Factories of the Future: Technological Advancements in the Manufacturing Industry

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 Factories of the Future
1.0 Introduction
1.1 Factory of the Future
1.1.1 Plant Structure
1.1.2 Plant Digitization
1.1.3 Plant Processes
1.1.4 Industry of the Future: A Fully Integrated Industry
1.2 Current Manufacturing Environment
1.3 Driving Technologies and Market Readiness
1.4 Connected Factory, Smart Factory, and Smart Manufacturing
1.4.1 Potential Benefits of a Connected Factory
1.5 Digital and Virtual Factory
1.5.1 Digital Factory
1.5.2 Virtual Factory
1.6 Advanced Manufacturing Technologies
1.6.1 Advantages of Advanced Manufacturing Technologies
1.7 Role of Factories of the Future (FoF) in Manufacturing Performance
1.8 Socio-Econo-Techno Justification of Factories of the Future
References
Chapter 2 Industry 5.0
2.1 Introduction
2.1.1 Industry 5.0 for Manufacturing
2.1.1.1 Industrial Revolutions
2.1.2 Real Personalization in Industry 5.0
2.1.3 Industry 5.0 for Human Workers
2.2 Individualized Human-Machine-Interaction
2.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them
2.4 Concerns in Industry 5.0
2.5 Humans Closer to the Design Process of Manufacturing
2.5.1 Enablers of Industry 5.0
2.6 Challenges and Enablers (Socio-Econo-Techno Justification)
2.6.1 Social Dimension
2.6.2 Governmental and Political Dimension
2.6.3 Interdisciplinarity
2.6.4 Economic Dimension
2.6.5 Scalability
2.7 Concluding Remarks
References
Chapter 3 Machine Learning – A Survey
3.1 Introduction
3.2 Machine Learning
3.2.1 Unsupervised Machine Learning
3.2.2 Variety of Unsupervised Learning
3.2.3 Supervised Machine Learning
3.2.4 Categories of Supervised Learning
3.3 Reinforcement Machine Learning
3.3.1 Applications of Reinforcement Learning
3.3.2 Dimensionality Reduction
3.4 Importance of Dimensionality Reduction in Machine Learning
3.4.1 Methods of Dimensionality Reduction
3.4.1.1 Principal Component Analysis (PCA)
3.4.1.2 Linear Discriminant Analysis (LDA)
3.4.1.3 Generalized Discriminant Analysis (GDA)
3.5 Distance Measures
3.6 Clustering
3.6.1 Algorithms in Clustering
3.6.2 Applications of Clustering
3.6.3 Iterative Distance-Based Clustering
3.7 Hierarchical Model
3.8 Density-Based Clustering
3.8.1 DBSCAN
3.8.2 OPTICS
3.9 Role of Machine Learning in Factories of the Future
3.10 Identification of the Probable Customers
3.11 Conclusion
References
Chapter 4 Understanding Neural Networks
4.1 Introduction
4.2 Components of Neural Networks
4.2.1 Neurons
4.2.2 Synapses and Weights
4.2.3 Bias
4.2.4 Architecture of Neural Networks
4.2.5 How Do Neural Networks Work?
4.2.6 Types of Neural Networks
4.2.6.1 Artificial Neural Network (ANN)
4.2.6.2 Recurrent Neural Network (RNN)
4.2.6.3 Convolutional Neural Network (CNN)
4.2.7 Learning Techniques in Neural Network
4.2.8 Applications of Neural Network
4.2.9 Advantages of Neural Networks
4.2.10 Disadvantages of Neural Network
4.2.11 Limitations of Neural Networks
4.3 Back-Propagation
4.3.1 Working of Back-Propagation
4.3.2 Types of Back-Propagation
4.3.2.1 Static Back-Propagation
4.3.2.2 Recurrent Back-Propagation
4.3.2.3 Advantages of Back-Propagation
4.3.2.4 Disadvantages of Back-Propagation
4.4 Activation Function (AF)
4.4.1 Sigmoid Active Function
4.4.1.1 Advantages
4.4.1.2 Disadvantages
4.4.2 RELU Activation Function
4.4.2.1 Advantages
4.4.2.2 Disadvantages
4.4.3 TANH Active Function
4.4.3.1 Advantages
4.4.3.2 Disadvantages
4.4.4 Linear Function
4.4.5 Advantages
4.4.6 Disadvantages
4.4.7 Softmax Function
4.4.8 Advantages
4.5 Comparison of Activation Functions
4.6 Machine Learning
4.6.1 Applications of Machine Learning
4.7 Conclusion
References
Chapter 5 Intelligent Machining
5.1 Introduction
5.2 Requirements for the Developments of Intelligent Machining
5.3 Components of Intelligent Machining
5.3.1 Intelligent Sensors
5.3.1.1 Features of Intelligent Sensors
5.3.1.2 Functions of Intelligent Sensors
5.3.1.3 Data Acquisition and Management System to Process and Store Signals
5.3.2 Machine Learning and Knowledge Discovery Component
5.3.3 Database Knowledge Discovery
5.3.4 Programmable Logical Controller (PLC)
5.3.5 Role of Intelligent Machining for Implementation of Green Manufacturing
5.3.6 Information Integration via Knowledge Graphs
5.4 Conclusion
References
Chapter 6 Advanced Maintenance and Reliability
6.1 Introduction
6.2 Condition-Based Maintenance
6.3 Computerized Maintenance Management Systems (CMMS)
6.4 Preventive Maintenance (PM)
6.5 Predictive Maintenance (PdM)
