Intelligent Manufacturing Management Systems: Operational Applications of Evolutionary Digital Technologies in Mechanical and Industrial Engineering

Description

INTRELLIGENT MANUFACTURING MANAGEMENT SYSTEMS The book explores the latest manufacturing techniques in relation to AI and evolutionary algorithms that can monitor and control the manufacturing environment. The concepts that pertain to the application of digital evolutionary technologies in the sphere of industrial engineering and manufacturing are presented in this book. A few chapters demonstrate stepwise discussion, case studies, structured literature review, rigorous experimentation results, and applications. Further chapters address the challenges encountered by industries in integrating these digital technologies into their operational activities, as well as the opportunities for this integration. In addition, the reader will find: Systemic explanations of the unique characteristics of big data, cloud computing, and AI used for decision-making in intelligent production systems;Highlights of the current and highly relevant topics in manufacturing management;Structured presentations resolving the issues being faced by many real-world applications in a broad range of areas such as smart supply chains, knowledge management, intelligent inventory management, IoT adoption in manufacturing management, and more;Intelligent techniques for sustainable practices in industrial waste management. Audience The book will be used by researchers, industry engineers, and data scientists/AI specialists working in industrial engineering, mechanical engineering, production engineering, manufacturing engineering, and operations and supply chain management. The book will also be valuable to the service sector industry, such as logistics and those implementing smart cities. Cover Title Page Copyright Page Contents Preface Part I: Smart Technologies in Manufacturing Chapter 1 Smart Manufacturing Systems for Industry 4.0 Abbreviations 1.1 Introduction 1.2 Research Methodology 1.3 Pillars of Smart Manufacturing 1.3.1 Manufacturing Technology and Processes 1.3.2 Materials 1.3.3 Data 1.3.4 Sustainability 1.3.5 Resource Sharing and Networking 1.3.6 Predictive Engineering 1.3.7 Stakeholders 1.3.8 Standardization 1.4 Enablers and Their Applications 1.4.1 Smart Design 1.4.2 Smart Machining 1.4.3 Smart Monitoring 1.4.4 Smart Control 1.4.5 Smart Scheduling 1.5 Assessment of Smart Manufacturing Systems 1.6 Challenges in Implementation of Smart Manufacturing Systems 1.6.1 Technological Issue 1.6.2 Methodological Issue 1.7 Implications of the Study for Academicians and Practitioners 1.8 Conclusion References Chapter 2 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities Abbreviations 2.1 Introduction to Smart Manufacturing 2.1.1 Background of SM 2.1.2 Traditional Manufacturing versus Smart Manufacturing 2.1.3 Concept and Evolution of Industry 4.0 2.1.4 Motivations for Research in Smart Manufacturing 2.1.5 Objectives and Need of Industry 4.0 2.1.6 Research Methodology 2.1.7 Principles of I4.0 2.1.8 Benefits/Advantages of Industry 4.0 2.2 Technology Pillars of Industry 4.0 2.2.1 Automation in Industry 4.0 2.2.1.1 Need of Automation 2.2.1.2 Components of Automation 2.2.1.3 Applications of Automation 2.2.2 Robots in Industry 4.0 2.2.2.1 Need of Robots 2.2.2.2 Advantages of Robots 2.2.2.3 Applications of Robots 2.2.2.4 Advances Robotics 2.2.3 Additive Manufacturing (AM) 2.2.3.1 Additive Manufacturing’s Potential Applications 2.2.4 Big Data Analytics 2.2.5 Cloud Computing 2.2.6 Cyber Security 2.2.6.1 Cyber-Security Challenges in Industry 4.0 2.2.7 Augmented Reality and Virtual Reality 2.2.8 Simulation 2.2.8.1 Need of Simulation in Smart Manufacturing 2.2.8.2 Advantages of Simulation 2.2.8.3 Simulation and Digital Twin 2.2.9 Digital Twins 2.2.9.1 Integration of Horizontal and Vertical Systems 2.2.10 IoT and IIoT in Industry 4.0 2.2.11 Artificial Intelligence in Industry 4.0 2.2.12 Implications of the Study for Academicians and Practitioners 2.3 Summary and Conclusions 2.3.1 Benefits of Industry 4.0 2.3.2 Challenges in Industry 4.0 2.3.3 Future Directions Acknowledgement References Chapter 3 IoT-Based Intelligent Manufacturing System: A Review 3.1 Introduction 3.2 Literature Review 3.3 Research Procedure 3.3.1 The Beginning and Advancement of SM/IM 3.3.2 