Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
List of Contributors
Chapter 1 Industry 4.0: Its evolution and future prospects
1.1 Introduction
1.1.1 Industry evolution 4.0
1.1.2 Smart factory
1.2 Sustainability consequences
1.2.1 Future work
1.2.2 Ecological feedback
1.3 Empirical analysis
1.4 Benefits of Industry 4.0 related to overall continual development
1.5 How Industry 4.0 will change the society
1.6 The impacts: use of todayβs technology has greatly increased productivity, personalization, and automation
1.7 New prospects and challenges
1.8 How Industry 4.0 improves the product design
1.9 Challenges and research direction
1.10 Strategy for employment of Industry 4.0
1.11 Conclusion
References
Chapter 2 Smart sensors and actuators in Industry 4.0
2.1 Introduction
2.2 Industry 4.0: relevance and implications
2.3 Smart factories
2.3.1 Benefits of smart factory
2.4 Smart factory vs. traditional factory
2.5 Sensors in smart factories
2.5.1 Active and passive sensors
2.5.2 Analog sensor
2.5.3 Digital sensor
2.5.4 Pressure sensor
2.5.5 Strain gauge sensor
2.5.6 Spring scale
2.5.7 Temperature sensor
2.5.8 Flow sensor
2.5.9 Position sensor
2.5.10 Smart sensor
2.5.11 Nuclear sensors
2.5.12 Micro-sensors (MEMS sensors)
2.5.13 Nano-sensors (NEMS)
2.5.14 Actuator
2.6 Role of smart sensors and actuators
2.7 Conclusion
References
Chapter 3 Industry 4.0 and 5.0 towards enhanced productivity and competitiveness
3.1 Introduction: Industrial revolution till Industry 4.0
3.2 Significance/need of Industry 4.0 and Industry 5.0
3.3 Basic Industrial Internet of Things (IIoT) concepts
3.4 Requirements of 5.0
3.5 Advantages of 5.0
3.6 Preparedness for Industry 4.0 and society 5.0
3.7 Preparing for Industry 4.0 and society 5.0
3.8 Limitations of Industry 4.0 and highlights of Industry 5.0
3.8.1 Industry 5.0 creates even higher-value jobs than Industry 4.0
3.8.2 Industry 4.0 focuses on productivity using technology, Industry 5.0 adds human and sustainable approaches
3.9 The European Union has defined list of technologies to support concept of Industry 5.0
3.10 How IoT, big data, and AI can enable a social transformation to some of the focus sectors
3.10.1 Healthcare
3.10.2 Mobility
3.10.3 Infrastructure
3.10.4 FinTech
3.11 Examples of Industrial Internet of Things (IIoT) along with their applications
3.12 Industry 4.0 β Legal challenges and strategies to overcome
3.12.1 Liability
3.12.2 Data protection and IT security
3.12.3 Labor laws
3.12.4 Intellectual property
3.13 New regulatory challenges and research directions
3.14 Conclusions
References
Chapter 4 Machine learning assisted manufacturing
4.1 Introduction
4.2 Industry 4.0
4.3 Machine learning
4.3.1 What is machine learning?
4.3.2 Support vector machines
4.3.3 Artificial neural networks
4.4 Machine learning in manufacturing processes
4.4.1 Machinery malfunction
4.4.2 Product quality management
4.4.3 Logistics and inventory
4.4.4 Cybersecurity
4.4.5 Environment and ecology
4.5 Fault diagnostics using artificial intelligence
4.5.1 Detecting faults in machinery
4.5.2 Product quality and management
4.6 Challenges in applying machine learning in manufacturing
References
Chapter 5 Advancement of machine learning and image processing in material science
5.1 Introduction
5.2 Historical evolution of machine learning in material science
5.3 Machine learning algorithm
5.3.1 Supervised learning
5.3.2 Unsupervised learning
5.3.3 Semi-supervised learning
5.3.4 Reinforced learning
5.3.5 Ensemble learning
5.4 Image processing technique
5.5 Problems and solutions associated with image processing
5.5.1 Smaller datasets
