{"product_id":"intelligent-manufacturing-management-systems-9781119836247","title":"Intelligent Manufacturing Management Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eINTRELLIGENT MANUFACTURING MANAGEMENT SYSTEMS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eThe book explores the latest manufacturing techniques in relation to AI and evolutionary algorithms that can monitor and control the manufacturing environment.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eIn addition, the reader will find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eSystemic explanations of the unique characteristics of big data, cloud computing, and AI used for decision-making in intelligent production systems;\u003c\/li\u003e \u003cli\u003eHighlights of the current and highly relevant topics in manufact\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Smart Technologies in Manufacturing 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Smart Manufacturing Systems for Industry 4.0 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGaijinliu Gangmei and Polash Pratim Dutta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 3\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.2 Research Methodology 5\u003c\/p\u003e \u003cp\u003e1.3 Pillars of Smart Manufacturing 6\u003c\/p\u003e \u003cp\u003e1.3.1 Manufacturing Technology and Processes 6\u003c\/p\u003e \u003cp\u003e1.3.2 Materials 7\u003c\/p\u003e \u003cp\u003e1.3.3 Data 8\u003c\/p\u003e \u003cp\u003e1.3.4 Sustainability 8\u003c\/p\u003e \u003cp\u003e1.3.5 Resource Sharing and Networking 9\u003c\/p\u003e \u003cp\u003e1.3.6 Predictive Engineering 9\u003c\/p\u003e \u003cp\u003e1.3.7 Stakeholders 10\u003c\/p\u003e \u003cp\u003e1.3.8 Standardization 10\u003c\/p\u003e \u003cp\u003e1.4 Enablers and Their Applications 11\u003c\/p\u003e \u003cp\u003e1.4.1 Smart Design 12\u003c\/p\u003e \u003cp\u003e1.4.2 Smart Machining 12\u003c\/p\u003e \u003cp\u003e1.4.3 Smart Monitoring 13\u003c\/p\u003e \u003cp\u003e1.4.4 Smart Control 13\u003c\/p\u003e \u003cp\u003e1.4.5 Smart Scheduling 14\u003c\/p\u003e \u003cp\u003e1.5 Assessment of Smart Manufacturing Systems 14\u003c\/p\u003e \u003cp\u003e1.6 Challenges in Implementation of Smart Manufacturing Systems 15\u003c\/p\u003e \u003cp\u003e1.6.1 Technological Issue 16\u003c\/p\u003e \u003cp\u003e1.6.2 Methodological Issue 16\u003c\/p\u003e \u003cp\u003e1.7 Implications of the Study for Academicians and Practitioners 17\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 17\u003c\/p\u003e \u003cp\u003eReferences 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities 23\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Deepak Kumar, G. Arun Manohar, R. Surya Teja, P. S. V. Ramana Rao, A. Mandal, Ajit Behera and P. Srinivasa Rao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 24\u003c\/p\u003e \u003cp\u003e2.1 Introduction to Smart Manufacturing 24\u003c\/p\u003e \u003cp\u003e2.1.1 Background of SM 24\u003c\/p\u003e \u003cp\u003e2.1.2 Traditional Manufacturing versus Smart Manufacturing 25\u003c\/p\u003e \u003cp\u003e2.1.3 Concept and Evolution of Industry 4.0 25\u003c\/p\u003e \u003cp\u003e2.1.4 Motivations for Research in Smart Manufacturing 28\u003c\/p\u003e \u003cp\u003e2.1.5 Objectives and Need of Industry 4.0 29\u003c\/p\u003e \u003cp\u003e2.1.6 Research Methodology 30\u003c\/p\u003e \u003cp\u003e2.1.7 Principles of I4. 