Beginning of SM/IM 3.3.3 Defining SM/IM 3.3.4 Potential of SM/IM 3.3.5 Statistical Analysis of SM/IM 3.3.6 Future Endeavour of SM/IM 3.3.7 Necessary Components of IoT Framework 3.3.8 Proposed System Based on IoT 3.3.9 Development of IoT in Industry 4.0 3.4 Smart Manufacturing 3.4.1 Re-Configurability Manufacturing System 3.4.2 RMS Framework Based Upon IoT 3.4.3 Machine Control 3.4.4 Machine Intelligence 3.4.5 Innovation and the IIoT 3.4.6 Wireless Technology 3.4.7 IP Mobility 3.4.8 Network Functionality Virtualization (NFV) 3.5 Academia Industry Collaboration 3.6 Conclusions References Chapter 4 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process Abbreviations 4.1 Introduction and Literature Reviews 4.1.1 Motivation Behind the Study 4.1.2 Objective of the Chapter 4.2 Network in Smart Manufacturing System 4.2.1 Challenges for Smart Manufacturing Industries 4.2.2 Smart Manufacturing Current Market Scenario 4.3 Data Drives in Smart Manufacturing 4.3.1 Benefits of Data-Driven Manufacturing 4.4 Manufacturing of Product Through 3D Printing Process 4.4.1 3D Printing Technology 4.4.2 3D Printing Technologies Classification 4.4.3 3D Printer Parameters 4.4.4 Significance of Honeycomb Structure 4.4.5 Acrylonitrile Butadiene Styrene (ABS) Thermoplastic Polymer Used for Honeycomb Structures Model 4.4.6 3D Printing Parameters and Their Descriptions 4.5 Conclusion References Chapter 5 Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management 5.1 Introduction 5.2 Objectives 5.3 Research Methodology 5.4 Literature Review 5.5 Components of SIM 5.5.1 Supply Chain Management (SCM) 5.5.2 Inventory Management System (IMS) 5.5.3 Internet of Things (IoT) 5.5.4 RFID System 5.5.5 Maintenance, Repair, and Operations 5.5.6 Deep Reinforcement Learning 5.6 Framework 5.7 Optimization 5.7.1 Inventory Optimization 5.8 Results and Discussion 5.9 A Mirror to Researchers and Managers 5.10 Conclusions 5.11 Future Scope References Chapter 6 Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0 6.1 Introduction 6.2 Machine Learning 6.3 Smart Factory 6.4 Intelligent Machining 6.5 Machine Learning Processes Used in Machining Process 6.6 Performance Improvement of Machine Structure Using Machine Learning 6.7 Conclusions References Chapter 7 Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies Abbreviations 7.1 Introduction 7.2 Literature Review 7.3 Methodology 7.3.1 Dataset Preparation 7.3.2 CWRU Dataset 7.3.3 Methodology Flow Chart 7.3.4 Data Pre-Processing 7.3.5 Models Deployed 7.3.6 Training and Testing 7.4 Analysis 7.4.1 Datasets 7.4.2 Feature Extraction 7.4.3 Splitting of Data into Samples 7.4.4 Algorithms Used 7.4.4.1 Multinomial Logistic Regression 7.4.4.2 K-Nearest Neighbors 7.4.4.3 Decision Tree 7.4.4.4 Support Vector Machine (SVM) 7.4.4.5 Random Forest 7.5 Results and Discussion 7.5.1 Importance of Classification Reports 7.5.2 Importance of Confusion Matrices 7.5.3 Decision Tree 7.5.4 Random Forest 7.5.5 K-Nearest Neighbors 7.5.6 Logistic Regression 7.5.7 Support Vector Machine 7.5.8 Comparison of the Algorithms 7.5.8.1 Accuracies 7.5.8.2 Precision and Recall 7.6 Conclusions 7.7 Scope of Future Work References Chapter 8 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment 8.1 Introduction 8.1.1 Color Image Processing 8.1.2 Motivation 8.1.3 Objectives 8.2 Literature Review 8.2.1 Gas Turbine Power Plants 8.2.2 Artificial Intelligent Methods 8.3 Materials and Methods 8.3.1 Feature Extraction 8.3.2 Classification 8.4 Results and Discussion 8.4.1 Fisher’s Linear Discriminant Function (FLDA) and Curvelet 8.5 Conclusion 8.5.1 Future Scope of Work References Chapter 9 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate Abbreviations 9.1 Introduction 9.2 Numerical Experimentation Program 9.3 Discussion of the Results 9.4 Conclusion Acknowledgements References Part II: Integration of Digital Technologies to Operations Chapter 10 Edge Computing-Based Conditional Monitoring 10.1 Introduction 10.1.1 Problem Statement 10.2 Literature Review 10.3 Edge Computing 10.4 Methodology 10.5 Discussion 10.5.1 Predictive Maintenance 10.5.2 Energy Efficiency Management 10.5.3 Smart Manufacturing 10.5.4 Conditional Monitoring via Edge Computing Locally 10.5.5 Lesson Learned 10.6 Conclusion References Chapter 11 Optimization Methodologies in Intelligent Manufacturing Systems: Application and Challenges 11.1 Introduction 