating Information Network
13.2.1 Sensors and Actuators
13.2.2 Emerging IORT Technologies
13.2.3 Communication Technologies
13.2.4 Voice Recognition, Voice Control
13.2.5 Machine Learning as Enabler for Adaptive
13.3 Marketplace for an IORT Ecosystem
13.3.1 Orchestration
13.3.2 The Applications in Warehouse and E-Commerce
13.4 IORT Practical Applications in Commerce
13.4.1 Both Amusement and Wellbeing
13.4.2 Coordination
13.5 Healthcare Robotics Process Automation Paradigm
13.6 Operative Robotics
13.6.1 Radiographer Robotics
13.6.2 Rehabilitating Robotics
13.6.3 Software in Smooth Robots and Prosthetics Smooth Grippers
13.7 Vibrotactile Stimulation
13.7.1 Estimated Usage and Success Rate of Robotics in Healthcare
13.8 IoT in Transportation
13.9 Applications of IoT and AI in Agriculture Automation
13.9.1 Smart Farm
13.9.2 UAV or Drones
13.9.3 Irrigation
13.9.4 Fertilizers Application
13.9.5 Weed and Pest Control
13.9.6 Storage of Farm Products
13.10 Sustainable Agriculture
13.11 Machine Intelligence
13.12 Virtual and Augmented Reality
13.13 Integration of Digital Twins with IoT
13.14 Biomedical Application
13.15 Smart Cities
13.16 Energy Management
13.17 Intelligent Connectivity
13.18 Continual Adaptation with Safety Guarantees
13.19 Multimodal Dialogue
13.20 Industrial IoT
13.21 IoT Fire Forecast Detectors
13.22 IoT-Based Greenhouse Management
13.23 IoT Architecture Domain
13.23.1 Centralized Smart Home Architecture
13.23.2 Distributed Smart Home Architecture
13.24 Emergent Interfaces
References
14. Real-Time Monitoring and Predictive Maintenance
R. Patel, S. Shah, S. Lella and A. Gajbahar
14.1 Introduction
14.2 Fundamentals
14.2.1 Reliability
14.2.2 Availability
14.2.3 Maintainability
14.2.4 Serviceability
14.3 Type of Predictive Maintenance
14.3.1 Corrective Maintenance
14.3.2 Risk-Based Maintenance
14.3.2.1 Assessment of the Risk
14.3.2.2 Evaluation of the Risk
14.3.2.3 Risk-Based Maintenance Planning
14.3.3 Preventive Maintenance
14.3.3.1 Cost-Based Preventive Maintenance
14.3.3.2 Time-Based Preventive Maintenance
14.3.3.3 Failure-Based Preventive Maintenance
14.3.4 Condition-Based Maintenance
14.3.4.1 Process for Condition-Based Maintenance
14.3.4.2 Implementation of Condition-Based Maintenance
14.3.5 Predetermined Maintenance
14.4 Real-Time Monitoring of Industrial Components
14.4.1 Real-Time Sensing
14.4.1.1 Significance of Real-Time Sensing
14.4.1.2 Sensual Virtuosity
14.4.1.3 Dynamic Data Elevation
14.4.1.4 Environment Harmonization
14.4.1.5 Futuristic Precision
14.4.2 Real-Time Controlling
14.4.2.1 Pioneering Proactivity
14.4.2.2 Orchestrating the Symphony of Proactive Maintenance
14.4.2.3 Integration with Real-World Applications
14.4.3 Real-Time Monitoring
14.4.3.1 Importance of Real-Time Monitoring
14.4.3.2 Infrastructure and Data Processing
14.4.3.3 Fault Detection and Prediction
14.4.3.4 Practical Application in Automotive Manufacturing
14.4.4 Real-Time Predicting
14.4.4.1 The Essence of Real-Time Predicting
14.4.4.2 The Technological Backbone
14.4.4.3 Applications Across Industries
14.4.5 Real-Time Alerting
14.5 Predictive Maintenance with Real-Time Actions for Industrial Components
14.5.1 Decision-Making
14.5.2 Machine Learning for Better Prediction
14.6 Challenges and Ethical Considerations
14.7 Conclusion
References
15. Advanced Topics for Blockchain-Based Applications: Open Issues, Technical, Legal, and Research Challenges
Amit Kumar Tyagi, Shabnam Kumari and Tanuj Surve
15.1 Introduction
15.2 Open Issues Towards Blockchain-Based Applications
15.3 Critical Challenges Towards Blockchain-Based Applications
15.3.1 Technical
15.3.2 Non-Technical
15.3.3 Legal
15.3.4 Research
15.4 Future Work
15.5 Conclusion
References
16. Issues and Challenges in Implementing Smart Manufacturing in the Current Scenario
Charanya J., Shrisudhan B., Sathruba P., Sharun P. and Prakash Duraisamy
16.1 Introduction
16.1.1 The Revolution of Industry
16.1.2 Trends in Industry 4.0 Development
16.2 Challenges in Smart Manufacturing
16.2.1 Technological Challenges
16.2.2 Infrastructure Challenges
16.2 3 Regulatory and Compliance Challenges
16.2.4 Human Factors
16.3 Difficulties and Prospects for Further Research on Blockchain Application
16.3.1 Unresolved Concerns about Blockchain Technology in Industry 4.0
16.3.2 Obstacles to Blockchain Implementation in Industries and Social Structures
16.3.3 Possible Applications of Blockchain Technology in Smart Manufacturing
16.4 Obstacles in Industry 4.0
16.4.1 Challenges and Responses
16.4.2 Industry 4.0 Sustainability and Resilience
16.5 The Internet of Things: Security Threats and Challenges (IoT)
16.5.1 Difficulties within the Smart Environment Based on IoT
16.6 Conclusion
References
Part 4: Near-Future Developments Towards AI and Digital Twin-Based Smart Manufacturing
