Industry automation : the technologies, platforms and use cases / editors, Pethuru Raj, Abhishek Kumar, Ananth Kumar, Neha Singhal.
This book details cutting-edge technologies, versatile tools, adaptive processes, integrated platforms, and best practices of digitized systems. With the faster maturity and stability of digitization and digitalization technologies, all kinds of physical, mechanical, and electrical systems in our ev...
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Full Text (via Taylor & Francis) |
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| Other Authors: | , , , |
| Format: | eBook |
| Language: | English |
| Published: |
[United States] :
River Publishers,
[2024]
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| Series: | River Publishers series in automation, control and robotics.
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Table of Contents:
- Preface xvii List of Figures xxi List of Tables xxvii List of Contributors xxix List of Abbreviations xxxiii 1 An Analytical Framework for the Industrial Internet of Things (IIoT): Importance, Recent Challenges, and Enabling Technologies 1 1.1 Introduction 2 1.1.1 Industrial automation with IoT 4 1.1.2 Objective 6 1.2 Literature Survey 6 1.2.1 Industry 4.0 10 1.3 Enabling Technologies for IIoT 13 1.3.1 Blockchain technology 13 1.3.2 Cloud computing 13 1.3.3 Big data analytics 14 1.3.4 Artificial intelligence and cyber-physical systems 14 1.3.5 Augmented and virtual reality 15 1.4 Framework and Case Studies 15 1.4.1 SnappyData 16 1.4.2 Fault detection classification 16 1.5 Challenges in IIoT 16 1.5.1 Schemes for efficient data storage 16 1.5.2 IoT systems from different vendors working together 17 1.5.3 Adaptable and resilient technologies for analyzing large datasets 17 1.5.4 Trust in IIoT systems 18 1.5.5 Integration of wireless technologies and protocols in the Internet of Things (IIoT) 18 1.5.6 The edge of decentralization 18 1.5.7 New operating systems for the Internet of Things 19 1.5.8 Public safety in IIoT 19 1.6 Application for IIoT Framework 20 1.7 Conclusion and Future Scope 20 2 Industry Automation: The Contributions of Artificial Intelligence (AI) 25 2.1 Introduction 26 2.2 Automation Systems Potential 29 2.3 Application Landscape and Production-related Scenarios 30 2.3.1 Autonomy-level classification of industrial AI applications 31 2.4 Impact of Artificial Intelligence in Industry4.0 (I4.0) 33 2.4.1 Order-controlled production (OCP) 33 2.4.2 Smart production (SP2) 34 2.4.3 Innovative product development (IPD) 35 2.4.4 Seamless and dynamic engineering of plants (SDP) 36 2.4.5 Circular economy (CRE) 37 2.4.6 5G for digital factories ⁰́₃ mobile controlled production (MCP) 39 2.5 Industry Use Cases for AI-enabled Collaboration 40 2.5.1 Artificial intelligence in healthcare industry 40 2.5.1.1 The use of predictive analytics to confirm the need for surgery 40 2.5.1.2 Intelligent surgical robots 42 2.5.2 Artificial intelligence in manufacturing and factories 42 2.5.2.1 Analytical services for advanced data 42 2.5.2.2 Predictive maintenance 43 2.5.2.3 Automation of robotic processes 44 2.5.3 Artificial intelligence in automobile 45 2.5.3.1 The use of artificial intelligence to improve design 45 2.5.3.2 AI application in manufacturing 45 2.5.3.3 Examples of AI in manufacturing ⁰́₃ inspiring changes 46 2.5.4 Application of artificial intelligence in quality control 47 2.5.5 Manufacturing industry trends with emerging AI 49 2.5.6 The Internet of Things is emerging as Industry 4.0's future 51 2.5.7 Future scope of research 52 2.6 Conclusion 53 3 Industry Automation: The Contributions of Artificial Intelligence 57 3.1 Introduction 58 3.2 Literature Review 59 3.3 Industry 5.0 and AI 60 3.4 Problems with Human ⁰́₃ Robot Collaboration 64 3.4.1 Issues with law and regulation 65 3.4.2 Subjective opinion for using robots at work 66 3.4.3 Psychosocial problems caused by human ⁰́₃ robot collaboration 66 3.4.4 Changes that result from human ⁰́₃ robot collaboration 66 3.4.5 The shifting functions of human resources divisions 67 3.5 Wafer Fabrication Automation 68 3.6 AI as a Vital Technology in Industry 5.0 69 3.6.1 Impact of AI on different industries 69 3.7 Artificial General Intelligence (AGI) 71 3.8 The Scenario of AI in the Focus of Manufacturing 71 3.9 Automation based on AI (ABAI) 72 3.9.1 Computerized root cause analysis using AI 73 3.9.2 Intelligent computing in product matching 75 3.10 Robotic Process Automation 76 3.11 The Digital Solutions Entangled in Industry 5.0 77 3.12 AMS for Industry 5.0: Advanced Manufacturing System 78 3.13 Methods, Data, and Results 78 3.14 Conclusion 80 4 Artificial Intelligence (AI) Driven Industrial Automation 85 4.1 Introduction 86 4.2 Evolution of Artificial Intelligence 87 4.3 Industry 4.0 Technologies 87 4.4 Development in AI 88 4.5 AI Future Perception 91 4.6 Digital Transformation 91 4.7 Components of AI in Automation 92 4.8 Artificial Intelligence Applications in Automation 93 4.9 Automation and AI 97 4.10 Conclusion 98 5 Quantum Machine and Deep Learning Models for Industry Automation 101 5.1 Introduction 102 5.2 Difference Between Classical and Quantum Data 104 5.3 Quantum Computing 104 5.3.1 Qubit 105 5.3.2 Superposition 105 5.3.3 Entanglement 105 5.4 Quantum Machine Learning (QML) 106 5.5 Classical Machine Learning vs. Quantum Computing 107 5.5.1 Linear algebra problems have been solved via quantum machine learning 107 5.6 Quantum Thinking in Depth 108 5.6.1 Principal component analysis in quantum 109 5.6.2 Support vector quantum machines 110 5.6.3 Optimization 110 5.7 Quantum Learning in Depth 111 5.7.1 Why is quantum machine learning so exciting? 