Intelligent manufacturing management systems : operational applications of evolutionary digital technologies in mechanical and industrial engineering / / edited by Kamalakanta Muduli [and four others]

• Pubblicazione: Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]; ©2023
• Descrizione fisica: 1 online resource (402 pages)
• Disciplina: 006.3
• Soggetto topico: Production control
• Persona (resp. second.): MuduliKamalakanta
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Intelligent manufacturing management systems
• ISBN: 1-119-83678-6; 1-119-83677-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830450703321
• Lo trovi qui: Univ. Federico II