17. Artificial Intelligence for Malware Analysis: A Systematic Study
Amit Kumar Tyagi and Santosh Reddy Addula
17.1 Introduction to AI, Malware and Cybersecurity Fundamentals
17.1.1 The Importance of AI and Malware Analysis in Cybersecurity
17.1.2 Organization of the Work
17.2 AI Applications in Cybersecurity
17.2.1 Types of Malware and Attack Vectors
17.3 Existed Techniques for Malware Analysis
17.3.1 The Need for Advanced Malware Analysis in this Smart Era
17.3.2 Adversarial Attacks and Evasion Techniques
17.4 AI-Powered Malware Analysis
17.4.1 Natural Language Processing for Threat Analysis
17.5 Available Malware Datasets and Benchmarks for Malware Analysis
17.6 Open Issues and Challenges in Dataset Creation in Malware Analysis
17.7 Metrics for Evaluating AI-Based Malware Detection
17.8 Technical, Legal Challenges and Issues Towards AI-Based Malware Analysis
17.9 Future Trends and Innovations Towards AI-Based Malware Analysis
17.9.1 The Future of Cybersecurity
17.9.2 Research Gaps and Opportunities Towards AI-Based Malware Analysis
17.10 Conclusion
References
18. Artificial Intelligence-Based Cyber Security and Digital Forensics: A Review
Amit Kumar Tyagi, Shabanm Kumari and Richa
18.1 Introduction to Cybersecurity Fundamentals and Digital Forensics Basics
18.1.1 The Role of AI in Cybersecurity
18.1.2 Threat Landscape and Challenges
18.1.3 Importance of Digital Forensics in Cybersecurity
18.1.4 AI-Driven Digital Forensics
18.1.5 Organization of the Work
18.2 Background and Motivation
18.2.1 Motivation
18.3 Digital Forensics and AI
18.3.1 Automating Digital Forensic Investigations
18.3.2 AI-Powered Evidence Analysis in Cyber Security
18.3.3 Benefits, Limitation, Issues, and Challenges of AI-Based Cyber Security and Digital Forensics
18.4 AI for Threat Detection, and Prevention
18.4.1 Simulators, Algorithms/Methods for Threat Intelligence in Cybersecurity
18.4.2 AI-Driven Cybersecurity Solutions for Next Generation Society
18.5 Important Challenges and Ethical Issues for AI-Powered Cyber Security and Digital Forensics
18.6 Future Trends and Innovations AI-Powered Cyber Security and Digital Forensics
18.6.1 Emerging Technologies in AI-Driven Cybersecurity
18.6.2 Research Gaps and Future Opportunities Towards AI-Powered Cyber Security and Digital Forensics
18.6.3 Unexplored Areas in AI-Based Cybersecurity
18.7 Conclusion
References
19. Blockchain Application in Industry 5.0
D. Saveetha, Vijayakumar Ponnusamy, Nemanja Zdravković and Nandini S.M.
19.1 Introduction
19.2 Blockchain for Industry 5.0 Transformative Potential
19.3 Applications of Blockchain in Industry 5.0
19.4 Supply Chain Management and Traceability
19.4.1 Transparency and Accountability in Supply Chains