0 30\u003c\/p\u003e \u003cp\u003e2.1.8 Benefits\/Advantages of Industry 4.0 31\u003c\/p\u003e \u003cp\u003e2.2 Technology Pillars of Industry 4.0 31\u003c\/p\u003e \u003cp\u003e2.2.1 Automation in Industry 4.0 33\u003c\/p\u003e \u003cp\u003e2.2.1.1 Need of Automation 33\u003c\/p\u003e \u003cp\u003e2.2.1.2 Components of Automation 33\u003c\/p\u003e \u003cp\u003e2.2.1.3 Applications of Automation 34\u003c\/p\u003e \u003cp\u003e2.2.2 Robots in Industry 4.0 34\u003c\/p\u003e \u003cp\u003e2.2.2.1 Need of Robots 35\u003c\/p\u003e \u003cp\u003e2.2.2.2 Advantages of Robots 35\u003c\/p\u003e \u003cp\u003e2.2.2.3 Applications of Robots 37\u003c\/p\u003e \u003cp\u003e2.2.2.4 Advances Robotics 37\u003c\/p\u003e \u003cp\u003e2.2.3 Additive Manufacturing (AM) 38\u003c\/p\u003e \u003cp\u003e2.2.3.1 Additive Manufacturing’s Potential Applications 39\u003c\/p\u003e \u003cp\u003e2.2.4 Big Data Analytics 40\u003c\/p\u003e \u003cp\u003e2.2.5 Cloud Computing 41\u003c\/p\u003e \u003cp\u003e2.2.6 Cyber Security 43\u003c\/p\u003e \u003cp\u003e2.2.6.1 Cyber-Security Challenges in Industry 4.0 43\u003c\/p\u003e \u003cp\u003e2.2.7 Augmented Reality and Virtual Reality 44\u003c\/p\u003e \u003cp\u003e2.2.8 Simulation 46\u003c\/p\u003e \u003cp\u003e2.2.8.1 Need of Simulation in Smart Manufacturing 46\u003c\/p\u003e \u003cp\u003e2.2.8.2 Advantages of Simulation 47\u003c\/p\u003e \u003cp\u003e2.2.8.3 Simulation and Digital Twin 47\u003c\/p\u003e \u003cp\u003e2.2.9 Digital Twins 47\u003c\/p\u003e \u003cp\u003e2.2.9.1 Integration of Horizontal and Vertical Systems 48\u003c\/p\u003e \u003cp\u003e2.2.10 IoT and IIoT in Industry 4.0 48\u003c\/p\u003e \u003cp\u003e2.2.11 Artificial Intelligence in Industry 4.0 49\u003c\/p\u003e \u003cp\u003e2.2.12 Implications of the Study for Academicians and Practitioners 51\u003c\/p\u003e \u003cp\u003e2.3 Summary and Conclusions 51\u003c\/p\u003e \u003cp\u003e2.3.1 Benefits of Industry 4.0 51\u003c\/p\u003e \u003cp\u003e2.3.2 Challenges in Industry 4.0 52\u003c\/p\u003e \u003cp\u003e2.3.3 Future Directions 52\u003c\/p\u003e \u003cp\u003eAcknowledgement 53\u003c\/p\u003e \u003cp\u003eReferences 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 IoT-Based Intelligent Manufacturing System: A Review 59\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Pritam Chakraborty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 60\u003c\/p\u003e \u003cp\u003e3.2 Literature Review 60\u003c\/p\u003e \u003cp\u003e3.3 Research Procedure 64\u003c\/p\u003e \u003cp\u003e3.3.1 The Beginning and Advancement of SM\/IM 64\u003c\/p\u003e \u003cp\u003e3.3.2 Beginning of SM\/IM 64\u003c\/p\u003e \u003cp\u003e3.3.3 Defining SM\/IM 65\u003c\/p\u003e \u003cp\u003e3.3.4 Potential of SM\/IM 66\u003c\/p\u003e \u003cp\u003e3.3.5 Statistical