6.6 Reliability Centered Maintenance (RCM)
6.6.1 RCM Principles
6.7 Condition Monitoring and Residual Life Prediction
6.8 Sustainability
6.8.1 Role of Sustainability in Manufacturing
6.9 Concluding Remarks
References
Chapter 7 Digital Manufacturing
7.1 Introduction
7.2 Product Life Cycle and Transition
7.3 Digital Thread
7.4 Digital Manufacturing Security
7.5 Role of Digital Manufacturing in Future Factories
7.6 Digital Manufacturing and CNC Machining
7.6.1 Introduction to CNC Machining
7.6.2 Equipment’s Used in CNC Machining
7.6.3 Analyzing Digital Manufacturing Design Considerations
7.6.4 Finishing of Part After Machining
7.7 Additive Manufacturing
7.7.1 Objective of Additive Manufacturing
7.7.2 Design Consideration
7.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries
7.9 Conclusion
References
Chapter 8 Artificial Intelligence in Machine Learning
8.1 Introduction
8.2 Case Studies
8.3 Advantages of A.I. in ML
8.4 Artificial Intelligence – Basics
8.4.1 History of A.I.
8.4.2 Limitations of Human Mind
8.4.3 Real Artificial Intelligence
8.4.4 Artificial Intelligence Subfields
8.4.5 The Positives of A.I.
8.4.6 Machine Learning
8.4.7 Machine Learning Models
8.4.8 Neural Networks
8.4.9 Constraints of Machine Learning
8.4.10 Different Kinds of Machine Learning
8.5 Application of Artificial Intelligence
8.5.1 Expert Systems
8.5.2 Natural Language Processing
8.5.3 Speech Recognition
8.5.4 Computer Vision
8.5.5 Robotics
8.6 Neural Networks (N.N.) Basics
8.6.1 Application of Neural Networks
8.6.2 Architecture of Neural Networks
8.6.3 Working of Artificial Neural Networks
8.7 Convolution Neural Networks
8.7.1 Working of Convolutional Neural Networks
8.7.2 Overview of CNN
8.7.3 Working of CNN
8.8 Image Classification
8.8.1 Concept of Image Classification
8.8.2 Type of Learning
8.8.3 Features of Image Classification
8.8.4 Examples of Image Classification
8.9 Text Classification
8.9.1 Text Classification Examples
8.9.2 Phases of Text Classification
8.9.3 Text Classification API
8.10 Recurrent Neural Network
8.10.1 Type of Recurrent Neural Network
8.11 Building Recurrent Neural Network
8.12 Long Short Term Memory Networks (LSTMs)
References
Chapter 9 Internet of Things
9.1 Introduction
9.2 M2M and Web of Things
9.3 Wireless Networks
9.4 Service Oriented Architecture
9.5 Complexity of Networks
9.6 Wireless Sensor Networks
9.7 Cloud Computing
9.8 Cloud Simulators
9.9 Fog Computing
9.10 Applications of IoT
9.11 Research Gaps and Challenges in IoT
9.12 Concluding Remarks
References
Chapter 10 Product Life Cycle
10.1 Introduction
10.2 Product Lifecycle Management (PLM)
10.2.1 Why Product Lifecycle Management?
10.2.2 Biological Product Lifecycle Stages
10.2.3 An Example Related to Stages in Product Lifecycle Management
10.2.4 Advanced Stages in Product Lifecycle Management
10.2.5 Strategies of Product Lifecycle Management
10.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies
10.3.1 Considerations in High and Low-Level Pricing
10.3.2 Penetration Pricing Strategy
10.3.3 Example for Penetration Pricing Strategy
10.3.4 Considerations in Penetration Pricing
10.4 How Do Product Lifecycle Management Work?
10.5 Application Process of Product Lifecycle Management (PLM)
10.6 Role of Unified Modelling Language (UML)
10.6.1 UML Activity Diagrams
10.7 Management of Product Information Throughout the Entire Product Lifecycle
10.8 PDM System in an Organization
10.8.1 Benefits of PDM
10.8.2 How Does the PDM Work?
10.8.3 The Services of Product Data Management
10.9 System Architecture
10.9.1 Process of System Architecture
10.10 Concepts of Model-Based System Engineering (MBSE)
10.10.1 Benefits of Model-Based System Engineering (MBSE)
10.11 Challenges of Post-COVID 19 in Manufacturing Sector
10.12 Recent Updates in Product Life Cycle
10.13 Conclusion
References
Chapter 11 Case Studies
11.1 Case Study in a Two-Wheeler Manufacturing Industry
11.1.1 Company Strategy
11.1.2 Initiatives Towards Technological Advancement
11.1.3 Management Initiatives
11.1.4 Sustainable Development Goals
11.1.5 Growth Framework with Customer Needs
11.1.6 Vision for the Future
11.2 Case Study in a Four-Wheeler Manufacturing Unit
11.2.1 Company Principles
11.2.2 Company Objectives
11.2.3 Company Strategy and Business Initiatives
11.2.4 Technology Initiatives
11.2.5 Management Initiatives
11.2.6 Quality
11.2.7 Sustainable Development Goals
11.2.8 Future Plan of Action
11.3 Conclusions
11.3.1 Limitations
11.3.2 Suggestions for Future Work
Index
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