19.4.2 Tracking and Authentication of Products
19.4.3 Reduction of Counterfeit Goods
19.5 Decentralized Manufacturing
19.5.1 Distributed Manufacturing Networks
19.5.2 Intellectual Property Protection
19.5.3 Quality Control and Certification
19.6 Energy Sector Transformation
19.6.1 Peer-to-Peer Energy Trading
19.6.2 Grid Management and Efficiency
19.6.3 Renewable Energy Certificates
19.7 Intelligent Health Care and Medical Records
19.7.1 Secure and Immutable Medical Data
19.7.2 Interoperability and Data Sharing
19.7.3 Drug Supply Chain Integrity
19.8 Financial Services and Payments
19.8.1 Cross-Border Payments
19.8.2 Financial Inclusion and Access
19.8.3 Identity Verification and KYC Verification
19.9 Case Studies
19.9.1 Case Study 1 Blockchain in Supply Chain Management
19.9.2 Case Study 2 Decentralized Manufacturing with Blockchain
19.9.3 Case Study 3 Blockchain-Based Energy Trading Platform
19.9.4 Case Study 4 Blockchain for Healthcare Data Management
19.9.5 Case Study 5 Blockchain in Financial Services
19.10 Open Issue and Critical Challenges
19.10.1 Scalability and Performance
19.10.2 Regulatory and Legal Frameworks
19.10.3 Interoperability and Standardization
19.10.4 Privacy and Data Protection
19.10.5 Governance and Trust
19.11 Conclusion
References
20. Blockchain-Empowered Internet of Things (IoTs) Platforms for Automation
in Various Sectors
Santosh Reddy Addula, Amit Kumar Tyagi, Kanchan Naithani and Shabnam Kumari
20.1 Introduction
20.2 Emerging Trends and Visions Towards Blockchain and Internet of Things
20.3 Usage of Blockchain in 5G-Enabled Internet of Things
20.4 Internet of Things and Industrial Internet of Things
20.6 Blockchain Platform for Industrial Internet of Things
20.7 Benefits and Features of Industrial Internet of Things
20.8 Application and Future Work of Blockchain Platform for Industrial Internet of Things
20.9 Future of Blockchain-Based Internet of Things
20.10 Blockchain-Enabled Internet of Things (IoTs) Platforms for Supply Chain
Functions
20.11 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Energy
and Smart Grids
20.12 Blockchain-Enabled Internet of Things (IoTs) Platforms for Industrial Control Systems
20.13 Blockchain-Enabled Internet of Things (IoTs) Platforms for IoT-Based
Customer Relationship Management (CRM) and Logistics CRM
20.14 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Healthcare
20.15 Blockchain-Enabled Internet of Things (IoTs) Platforms for Digital Marketing and Online Social Networking
20.16 Blockchain-Enabled Internet of Things (IoTs) Platforms for Online Social Networking
20.17 Blockchain-Enabled Internet of Things (IoTs) Platforms for Textile Industry
20.18 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Banking and Financial Services
