Factories of the future : technological advancements in the manufacturing industry / edited by Chandan Deep Singh and Harleen Kaur.

Format:

Book

Contributor:

Singh, Chandan Deep, editor.

Kaur, Harleen, editor.

Language:

English

Subjects (All):

Manufacturing industries--Technological innovations.

Manufacturing industries.

Manufacturing industries--Management.

Physical Description:

1 online resource (305 pages)

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc. and Scrivener Publishing LLC, [2023]

Summary:

FACTORIES OF THE FUTURE The book provides insight into various technologies adopted and to be adopted in the future by industries and measures the impact of these technologies on manufacturing performance and their sustainability. Businesses and manufacturers face a slew of demands beyond the usual issues of staying agile and surviving in a competitive landscape within a rapidly changing world. Factories of the Future deftly takes the reader through the continuous technology changes and looks ten years down the road at what manufacturing will mostly look like. The book is divided into two parts: Emerging technologies and advancements in existing technologies. Emerging technologies consist of Industry 4.0 and 5.0 themes, machine learning, intelligent machining, advanced maintenance, reliability, and green manufacturing. The advances of existing technologies consist of digital manufacturing, artificial intelligence in machine learning, Internet of Things, product life cycle, and the impact of factories on the future of manufacturing performance of the manufacturing industries. Readers will find in this illuminating book: A comprehensive discussion of almost all emerging technologies, including "green" manufacturing; An overview of the social, economic, and technical aspects of these technologies; An explanation of these technological advancements on manufacturing performance, through case studies and other analytical tools.

Contents:

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

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

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

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

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

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

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)

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

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

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.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781119865216

1119865212

9781119865209

1119865204

OCLC:

1376933067