Overview

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.

Full Product Details

Author:   Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea) ,  V. P. Kommula (University of Botswana) ,  Devendra K. Yadav (National Institute of Technology Calicut, Kerala, India) ,  M. Chithirai Pon Selvan (Curtin University, Dubai)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-Scrivener
Weight:   0.798kg
ISBN:  

9781119836247

ISBN 10:   1119836247
Pages:   400
Publication Date:   09 February 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

Preface xvii Part I: Smart Technologies in Manufacturing 1 1 Smart Manufacturing Systems for Industry 4.0 3 Gaijinliu Gangmei and Polash Pratim Dutta Abbreviations 3 1.1 Introduction 4 1.2 Research Methodology 5 1.3 Pillars of Smart Manufacturing 6 1.3.1 Manufacturing Technology and Processes 6 1.3.2 Materials 7 1.3.3 Data 8 1.3.4 Sustainability 8 1.3.5 Resource Sharing and Networking 9 1.3.6 Predictive Engineering 9 1.3.7 Stakeholders 10 1.3.8 Standardization 10 1.4 Enablers and Their Applications 11 1.4.1 Smart Design 12 1.4.2 Smart Machining 12 1.4.3 Smart Monitoring 13 1.4.4 Smart Control 13 1.4.5 Smart Scheduling 14 1.5 Assessment of Smart Manufacturing Systems 14 1.6 Challenges in Implementation of Smart Manufacturing Systems 15 1.6.1 Technological Issue 16 1.6.2 Methodological Issue 16 1.7 Implications of the Study for Academicians and Practitioners 17 1.8 Conclusion 17 References 18 2 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities 23 S. Deepak Kumar, G. Arun Manohar, R. Surya Teja, P. S. V. Ramana Rao, A. Mandal, Ajit Behera and P. Srinivasa Rao Abbreviations 24 2.1 Introduction to Smart Manufacturing 24 2.1.1 Background of SM 24 2.1.2 Traditional Manufacturing versus Smart Manufacturing 25 2.1.3 Concept and Evolution of Industry 4.0 25 2.1.4 Motivations for Research in Smart Manufacturing 28 2.1.5 Objectives and Need of Industry 4.0 29 2.1.6 Research Methodology 30 2.1.7 Principles of I4. 0 30 2.1.8 Benefits/Advantages of Industry 4.0 31 2.2 Technology Pillars of Industry 4.0 31 2.2.1 Automation in Industry 4.0 33 2.2.1.1 Need of Automation 33 2.2.1.2 Components of Automation 33 2.2.1.3 Applications of Automation 34 2.2.2 Robots in Industry 4.0 34 2.2.2.1 Need of Robots 35 2.2.2.2 Advantages of Robots 35 2.2.2.3 Applications of Robots 37 2.2.2.4 Advances Robotics 37 2.2.3 Additive Manufacturing (AM) 38 2.2.3.1 Additive Manufacturing’s Potential Applications 39 2.2.4 Big Data Analytics 40 2.2.5 Cloud Computing 41 2.2.6 Cyber Security 43 2.2.6.1 Cyber-Security Challenges in Industry 4.0 43 2.2.7 Augmented Reality and Virtual Reality 44 2.2.8 Simulation 46 2.2.8.1 Need of Simulation in Smart Manufacturing 46 2.2.8.2 Advantages of Simulation 47 2.2.8.3 Simulation and Digital Twin 47 2.2.9 Digital Twins 47 2.2.9.1 Integration of Horizontal and Vertical Systems 48 2.2.10 IoT and IIoT in Industry 4.0 48 2.2.11 Artificial Intelligence in Industry 4.0 49 2.2.12 Implications of the Study for Academicians and Practitioners 51 2.3 Summary and Conclusions 51 2.3.1 Benefits of Industry 4.0 51 2.3.2 Challenges in Industry 4.0 52 2.3.3 Future Directions 52 Acknowledgement 53 References 53 3 IoT-Based Intelligent Manufacturing System: A Review 59 Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Pritam Chakraborty 3.1 Introduction 60 3.2 Literature Review 60 3.3 Research