Analysis of SM\/IM 68\u003c\/p\u003e \u003cp\u003e3.3.6 Future Endeavour of SM\/IM 68\u003c\/p\u003e \u003cp\u003e3.3.7 Necessary Components of IoT Framework 69\u003c\/p\u003e \u003cp\u003e3.3.8 Proposed System Based on IoT 71\u003c\/p\u003e \u003cp\u003e3.3.9 Development of IoT in Industry 4.0 72\u003c\/p\u003e \u003cp\u003e3.4 Smart Manufacturing 73\u003c\/p\u003e \u003cp\u003e3.4.1 Re-Configurability Manufacturing System 73\u003c\/p\u003e \u003cp\u003e3.4.2 RMS Framework Based Upon IoT 75\u003c\/p\u003e \u003cp\u003e3.4.3 Machine Control 76\u003c\/p\u003e \u003cp\u003e3.4.4 Machine Intelligence 77\u003c\/p\u003e \u003cp\u003e3.4.5 Innovation and the IIoT 78\u003c\/p\u003e \u003cp\u003e3.4.6 Wireless Technology 78\u003c\/p\u003e \u003cp\u003e3.4.7 IP Mobility 78\u003c\/p\u003e \u003cp\u003e3.4.8 Network Functionality Virtualization (NFV) 79\u003c\/p\u003e \u003cp\u003e3.5 Academia Industry Collaboration 79\u003c\/p\u003e \u003cp\u003e3.6 Conclusions 80\u003c\/p\u003e \u003cp\u003eReferences 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process 85\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKali Charan Rath, Prasenjit Chatterjee, Pankajkumar Munibara Patro, Polaiah Bojja, Amaresh Kumar and Rashmi Prava Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 86\u003c\/p\u003e \u003cp\u003e4.1 Introduction and Literature Reviews 86\u003c\/p\u003e \u003cp\u003e4.1.1 Motivation Behind the Study 88\u003c\/p\u003e \u003cp\u003e4.1.2 Objective of the Chapter 89\u003c\/p\u003e \u003cp\u003e4.2 Network in Smart Manufacturing System 89\u003c\/p\u003e \u003cp\u003e4.2.1 Challenges for Smart Manufacturing Industries 90\u003c\/p\u003e \u003cp\u003e4.2.2 Smart Manufacturing Current Market Scenario 93\u003c\/p\u003e \u003cp\u003e4.3 Data Drives in Smart Manufacturing 93\u003c\/p\u003e \u003cp\u003e4.3.1 Benefits of Data-Driven Manufacturing 94\u003c\/p\u003e \u003cp\u003e4.4 Manufacturing of Product Through 3D Printing Process 97\u003c\/p\u003e \u003cp\u003e4.4.1 3D Printing Technology 99\u003c\/p\u003e \u003cp\u003e4.4.2 3D Printing Technologies Classification 100\u003c\/p\u003e \u003cp\u003e4.4.3 3D Printer Parameters 101\u003c\/p\u003e \u003cp\u003e4.4.4 Significance of Honeycomb Structure 102\u003c\/p\u003e \u003cp\u003e4.4.5 Acrylonitrile Butadiene Styrene (ABS) Thermoplastic Polymer Used for Honeycomb Structures Model 103\u003c\/p\u003e \u003cp\u003e4.4.6 3D Printing Parameters and Their Descriptions 107\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 107\u003c\/p\u003e \u003cp\u003eReferences 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management 113\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHiranmoy Samanta and Kamal Golui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 114\u003c\/p\u003e \u003cp\u003e5.2 Objectives 114\u003c\/p\u003e \u003cp\u003e5.3 Research Methodology 114\u003c\/p\u003e \u003cp\u003e5.4 Literature Review 115\u003c\/p\u003e \u003cp\u003e5.5 Components of SIM 116\u003c\/p\u003e \u003cp\u003e5.5.1 Supply Chain Management (SCM) 116\u003c\/p\u003e \u003cp\u003e5.5.2 