111 5.8 The Essence of Quantum Computing 113 5.8.1 Taking the initiative to manage uncertainty 113 5.8.2 Welcoming a new AI era 113 5.8.3 Cybersecurity advancement 113 5.8.4 Accuracy of weather predictions 114 5.8.5 A signal to develop better life-saving drugs 114 5.9 A Portal to Exciting Future Technology 114 5.9.1 How AI will change thanks to quantum computing 114 5.9.2 Processes for making better business decisions 115 5.9.3 Quantum security and artificial intelligence 115 5.9.4 AI and quantum computing complement DevOps 116 5.9.5 Where are our IT systems vulnerable? 116 5.9.6 Limitation of quantum machine learning 116 5.9.7 Hardware constraints 117 5.9.8 Program restrictions 117 5.10 More on Quantum Computing and Machine Learning Connections 118 5.10.1 Wavefunction 118 5.10.2 The significance of accuracy 119 5.10.3 Data power and quantum machine learning 121 5.11 Case Study 123 5.11.1 Q-SVM (quantum support vector machine algorithm) 123 5.11.2 Why did they need Q-SVM? 124 5.11.3 Import the library 124 5.11.4 Install the dataset 125 5.12 Quantum Computing and Machine Learning for Industry Automation 127 5.12.1 Discover 128 5.12.2 Design 128 5.12.3 Control 128 5.12.4 Supply chains 129 5.12.5 How does manufacturing begin? 129 5.13 Conclusion and Future Scope 130 6 The Contribution of Computer Vision in the Manufacturing Industries and the Scope for Further Excellence 135 6.1 Introduction 136 6.2 Components of a Machine Vision Systems 138 6.3 Image Formation 141 6.4 Computer vision algorithms 141 6.5 Use Case of the Computer Vision in Industries 141 6.5.1 Product assembly 141 6.5.2 Defect detection 142 6.5.3 3D Vision system 144 6.5.4 Vision-guided robots 144 6.5.5 Predictive maintenance 145 6.6 Safety and Security Standards 145 6.7 Packaging Standards 147 6.8 Barcode Analysis 148 6.9 Inventory Management 148 6.10 Optimizing Supply Chains 149 6.11 Quality Inspection with Computer Vision 149 6.12 Computer Vision during the Covid-19 Pandemic 150 6.13 Computer Vision in the Automotive Industry 151 6.13.1 Press shop 152 6.13.2 Body shop 153 6.13.3 Paint shop 154 6.13.4 Final assembly shop 154 6.14 Computer Vision Performance Metrics 155 6.14.1 Intersection over union (IoU) 155 6.14.2 Precision 155 6.14.3 Recall 156 6.14.4 F1 score 156 6.15 Conclusion 158 7 Waste Management 4.0: An Industry Automation Approach to the FutureWaste Management System 163 7.1 Introduction 164 7.2 Exploring CPS 166 7.2.1 CPS 166 7.2.2 Drawbacks of CPS 167 7.3 Industry 4.0 Environment 168 7.4 Challenges in the Waste Management Industry 169 7.5 Applications of CPS in the Waste Management Industry 171 7.6 Influence of Industry 4.0 on the Waste Management Industry 172 7.7 Barriers to Implementing Industry 4.0 in the Waste Management Industry 174 7.8 Case Study: Machine Learning for Waste Management 175 7.9 Conclusion 177 8 Industrial Internet of Things (IIoT) for E-waste Recycling System 181 8.1 Introduction 181 8.2 Background Study 182 8.3 IIoT Working 182 8.4 IIoT Security 183 8.4.1 Risks and challenges of IIoT 183 8.4.2 Difference between IoT and IIoT 184 8.4.3 IIoT applications and examples 184 8.5 Industries using IIoT 185 8.6 Advantages and Disadvantages of IIoT 186 8.6.1 Hindrances of IIoT 188 8.7 Case Study - IoT for E-waste Recycling System 189 8.8 Future Trends of IIoT 192 8.9 Conclusion 195 9 A Multi-hazard Industry Assessment System Based on Unmanned Aerial Vehicles (UAVs) for Bridges Crossing Seasonal Rivers 199 9.1 Introduction 200 9.2 Literature Survey 203 9.3 Methodology 206 9.3.1 UAV-derived DEM generation by 3D style 206 9.3.2 Hydrodynamic analyses 207 9.3.3 3D FEM generation 208 9.3.4 Tectonic evaluation 208 9.3.5 Soil modeling
- 208 9.3.6 Bridge modeling 211 9.4 Result and Discussion 213 9.4.1 Scour depth and flood load calculations by hydraulic modeling 213 9.5 Conclusion 219 10 Air Quality Prediction using Machine Learning Techniques for Intelligent Monitoring Systems 223 10.1 Introduction 224 10.2 Materials and Methods 226 10.3 Results and Discussion 233 10.4 Conclusion 233 11 Facial Emotion Classification for Industry Automation using Convolutional Neural Networks 237 11.1 Introduction 237 11.2 Related Works 238 11.3 Dataset Description 242 11.4 Model Architect ...