Procedure 64 3.3.1 The Beginning and Advancement of SM/IM 64 3.3.2 Beginning of SM/IM 64 3.3.3 Defining SM/IM 65 3.3.4 Potential of SM/IM 66 3.3.5 Statistical Analysis of SM/IM 68 3.3.6 Future Endeavour of SM/IM 68 3.3.7 Necessary Components of IoT Framework 69 3.3.8 Proposed System Based on IoT 71 3.3.9 Development of IoT in Industry 4.0 72 3.4 Smart Manufacturing 73 3.4.1 Re-Configurability Manufacturing System 73 3.4.2 RMS Framework Based Upon IoT 75 3.4.3 Machine Control 76 3.4.4 Machine Intelligence 77 3.4.5 Innovation and the IIoT 78 3.4.6 Wireless Technology 78 3.4.7 IP Mobility 78 3.4.8 Network Functionality Virtualization (NFV) 79 3.5 Academia Industry Collaboration 79 3.6 Conclusions 80 References 81 4 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process 85 Kali Charan Rath, Prasenjit Chatterjee, Pankajkumar Munibara Patro, Polaiah Bojja, Amaresh Kumar and Rashmi Prava Das Abbreviations 86 4.1 Introduction and Literature Reviews 86 4.1.1 Motivation Behind the Study 88 4.1.2 Objective of the Chapter 89 4.2 Network in Smart Manufacturing System 89 4.2.1 Challenges for Smart Manufacturing Industries 90 4.2.2 Smart Manufacturing Current Market Scenario 93 4.3 Data Drives in Smart Manufacturing 93 4.3.1 Benefits of Data-Driven Manufacturing 94 4.4 Manufacturing of Product Through 3D Printing Process 97 4.4.1 3D Printing Technology 99 4.4.2 3D Printing Technologies Classification 100 4.4.3 3D Printer Parameters 101 4.4.4 Significance of Honeycomb Structure 102 4.4.5 Acrylonitrile Butadiene Styrene (ABS) Thermoplastic Polymer Used for Honeycomb Structures Model 103 4.4.6 3D Printing Parameters and Their Descriptions 107 4.5 Conclusion 107 References 109 5 Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management 113 Hiranmoy Samanta and Kamal Golui 5.1 Introduction 114 5.2 Objectives 114 5.3 Research Methodology 114 5.4 Literature Review 115 5.5 Components of SIM 116 5.5.1 Supply Chain Management (SCM) 116 5.5.2 Inventory Management System (IMS) 117 5.5.3 Internet of Things (IoT) 120 5.5.4 RFID System 121 5.5.5 Maintenance, Repair, and Operations 123 5.5.6 Deep Reinforcement Learning 125 5.6 Framework 127 5.7 Optimization 130 5.7.1 Inventory Optimization 130 5.8 Results and Discussion 131 5.9 A Mirror to Researchers and Managers 132 5.10 Conclusions 133 5.11 Future Scope 133 References 134 6 Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0 141 Bikash Chandra Behera, Bikash Ranjan Moharana, Matruprasad Rout and Kishore Debnath 6.1 Introduction 142 6.2 Machine Learning 143 6.3 Smart Factory 146 6.4 Intelligent Machining 148 6.5 Machine Learning Processes Used in Machining Process 150 6.6 Performance Improvement of Machine Structure Using Machine Learning 152 6.7 Conclusions 153 References 153 7 Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies 157 Jagadeesha T., Thutupalli Srinivasa Advaith, Choppala Sarath Wesley, Grandhi Sri Sai Charith and Doppalapudi Manohar Abbreviations 158 7.1 Introduction 158 7.2 Literature Review 159 7.3 Methodology 161 7.3.1 Dataset Preparation 161 7.3.2 CWRU Dataset 161 7.3.3 Methodology Flow Chart 161 7.3.4 Data Pre-Processing 162 7.3.5 Models Deployed 163 7.3.6 Training and Testing 163 7.4 Analysis 164 7.4.1 Datasets 164 7.4.2 Feature Extraction 168 7.4.3 Splitting of Data into Samples 168 7.4.4 Algorithms Used 169 7.4.4.1 Multinomial Logistic Regression 169 7.4.4.2 