5.5.2 Data augmentation
5.5.3 Transfer learning
5.6 Implementation of machine learning in material science
5.7 Data ecosystem
5.7.1 MDF
5.7.2 DLHub
5.8 SWOT analysis of ML in material science
5.9 Conclusion and future scope
References
Chapter 6 Blockchain, artificial intelligence, and big data: Advanced technologies for Industry 4.0
6.1 Introduction
6.2 Blockchain
6.2.1 Blockchain in industry
6.2.2 Challenges for blockchain implementation
6.3 Artificial intelligence
6.3.1 Artificial intelligence in industry
6.3.2 Challenges for artificial intelligence implementation
6.4 Big data
6.4.1 Big data in industry
6.4.2 Challenges for big data implementation
6.5 Conclusion
References
Chapter 7 Enhanced sensor and improved connectivity as key enablers of Industry 4.0
7.1 Introduction
7.2 Enhanced sensors
7.2.1 Versatility
7.2.2 Multisensory
7.2.3 Non-invasive
7.2.4 Soft/virtual sensor
7.2.5 Big-data handling
7.2.6 Self-calibration
7.2.7 Self-testing
7.2.8 Self-learning
7.2.9 Low-cost
7.2.10 Reduced response time
7.2.11 Reduced size (miniaturization)
7.2.12 Improved accuracy
7.2.13 Reliability
7.2.14 Sensor and actuator integration (plug and play sensor)
7.3 Improved connectivity
7.3.1 Ad hoc networks
7.3.2 Data transfer rate
7.3.3 Robustness
7.3.4 Cloud communication
7.3.5 Standards and architecture
7.3.6 Horizontal and vertical integration
7.4 Conclusions
References
Annexure 7.1
Chapter 8 Reconfigurable robotic systems for Industry 4.0
8.1 Introduction
8.2 Customized robotic manipulators
8.3 Reconfigurable manufacturing systems: a broad perspective
8.4 Modular robotic systems
8.4.1 Design challenges
8.4.2 Basic classifications of modular links
8.4.3 Module architecture
8.4.4 Assembly planning
8.4.5 Multi-layer approach to modularity
8.5 Workspace reconstruction framework
References
Chapter 9 Introduction to additive manufacturing: Concepts, challenges, and future scope
9.1 Introduction
9.2 Additive manufacturing
9.3 The general AM process chain
9.3.1 CAD model
9.3.2 Conversion to STL
9.3.3 Transfer and manipulation of STL file to AM machine
9.3.4 Machine setup
9.3.5 Build
9.3.6 Removal and cleaning
9.3.7 Post processing
9.3.8 Application
9.4 Additive manufacturing technologies
9.4.1 Vat polymerization
9.4.2 Material jetting
9.4.3 Binder jetting
9.4.4 Material extrusion
9.4.5 Powder bed fusion
9.4.6 Sheet lamination
9.4.7 Direct energy deposition
9.5 Design for AM
9.5.1 Residual stress
9.5.2 Support structures
9.5.3 Part orientation
9.6 Future scope of AM
9.7 Conclusion
References
Chapter 10 Recycling of metal/polymer waste as a feedstock material via additive manufacturing technology: A review
10.1 Introduction
10.2 Steps involved, advantages, and limitations of AM
10.3 Additive manufacturing processes
10.3.1 Selective laser sintering (SLS)
10.3.2 Fused deposition melting (FDM)
10.3.3 Stereolithography
10.3.4 Binder jetting
10.3.5 Laminated object manufacturing
10.4 Cold spray process
10.5 Common materials used in AM
10.5.1 Polymers
10.5.2 Metals
10.6 Discussion
10.7 Conclusion
Acknowledgment
Abbreviations
References
Chapter 11 Tool condition monitoring in mechanical micromachining
11.1 Introduction
11.1.1 Micromachining
11.2 Tool condition monitoring (TCM)
11.2.1 Acoustic emission (AE) sensor
11.2.2 AE signal analysis
11.2.3 Accelerometer sensor
11.2.4 Cutting force sensor
11.3 Tool monitoring
11.4 Temperature sensors
11.5 Tool condition monitoring in mechanical micromachining
11.6 Tool condition monitoring using multiple sensors
11.6.1 Advantages of tool condition monitoring
11.7 Research direction
11.8 Conclusion
References
Index