20.19 Blockchain-Enabled Internet of Things (IoTs) Platforms for Military Services
20.20 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Agricultural Services
20.21 Blockchain Deployment in 5G-Enabled Smart Industrial Automation
20.22 Open Issues in Blockchain-Empowered Internet of Things
20.23 Critical Challenges Towards Using Blockchain-Empowered Internet of Things
20.24 Conclusion
References
21. Digital Twin-Enabled Smart Manufacturing: Challenges and Future Directions
M. Maranco, M. Sivakumar, N. Krishnaraj, Kashyapa Abhiram Ivaturi and Nidhya R.
21.1 Introduction
21.2 Related Works
21.3 Current Landscape of Digital Twin Adoption
21.3.1 Present Used Locations
21.3.2 Benefits Associated with Adopting Digital Twins in Manufacturing Processes
21.3.2.1 Enhanced Product Quality
21.3.2.2 Increased Flexibility and Agility
21.3.2.3 Reduced Downtime and Maintenance Costs
21.3.2.4 Optimized Supply Chain Management
21.3.2.5 Enhanced Collaboration and Knowledge Sharing
21.3.3 Case Studies Illustrating Real-Life Examples of Digital Twin-Enabled Smart Manufacturing Initiatives
21.4 Challenges in Digital Twin Implementations
21.4.1 Key Challenges in Implementing Digital Twin Technologies
21.4.1.1 Data Integration and Quality
21.4.1.2 Complexity and Scalability
21.4.1.3 Interoperability
21.4.1.4 Security and Privacy
21.4.1.5 Cost and ROI Justification
21.4.1.6 Skill Gap and Change Management
21.4.1.7 Regulatory Compliance and Standards
21.4.2 Technical Challenges in Implementing Digital Twin Technologies
21.4.2.1 Data Integration
21.4.2.2 Interoperability
21.4.2.3 Scalability
21.4.2.4 Security and Privacy
21.4.2.5 Modeling and Simulation Accuracy
21.4.3 Organizational Challenges
21.4.3.1 Cultural Barriers
21.4.3.2 Skill Gaps
21.4.3.3 Change Management Issues
21.5 Future Work
21.5.1 Emerging Trends
21.5.1.1 Integration with IoT and Sensor Networks
21.5.1.2 AI and Machine Learning
21.5.1.3 Edge Computing
21.5.1.4 Digital Twin Lifecycle Management
21.5.1.5 Simulation and Virtual Testing
21.5.1.6 Blockchain Integration for Data Security
21.5.1.7 Cross-Domain Integration
21.5.1.8 Human-Digital Twin Interaction
21.5.1.9 Ethical and Regulatory Considerations
21.5.1.10 Quantum Computing
21.5.2 Prediction of Future Applications and Potential Impact on Smart Manufacturing
21.5.2.1 AI-Driven Predictive Maintenance
21.5.2.2 Digital Twins
21.5.2.3 IoT-Enabled Supply Chain Management
21.5.2.4 Robotics and Automation
21.5.2.5 Additive Manufacturing (3D Printing)
21.5.2.6 Blockchain for Supply Chain Transparency
21.5.2.7 Augmented Reality (AR) for Maintenance and Training
21.5.3 Discussion on the Role of Artificial Intelligence, Machine Learning, and Edge Computing in Enhancing Digital Twin Capabilities
21.5.3.1 Artificial Intelligence (AI)
21.5.3.2 Machine Learning (ML)
21.5.3.3 Edge Computing
21.6 Conclusion
21.6.1 Quick Recap
21.6.1.1 Opportunities
21.6.1.2 Challenges
21.6.2 Key Takeaways and Further Researches
21.6.2.1 Key Takeaways
21.6.3 Suggestions for Further Research
References
22. Future of Computer Vision and Industrial Robotics in Smart Manufacturing
Santosh Reddy Addula and Amit Kumar Tyagi
22.1 Introduction to Computer Vision and Industrial Robotics, and Smart Manufacturing
22.1.1 Evolution of Manufacturing and Automation
22.1.2 The Role of Computer Vision and Robotics in Smart Manufacturing
22.1.3 Limitations of Traditional Manufacturing Processes
22.1.4 The Need for Digital Transformation in Manufacturing
22.1.5 Labor Shortages and Efficiency Demands