K-Nearest Neighbors 170 7.4.4.3 Decision Tree 172 7.4.4.4 Support Vector Machine (SVM) 173 7.4.4.5 Random Forest 175 7.5 Results and Discussion 177 7.5.1 Importance of Classification Reports 177 7.5.2 Importance of Confusion Matrices 177 7.5.3 Decision Tree 178 7.5.4 Random Forest 180 7.5.5 K-Nearest Neighbors 182 7.5.6 Logistic Regression 185 7.5.7 Support Vector Machine 185 7.5.8 Comparison of the Algorithms 188 7.5.8.1 Accuracies 188 7.5.8.2 Precision and Recall 188 7.6 Conclusions 191 7.7 Scope of Future Work 191 References 192 8 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment 195 K. Sujatha, R. Krishnakumar, N.P.G. Bhavani, U. Jayalatsumi, V. Srividhya, C. Kamatchi and R. Vani 8.1 Introduction 196 8.1.1 Color Image Processing 197 8.1.2 Motivation 199 8.1.3 Objectives 199 8.2 Literature Review 200 8.2.1 Gas Turbine Power Plants 200 8.2.2 Artificial Intelligent Methods 201 8.3 Materials and Methods 202 8.3.1 Feature Extraction 202 8.3.2 Classification 203 8.4 Results and Discussion 204 8.4.1 Fisher’s Linear Discriminant Function (FLDA) and Curvelet 204 8.5 Conclusion 219 8.5.1 Future Scope of Work 220 References 221 9 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate 223 Sidharth Patro, Trupti Ranjan Mahapatra, Romeo S. Fono Tamo, Allu Vikram Kishore Murty, Soumya Ranjan Parimanik and Debadutta Mishra Abbreviations 224 9.1 Introduction 224 9.2 Numerical Experimentation Program 227 9.3 Discussion of the Results 239 9.4 Conclusion 244 Acknowledgements 245 References 245 Part II: Integration of Digital Technologies to Operations 249 10 Edge Computing-Based Conditional Monitoring 251 Granville Embia, Aezeden Mohamed, Bikash Ranjan Moharana and Kamalakanta Muduli 10.1 Introduction 252 10.1.1 Problem Statement 252 10.2 Literature Review 253 10.3 Edge Computing 257 10.4 Methodology 259 10.5 Discussion 263 10.5.1 Predictive Maintenance 263 10.5.2 Energy Efficiency Management 264 10.5.3 Smart Manufacturing 265 10.5.4 Conditional Monitoring via Edge Computing Locally 266 10.5.5 Lesson Learned 266 10.6 Conclusion 267 References 267 11 Optimization Methodologies in Intelligent Manufacturing Systems: Application and Challenges 271 Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Atiur Alam 11.1 Introduction 272 11.2 Literature Review 273 11.3 Intelligent Manufacturing System Framework 275 11.3.1 Principles of Developing Industry 4.0 Solutions 277 11.3.2 Quantitative Analysis 279 11.3.2.1 Optimization Characteristics and Requirements of Industry 4.0 279 11.3.3 Optimization Methodologies and Algorithms 281 11.4 Bayesian Networks (BNs) 287 11.4.1 Instance-Based Learning (IBL) 288 11.4.2 The IB1 Algorithm 288 11.4.3 Artificial Neural Networks 289 11.4.4 A Comparison Between Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) 291 11.5 Problems of Implementing Machine Learning in Manufacturing 293 11.6 Conclusions 293 References 294 12 Challenges of Warehouse Management Towards Smart Manufacturing: A Case of an Indian Consumer Electrical Company 297 Natarajan Ramanathan, Neeraj Vairagi, Sakti Parida, Sushanta Tripathy, Ashok Kumar Sar, Kumar Mohanty and Alisha Lakra 12.1 Introduction 298 12.2 Literature Review 300 12.2.1 Shortage of Space 301 12.2.2 Non-Moving Materials 301 12.2.3 Lack of Action on Liquidation 302 12.2.4 Defective Material from Both Ends 302 12.2.5 Gap Between the Demand and the Supply 302 12.2.6 Multiple