Inventory Management System (IMS) 117\u003c\/p\u003e \u003cp\u003e5.5.3 Internet of Things (IoT) 120\u003c\/p\u003e \u003cp\u003e5.5.4 RFID System 121\u003c\/p\u003e \u003cp\u003e5.5.5 Maintenance, Repair, and Operations 123\u003c\/p\u003e \u003cp\u003e5.5.6 Deep Reinforcement Learning 125\u003c\/p\u003e \u003cp\u003e5.6 Framework 127\u003c\/p\u003e \u003cp\u003e5.7 Optimization 130\u003c\/p\u003e \u003cp\u003e5.7.1 Inventory Optimization 130\u003c\/p\u003e \u003cp\u003e5.8 Results and Discussion 131\u003c\/p\u003e \u003cp\u003e5.9 A Mirror to Researchers and Managers 132\u003c\/p\u003e \u003cp\u003e5.10 Conclusions 133\u003c\/p\u003e \u003cp\u003e5.11 Future Scope 133\u003c\/p\u003e \u003cp\u003eReferences 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0 141\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBikash Chandra Behera, Bikash Ranjan Moharana, Matruprasad Rout and Kishore Debnath\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 142\u003c\/p\u003e \u003cp\u003e6.2 Machine Learning 143\u003c\/p\u003e \u003cp\u003e6.3 Smart Factory 146\u003c\/p\u003e \u003cp\u003e6.4 Intelligent Machining 148\u003c\/p\u003e \u003cp\u003e6.5 Machine Learning Processes Used in Machining Process 150\u003c\/p\u003e \u003cp\u003e6.6 Performance Improvement of Machine Structure Using Machine Learning 152\u003c\/p\u003e \u003cp\u003e6.7 Conclusions 153\u003c\/p\u003e \u003cp\u003eReferences 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies 157\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJagadeesha T., Thutupalli Srinivasa Advaith, Choppala Sarath Wesley, Grandhi Sri Sai Charith and Doppalapudi Manohar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 158\u003c\/p\u003e \u003cp\u003e7.1 Introduction 158\u003c\/p\u003e \u003cp\u003e7.2 Literature Review 159\u003c\/p\u003e \u003cp\u003e7.3 Methodology 161\u003c\/p\u003e \u003cp\u003e7.3.1 Dataset Preparation 161\u003c\/p\u003e \u003cp\u003e7.3.2 CWRU Dataset 161\u003c\/p\u003e \u003cp\u003e7.3.3 Methodology Flow Chart 161\u003c\/p\u003e \u003cp\u003e7.3.4 Data Pre-Processing 162\u003c\/p\u003e \u003cp\u003e7.3.5 Models Deployed 163\u003c\/p\u003e \u003cp\u003e7.3.6 Training and Testing 163\u003c\/p\u003e \u003cp\u003e7.4 Analysis 164\u003c\/p\u003e \u003cp\u003e7.4.1 Datasets 164\u003c\/p\u003e \u003cp\u003e7.4.2 Feature Extraction 168\u003c\/p\u003e \u003cp\u003e7.4.3 Splitting of Data into Samples 168\u003c\/p\u003e \u003cp\u003e7.4.4 Algorithms Used 169\u003c\/p\u003e \u003cp\u003e7.4.4.1 Multinomial Logistic Regression 169\u003c\/p\u003e \u003cp\u003e7.4.4.2 K-Nearest Neighbors 170\u003c\/p\u003e \u003cp\u003e7.4.4.3 Decision Tree 172\u003c\/p\u003e \u003cp\u003e7.4.4.4 Support Vector Machine (SVM) 173\u003c\/p\u003e \u003cp\u003e7.4.4.5 Random Forest 175\u003c\/p\u003e \u003cp\u003e7.5 Results and Discussion 177\u003c\/p\u003e \u003cp\u003e7.5.1 Importance of Classification Reports 177\u003c\/p\u003e \u003cp\u003e7.5.2 Importance of Confusion Matrices 177\u003c\/p\u003e \u003cp\u003e7.5.3 Decision Tree 178\u003c\/p\u003e \u003cp\u003e7.5.4 Random Forest 