22.1.6 Organization of the Work
22.2 Role of Computer Vision in Smart Manufacturing
22.2.1 Computer Vision: Definitions, Key Components, Benefits, Disadvantages
22.2.2 Applications of Computer Vision in Manufacturing
22.3 Industrial Robotics in Smart Manufacturing
22.3.1 Industrial Robots: Introduction, Types, Benefits and Key Applications
22.3.2 Robotic Automation and Collaborative Robots (Cobots) in Manufacturing
22.4 Convergence of Computer Vision and Robotics in Today’s Smart Era: Applications and Opportunities
22.5 Emerging Technologies for Computer Vision and Robotics in Smart Manufacturing
22.6 Applications of Computer Vision and Robotics in Smart Manufacturing
22.7 Security and Data Privacy Towards Computer Vision and Robotics-Based
Smart Manufacturing
22.8 Case Studies in Computer Vision and Robotics in Manufacturing
22.8.1 Computer Vision for Quality Control in Manufacturing
22.8.2 Robotics in Automotive Manufacturing
22.8.3 Collaborative Robots in Electronics Assembly in Manufacturing
22.9 Future Opportunities Towards Computer Vision and Robotics-Based Smart Manufacturing
22.10 Sustainability and Environmental Impact Towards Computer Vision and
Robotics-Based Smart Manufacturing
22.10.1 Overcoming Implementation Challenges Towards Computer Vision and Robotics in Smart Manufacturing
22.11 Conclusion
References
23. The Future of Manufacturing with AI and Data Analytics
Neel Shah, Sneh Shah, Janvi Bhanushali, Nirav Bhatt, Nikita Bhatt and Hiren Mewada
23.1 Introduction
23.2 Different Types of Maintenance Strategies
23.3 New Research Trends in Manufacturing
23.3.1 Usage of IoT in Modern Manufacturing
23.3.2 Usage of Big Data in Modern Manufacturing
23.3.3 Development of AI Technologies in Manufacturing
23.3.4 Digital Twin
23.4 Conception of Different AI Technologies
23.4.1 Artificial Intelligence and Machine Learning
23.4.2 Deep Learning
23.4.2.1 Convolution Layers (CNNs)
23.4.2.2 Transformer Architecture
23.4.3 Reinforcement Learning
23.4.4 Unsupervised Learning
23.4.5 Transfer Learning
23.4.6 State-of-the-Art Models
23.5 Digital Twins
23.6 Role of Artificial Intelligence in Predictive Maintenance
23.6.1 Types of Data Used for Predictive Maintenance
23.6.2 Synthetic Data Generation
23.6.2.1 Generative Adversarial Net (GAN)
23.6.2.2 Diffusion Models
23.6.3 Prognostic and Health Management (PHM)
23.6.3.1 Remaining Useful Time (RUL)
23.6.3.2 End of Life (EOL)
23.6.4 Anomaly Detection
23.6.5 Maintenance Strategy Optimization
23.7 Limitations and Challenges
23.8 Opportunities and Future Scope
23.9 Concluding Remarks
References
24. Artificial Intelligence Techniques in Predictive Maintenance, Their Applications, Challenges, and Prospects
Akriti Rai, Jyotika shastri and Hina Bansal
24.1 Introduction
24.2 Techniques of Predictive Maintenance
24.2.1 Time Series Analysis
24.2.2 Probability Forecasting
24.2.2.1 Steps in Probability Forecasting
24.2.3 AI Technologies Used in Predictive Maintenance
24.2.3.1 Natural Language Processing (NLP)
24.2.3.2 Machine Learning
24.2.3.3 Compute Vision
24.2.3.4 Deep Learning
24.2.3.5 Intelligent Teaching
24.2.4 Implementation of Predictive Maintenance in AI
24.2.4.1 Manufacturing
24.2.4.2 Health Care
24.2.4.3 Oil and Gas
24.2.4.4 Automotive
24.2.5 Challenges
24.2.5.1 Data Security
24.2.6 Devices
24.2.6.1 EMR’s (Electronic Medical Records)
24.2.6.2 Smart Devices, Signals, and In Vitro Diagnosis
24.3 Conclusion
References
Index
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