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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.
List of contents
Preface xiii
1 Factories of the Future 1
Talwinder Singh and Davinder Singh
1.0 Introduction 2
1.1 Factory of the Future 3
1.1.1 Plant Structure 3
1.1.2 Plant Digitization 4
1.1.3 Plant Processes 4
1.1.4 Industry of the Future: A Fully Integrated Industry 5
1.2 Current Manufacturing Environment 6
1.3 Driving Technologies and Market Readiness 8
1.4 Connected Factory, Smart Factory, and Smart Manufacturing 11
1.4.1 Potential Benefits of a Connected Factory 13
1.5 Digital and Virtual Factory 13
1.5.1 Digital Factory 13
1.5.2 Virtual Factory 14
1.6 Advanced Manufacturing Technologies 14
1.6.1 Advantages of Advanced Manufacturing Technologies 16
1.7 Role of Factories of the Future (FoF) in Manufacturing Performance 17
1.8 Socio-Econo-Techno Justification of Factories of the Future 17
References 18
2 Industry 5.0 21
Talwinder Singh, Davinder Singh, Chandan Deep Singh and Kanwaljit Singh
2.1 Introduction 22
2.1.1 Industry 5.0 for Manufacturing 22
2.1.1.1 Industrial Revolutions 23
2.1.2 Real Personalization in Industry 5.0 25
2.1.3 Industry 5.0 for Human Workers 28
2.2 Individualized Human-Machine-Interaction 29
2.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them 31
2.4 Concerns in Industry 5.0 32
2.5 Humans Closer to the Design Process of Manufacturing 35
2.5.1 Enablers of Industry 5.0 36
2.6 Challenges and Enablers (Socio-Econo-Techno Justification) 37
2.6.1 Social Dimension 37
2.6.2 Governmental and Political Dimension 38
2.6.3 Interdisciplinarity 40
2.6.4 Economic Dimension 40
2.6.5 Scalability 41
2.7 Concluding Remarks 42
References 43
3 Machine Learning - A Survey 47
Navdeep Singh and Aanchal Goyal
3.1 Introduction 48
3.2 Machine Learning 49
3.2.1 Unsupervised Machine Learning 50
3.2.2 Variety of Unsupervised Learning 51
3.2.3 Supervised Machine Learning 52
3.2.4 Categories of Supervised Learning 54
3.3 Reinforcement Machine Learning 54
3.3.1 Applications of Reinforcement Learning 56
3.3.2 Dimensionality Reduction 57
3.4 Importance of Dimensionality Reduction in Machine Learning 58
3.4.1 Methods of Dimensionality Reduction 58
3.4.1.1 Principal Component Analysis (PCA) 58
3.4.1.2 Linear Discriminant Analysis (LDA) 59
3.4.1.3 Generalized Discriminant Analysis (GDA) 61
3.5 Distance Measures 61
3.6 Clustering 65
3.6.1 Algorithms in Clustering 67
3.6.2 Applications of Clustering 68
3.6.3 Iterative Distance-Based Clustering 69
3.7 Hierarchical Model 70
3.8 Density-Based Clustering 72
3.8.1 Dbscan 72
3.8.2 Optics 73
3.9 Role of Machine Learning in Factories of the Future 74
3.10 Identification of the Probable Customers 75
3.11 Conclusion 78
References 79
4 Understanding Neural Networks 83
Er. Lal Chand, Sikander Singh Cheema and Manpreet Kaur
4.1 Introduction 83
4.2 Components of Neural Networks 84
4.2.1 Neurons 85
4.2.2 Synapses and Weights 86
4.2.3 Bias 86
4.2.4 Architecture of Neural Networks 86
4.2.5 How Do Neural Networks Work? 87
4.2.6 Types of Neural Networks 88
4.2.6.1 Artificial Neural Network (ANN) 88
4.2.6.2 Recurre
About the author
Audience The book will be read by academic researchers and industry engineers, managers, and specialists in industrial manufacturing and production, mechanical and electronics engineering and their allied disciplines. It will also be helpful to those in industrial R&D departments, as industries are always adopting new technologies and advancements are continually made in this sector.
Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. He has published over 100 papers in various peer-reviewed international journals and conferences.
Harleen Kaur, PhD, is doing project work with the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. Previously, she worked as a manager at DELBREC Industries, Pvt. Ltd., as well as an assistant professor of management at Asra Institute of Advanced Studies, Bhawanigarh, India.