180\u003c\/p\u003e \u003cp\u003e7.5.5 K-Nearest Neighbors 182\u003c\/p\u003e \u003cp\u003e7.5.6 Logistic Regression 185\u003c\/p\u003e \u003cp\u003e7.5.7 Support Vector Machine 185\u003c\/p\u003e \u003cp\u003e7.5.8 Comparison of the Algorithms 188\u003c\/p\u003e \u003cp\u003e7.5.8.1 Accuracies 188\u003c\/p\u003e \u003cp\u003e7.5.8.2 Precision and Recall 188\u003c\/p\u003e \u003cp\u003e7.6 Conclusions 191\u003c\/p\u003e \u003cp\u003e7.7 Scope of Future Work 191\u003c\/p\u003e \u003cp\u003eReferences 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment 195\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eK. Sujatha, R. Krishnakumar, N.P.G. Bhavani, U. Jayalatsumi, V. Srividhya, C. Kamatchi and R. Vani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 196\u003c\/p\u003e \u003cp\u003e8.1.1 Color Image Processing 197\u003c\/p\u003e \u003cp\u003e8.1.2 Motivation 199\u003c\/p\u003e \u003cp\u003e8.1.3 Objectives 199\u003c\/p\u003e \u003cp\u003e8.2 Literature Review 200\u003c\/p\u003e \u003cp\u003e8.2.1 Gas Turbine Power Plants 200\u003c\/p\u003e \u003cp\u003e8.2.2 Artificial Intelligent Methods 201\u003c\/p\u003e \u003cp\u003e8.3 Materials and Methods 202\u003c\/p\u003e \u003cp\u003e8.3.1 Feature Extraction 202\u003c\/p\u003e \u003cp\u003e8.3.2 Classification 203\u003c\/p\u003e \u003cp\u003e8.4 Results and Discussion 204\u003c\/p\u003e \u003cp\u003e8.4.1 Fisher’s Linear Discriminant Function (FLDA) and Curvelet 204\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 219\u003c\/p\u003e \u003cp\u003e8.5.1 Future Scope of Work 220\u003c\/p\u003e \u003cp\u003eReferences 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate 223\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSidharth Patro, Trupti Ranjan Mahapatra, Romeo S. Fono Tamo, Allu Vikram Kishore Murty, Soumya Ranjan Parimanik and Debadutta Mishra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 224\u003c\/p\u003e \u003cp\u003e9.1 Introduction 224\u003c\/p\u003e \u003cp\u003e9.2 Numerical Experimentation Program 227\u003c\/p\u003e \u003cp\u003e9.3 Discussion of the Results 239\u003c\/p\u003e \u003cp\u003e9.4 Conclusion 244\u003c\/p\u003e \u003cp\u003eAcknowledgements 245\u003c\/p\u003e \u003cp\u003eReferences 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Integration of Digital Technologies to Operations 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Edge Computing-Based Conditional Monitoring 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGranville Embia, Aezeden Mohamed, Bikash Ranjan Moharana and Kamalakanta Muduli\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 252\u003c\/p\u003e \u003cp\u003e10.1.1 Problem Statement 252\u003c\/p\u003e \u003cp\u003e10.2 Literature Review 253\u003c\/p\u003e \u003cp\u003e10.3 Edge Computing 257\u003c\/p\u003e \u003cp\u003e10.4 Methodology 259\u003c\/p\u003e \u003cp\u003e10.5 Discussion 263\u003c\/p\u003e \u003cp\u003e10.5.1 Predictive Maintenance 263\u003c\/p\u003e \u003cp\u003e10.5.2 Energy Efficiency Management 264\u003c\/p\u003e \u003cp\u003e10.5.3 Smart Manufacturing 265\u003c\/p\u003e \u003cp\u003e10.5.4 Conditional Monitoring via Edge Computing Locally 266\u003c\/p\u003e \u003cp\u003e10.5.5 Lesson Learned 266\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 267\u003c\/p\u003e \u003cp\u003eReferences 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Optimization Methodologies in Intelligent Manufacturing Systems: Application and Challenges 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Atiur Alam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 272\u003c\/p\u003e \u003cp\u003e11.2 Literature Review 273\u003c\/p\u003e \u003cp\u003e11.3 Intelligent Manufacturing System Framework 275\u003c\/p\u003e \u003cp\u003e11.3.1 Principles of Developing Industry 4.0 Solutions 277\u003c\/p\u003e \u003cp\u003e11.3.2 Quantitative Analysis 279\u003c\/p\u003e \u003cp\u003e11.3.2.1 Optimization Characteristics and Requirements of Industry 4.0 279\u003c\/p\u003e \u003cp\u003e11.3.3 Optimization Methodologies and Algorithms 281\u003c\/p\u003e \u003cp\u003e11.4 Bayesian Networks (BNs) 287\u003c\/p\u003e \u003cp\u003e11.4.1 Instance-Based Learning (IBL) 288\u003c\/p\u003e \u003cp\u003e11.4.2 The IB1 Algorithm 288\u003c\/p\u003e \u003cp\u003e11.4.3 Artificial Neural Networks 289\u003c\/p\u003e \u003cp\u003e11.4.4 A Comparison Between Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) 291\u003c\/p\u003e \u003cp\u003e11.5 Problems of Implementing Machine Learning in Manufacturing 293\u003c\/p\u003e \u003cp\u003e11.6 Conclusions 293\u003c\/p\u003e \u003cp\u003eReferences 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Challenges of Warehouse Management Towards Smart Manufacturing: A Case of an Indian Consumer Electrical Company 297\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNatarajan Ramanathan, Neeraj Vairagi, Sakti Parida, Sushanta Tripathy, Ashok Kumar Sar, Kumar Mohanty and Alisha Lakra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 298\u003c\/p\u003e \u003cp\u003e12.2 Literature Review 300\u003c\/p\u003e \u003cp\u003e12.2.1 Shortage of Space 301\u003c\/p\u003e \u003cp\u003e12.2.2 Non-Moving Materials 301\u003c\/p\u003e \u003cp\u003e12.2.3 Lack of Action on Liquidation 302\u003c\/p\u003e \u003cp\u003e12.2.4 Defective Material from Both Ends 302\u003c\/p\u003e \u003cp\u003e12.2.5 Gap Between the Demand and the Supply 302\u003c\/p\u003e \u003cp\u003e12.2.6 Multiple Price Revision 303\u003c\/p\u003e \u003cp\u003e12.2.7 More Manual Timing for Loading and Unloading 303\u003c\/p\u003e \u003cp\u003e12.2.8 Operational Challenges for Seasonal Products 303\u003c\/p\u003e \u003cp\u003e12.2.9 Lack of Automation 303\u003c\/p\u003e \u003cp\u003e12.2.10 Manpower Balancing Between Peak and Off 304\u003c\/p\u003e \u003cp\u003e12.3 The Proposed ISM Methodology 304\u003c\/p\u003e \u003cp\u003e12.3.1 Establishment of the Structural Self-Interaction Matrix (SSIM) 306\u003c\/p\u003e \u003cp\u003e12.3.2 Creation of the Reachability Matrix 307\u003c\/p\u003e \u003cp\u003e12.3.3 Implementation of the Level Partitions 308\u003c\/p\u003e \u003cp\u003e12.3.4 Classification of the Selected Challenges 309\u003c\/p\u003e \u003cp\u003e12.3.5 Development of the Final ISM Model 310\u003c\/p\u003e \u003cp\u003e12.4 Results and Discussion 311\u003c\/p\u003e \u003cp\u003e12.5 Practical Implications 312\u003c\/p\u003e \u003cp\u003e12.6 Conclusions 313\u003c\/p\u003e \u003cp\u003eReferences 314\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 The Impact of Organizational Ergonomics on Teaching Rapid Prototyping 319\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYaone Rapitsenyane, Patience Erick, Oanthata Jester Sealetsa and Richie Moalosi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 320\u003c\/p\u003e \u003cp\u003e13.1 Introduction 320\u003c\/p\u003e \u003cp\u003e13.2 Organizational Ergonomics 322\u003c\/p\u003e \u003cp\u003e13.2.1 Aim of Organizational Ergonomics 323\u003c\/p\u003e \u003cp\u003e13.3 Rapid Prototyping and Teaching Rapid Prototyping 323\u003c\/p\u003e \u003cp\u003e13.4 Industry 4.0 Factors Associated with Organizational Ergonomics in a Rapid Prototyping\/Manufacturing Facility 325\u003c\/p\u003e \u003cp\u003e13.4.1 Technology 326\u003c\/p\u003e \u003cp\u003e13.4.2 Communication 327\u003c\/p\u003e \u003cp\u003e13.4.3 Teamwork 328\u003c\/p\u003e \u003cp\u003e13.4.4 Human Resource 328\u003c\/p\u003e \u003cp\u003e13.4.5 Quality Management 329\u003c\/p\u003e \u003cp\u003e13.5 Implications of Industry 4.0 on Prototyping and Prototyping Facilities in Design Schools 329\u003c\/p\u003e \u003cp\u003e13.6 The Influence of Cooperative Working Ergonomics of Distributed Manufacturing in Teaching and Learning Rapid Prototyping 332\u003c\/p\u003e \u003cp\u003e13.7 Health and Safety in Rapid Prototyping Laboratories 333\u003c\/p\u003e \u003cp\u003e13.7.1 Common Health Hazards in 3D Printing 333\u003c\/p\u003e \u003cp\u003e13.7.2 Chemical Hazards 335\u003c\/p\u003e \u003cp\u003e13.7.3 Flammable\/Explosion Hazards 336\u003c\/p\u003e \u003cp\u003e13.7.4 UV and Laser Radiation Hazard 336\u003c\/p\u003e \u003cp\u003e13.7.5 Other Hazards 336\u003c\/p\u003e \u003cp\u003e13.7.6 Hazard Controls 337\u003c\/p\u003e \u003cp\u003e13.7.7 Engineering Controls 337\u003c\/p\u003e \u003cp\u003e13.7.8 Administrative Controls 338\u003c\/p\u003e \u003cp\u003e13.7.9 Personal Protective Equipment 338\u003c\/p\u003e \u003cp\u003e13.8 Impact of Digital\/Rapid Prototyping on Organizational Ergonomics 339\u003c\/p\u003e \u003cp\u003e13.9 Implications of the Study for Academicians and Practitioners 340\u003c\/p\u003e \u003cp\u003e13.10 Conclusions and Future Work 341\u003c\/p\u003e \u003cp\u003eReferences 343\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Sustainable Manufacturing Practices through Additive Manufacturing: A Case Study on a Can-Making Manufacturer 349\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKiren Piso, Aezeden Mohamed, Bikash Ranjan Moharana, Kamalakanta Muduli and Noorhafiza Muhammad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 350\u003c\/p\u003e \u003cp\u003e14.2 Literature Review 352\u003c\/p\u003e \u003cp\u003e14.3 Research Set Up 354\u003c\/p\u003e \u003cp\u003e14.4 Additive Manufacturing Techniques 