11.2 Literature Review 11.3 Intelligent Manufacturing System Framework 11.3.1 Principles of Developing Industry 4.0 Solutions 11.3.2 Quantitative Analysis 11.3.2.1 Optimization Characteristics and Requirements of Industry 4.0 11.3.3 Optimization Methodologies and Algorithms 11.4 Bayesian Networks (BNs) 11.4.1 Instance-Based Learning (IBL) 11.4.2 The IB1 Algorithm 11.4.3 Artificial Neural Networks 11.4.4 A Comparison Between Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) 11.5 Problems of Implementing Machine Learning in Manufacturing 11.6 Conclusions References Chapter 12 Challenges of Warehouse Management Towards Smart Manufacturing: A Case of an Indian Consumer Electrical Company 12.1 Introduction 12.2 Literature Review 12.2.1 Shortage of Space 12.2.2 Non-Moving Materials 12.2.3 Lack of Action on Liquidation 12.2.4 Defective Material from Both Ends 12.2.5 Gap Between the Demand and the Supply 12.2.6 Multiple Price Revision 12.2.7 More Manual Timing for Loading and Unloading 12.2.8 Operational Challenges for Seasonal Products 12.2.9 Lack of Automation 12.2.10 Manpower Balancing Between Peak and Off 12.3 The Proposed ISM Methodology 12.3.1 Establishment of the Structural Self-Interaction Matrix (SSIM) 12.3.2 Creation of the Reachability Matrix 12.3.3 Implementation of the Level Partitions 12.3.4 Classification of the Selected Challenges 12.3.5 Development of the Final ISM Model 12.4 Results and Discussion 12.5 Practical Implications 12.6 Conclusions References Chapter 13 The Impact of Organizational Ergonomics on Teaching Rapid Prototyping Abbreviations 13.1 Introduction 13.2 Organizational Ergonomics 13.2.1 Aim of Organizational Ergonomics 13.3 Rapid Prototyping and Teaching Rapid Prototyping 13.4 Industry 4.0 Factors Associated with Organizational Ergonomics in a Rapid Prototyping/Manufacturing Facility 13.4.1 Technology 13.4.2 Communication 13.4.3 Teamwork 13.4.4 Human Resource 13.4.5 Quality Management 13.5 Implications of Industry 4.0 on Prototyping and Prototyping Facilities in Design Schools 13.6 The Influence of Cooperative Working Ergonomics of Distributed Manufacturing in Teaching and Learning Rapid Prototyping 13.7 Health and Safety in Rapid Prototyping Laboratories 13.7.1 Common Health Hazards in 3D Printing 13.7.2 Chemical Hazards 13.7.3 Flammable/Explosion Hazards 13.7.4 UV and Laser Radiation Hazard 13.7.5 Other Hazards 13.7.6 Hazard Controls 13.7.7 Engineering Controls 13.7.8 Administrative Controls 13.7.9 Personal Protective Equipment 13.8 Impact of Digital/Rapid Prototyping on Organizational Ergonomics 13.9 Implications of the Study for Academicians and Practitioners 13.10 Conclusions and Future Work References Chapter 14 Sustainable Manufacturing Practices through Additive Manufacturing: A Case Study on a Can-Making Manufacturer 14.1 Introduction 14.2 Literature Review 14.3 Research Set Up 14.4 Additive Manufacturing Techniques 14.4.1 Types of Additive Manufacturing 14.4.1.1 Fused Deposition Modelling (FDM) 14.4.1.2 Stereolithography (SLA) 14.4.1.3 Selective Laser Sintering (SLS) 14.4.1.4 Direct Energy Deposition (DED) 14.4.1.5 Digital Light Processing (DLP) 14.5 Strategies Used by Production Company 14.5.1 Maintenance Strategies 14.5.1.1 Breakdown Maintenance (BM) 14.5.1.2 Preventive Maintenance (PM) 14.5.1.3 Periodic Maintenance (Time Based Maintenance – TBM) 14.5.1.4 Predictive Maintenance (PM) 14.5.1.5 Corrective Maintenance (CM) 14.5.1.6 Maintenance Prevention (PM) 14.5.2 Inventory Control in Manufacturing 14.5.2.1 Inventory Control and Maintenance in Manufacturing 14.5.2.2 Warehouse Storages 14.5.3 Time Factor in Manufacturing 14.5.3.1 Breakdown Time 14.5.3.2 Set-Up Time 14.5.3.3 Manned Time (Available Time) 14.5.3.4 Operating Working Time 14.5.3.5 Operating Time 14.5.3.6 Production Time 14.6 Sustainable Manufacturing 14.6.1 Social Aspect of Sustainable Manufacturing 14.6.2 Environmental Aspects of Sustainable Manufacturing 14.6.3 Economical Aspect of Sustainable Manufacturing 14.7 Sustainable Additive Manufacturing 14.7.1 Energy 14.7.2 Cost 14.7.2.1 Downtime Cost 14.7.3 Supply Chain 14.7.4 Maintenance with Additive Manufacturing 14.8 Additive Manufacturing with IFC CMD: A Case Study 14.9 Contribution of Additive Manufacturing Towards Sustainability 14.10 Limitations of Additive Manufacturing 14.11 Conclusions and Recommendations References Index EULA