356\u003c\/p\u003e \u003cp\u003e14.4.1 Types of Additive Manufacturing 356\u003c\/p\u003e \u003cp\u003e14.4.1.1 Fused Deposition Modelling (FDM) 356\u003c\/p\u003e \u003cp\u003e14.4.1.2 Stereolithography (SLA) 356\u003c\/p\u003e \u003cp\u003e14.4.1.3 Selective Laser Sintering (SLS) 357\u003c\/p\u003e \u003cp\u003e14.4.1.4 Direct Energy Deposition (DED) 357\u003c\/p\u003e \u003cp\u003e14.4.1.5 Digital Light Processing (DLP) 358\u003c\/p\u003e \u003cp\u003e14.5 Strategies Used by Production Company 358\u003c\/p\u003e \u003cp\u003e14.5.1 Maintenance Strategies 358\u003c\/p\u003e \u003cp\u003e14.5.1.1 Breakdown Maintenance (BM) 358\u003c\/p\u003e \u003cp\u003e14.5.1.2 Preventive Maintenance (PM) 358\u003c\/p\u003e \u003cp\u003e14.5.1.3 Periodic Maintenance (Time Based Maintenance – TBM) 359\u003c\/p\u003e \u003cp\u003e14.5.1.4 Predictive Maintenance (PM) 359\u003c\/p\u003e \u003cp\u003e14.5.1.5 Corrective Maintenance (CM) 359\u003c\/p\u003e \u003cp\u003e14.5.1.6 Maintenance Prevention (PM) 359\u003c\/p\u003e \u003cp\u003e14.5.2 Inventory Control in Manufacturing 359\u003c\/p\u003e \u003cp\u003e14.5.2.1 Inventory Control and Maintenance in Manufacturing 360\u003c\/p\u003e \u003cp\u003e14.5.2.2 Warehouse Storages 360\u003c\/p\u003e \u003cp\u003e14.5.3 Time Factor in Manufacturing 361\u003c\/p\u003e \u003cp\u003e14.5.3.1 Breakdown Time 361\u003c\/p\u003e \u003cp\u003e14.5.3.2 Set-Up Time 361\u003c\/p\u003e \u003cp\u003e14.5.3.3 Manned Time (Available Time) 361\u003c\/p\u003e \u003cp\u003e14.5.3.4 Operating Working Time 361\u003c\/p\u003e \u003cp\u003e14.5.3.5 Operating Time 362\u003c\/p\u003e \u003cp\u003e14.5.3.6 Production Time 362\u003c\/p\u003e \u003cp\u003e14.6 Sustainable Manufacturing 362\u003c\/p\u003e \u003cp\u003e14.6.1 Social Aspect of Sustainable Manufacturing 363\u003c\/p\u003e \u003cp\u003e14.6.2 Environmental Aspects of Sustainable Manufacturing 364\u003c\/p\u003e \u003cp\u003e14.6.3 Economical Aspect of Sustainable Manufacturing 364\u003c\/p\u003e \u003cp\u003e14.7 Sustainable Additive Manufacturing 365\u003c\/p\u003e \u003cp\u003e14.7.1 Energy 365\u003c\/p\u003e \u003cp\u003e14.7.2 Cost 366\u003c\/p\u003e \u003cp\u003e14.7.2.1 Downtime Cost 366\u003c\/p\u003e \u003cp\u003e14.7.3 Supply Chain 368\u003c\/p\u003e \u003cp\u003e14.7.4 Maintenance with Additive Manufacturing 368\u003c\/p\u003e \u003cp\u003e14.8 Additive Manufacturing with IFC CMD: A Case Study 369\u003c\/p\u003e \u003cp\u003e14.9 Contribution of Additive Manufacturing Towards Sustainability 370\u003c\/p\u003e \u003cp\u003e14.10 Limitations of Additive Manufacturing 372\u003c\/p\u003e \u003cp\u003e14.11 Conclusions and Recommendations 373\u003c\/p\u003e \u003cp\u003eReferences 373\u003c\/p\u003e \u003cp\u003eIndex 377\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":51039270306135,"sku":"9781119836247","price":168.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119836247.jpg?v=1750943117","url":"https:\/\/bookcurl.com\/products\/intelligent-manufacturing-management-systems-9781119836247","provider":"Book Curl","version":"1.0","type":"link"}