Price Revision 303 12.2.7 More Manual Timing for Loading and Unloading 303 12.2.8 Operational Challenges for Seasonal Products 303 12.2.9 Lack of Automation 303 12.2.10 Manpower Balancing Between Peak and Off 304 12.3 The Proposed ISM Methodology 304 12.3.1 Establishment of the Structural Self-Interaction Matrix (SSIM) 306 12.3.2 Creation of the Reachability Matrix 307 12.3.3 Implementation of the Level Partitions 308 12.3.4 Classification of the Selected Challenges 309 12.3.5 Development of the Final ISM Model 310 12.4 Results and Discussion 311 12.5 Practical Implications 312 12.6 Conclusions 313 References 314 13 The Impact of Organizational Ergonomics on Teaching Rapid Prototyping 319 Yaone Rapitsenyane, Patience Erick, Oanthata Jester Sealetsa and Richie Moalosi Abbreviations 320 13.1 Introduction 320 13.2 Organizational Ergonomics 322 13.2.1 Aim of Organizational Ergonomics 323 13.3 Rapid Prototyping and Teaching Rapid Prototyping 323 13.4 Industry 4.0 Factors Associated with Organizational Ergonomics in a Rapid Prototyping/Manufacturing Facility 325 13.4.1 Technology 326 13.4.2 Communication 327 13.4.3 Teamwork 328 13.4.4 Human Resource 328 13.4.5 Quality Management 329 13.5 Implications of Industry 4.0 on Prototyping and Prototyping Facilities in Design Schools 329 13.6 The Influence of Cooperative Working Ergonomics of Distributed Manufacturing in Teaching and Learning Rapid Prototyping 332 13.7 Health and Safety in Rapid Prototyping Laboratories 333 13.7.1 Common Health Hazards in 3D Printing 333 13.7.2 Chemical Hazards 335 13.7.3 Flammable/Explosion Hazards 336 13.7.4 UV and Laser Radiation Hazard 336 13.7.5 Other Hazards 336 13.7.6 Hazard Controls 337 13.7.7 Engineering Controls 337 13.7.8 Administrative Controls 338 13.7.9 Personal Protective Equipment 338 13.8 Impact of Digital/Rapid Prototyping on Organizational Ergonomics 339 13.9 Implications of the Study for Academicians and Practitioners 340 13.10 Conclusions and Future Work 341 References 343 14 Sustainable Manufacturing Practices through Additive Manufacturing: A Case Study on a Can-Making Manufacturer 349 Kiren Piso, Aezeden Mohamed, Bikash Ranjan Moharana, Kamalakanta Muduli and Noorhafiza Muhammad 14.1 Introduction 350 14.2 Literature Review 352 14.3 Research Set Up 354 14.4 Additive Manufacturing Techniques 356 14.4.1 Types of Additive Manufacturing 356 14.4.1.1 Fused Deposition Modelling (FDM) 356 14.4.1.2 Stereolithography (SLA) 356 14.4.1.3 Selective Laser Sintering (SLS) 357 14.4.1.4 Direct Energy Deposition (DED) 357 14.4.1.5 Digital Light Processing (DLP) 358 14.5 Strategies Used by Production Company 358 14.5.1 Maintenance Strategies 358 14.5.1.1 Breakdown Maintenance (BM) 358 14.5.1.2 Preventive Maintenance (PM) 358 14.5.1.3 Periodic Maintenance (Time Based Maintenance – TBM) 359 14.5.1.4 Predictive Maintenance (PM) 359 14.5.1.5 Corrective Maintenance (CM) 359 14.5.1.6 Maintenance Prevention (PM) 359 14.5.2 Inventory Control in Manufacturing 359 14.5.2.1 Inventory Control and Maintenance in Manufacturing 360 14.5.2.2 Warehouse Storages 360 14.5.3 Time Factor in Manufacturing 361 14.5.3.1 Breakdown Time 361 14.5.3.2 Set-Up Time 361 14.5.3.3 Manned Time (Available Time) 361 14.5.3.4 Operating Working Time 361 14.5.3.5 Operating Time 362 14.5.3.6 Production Time 362 14.6 Sustainable Manufacturing 362 14.6.1 Social Aspect of Sustainable Manufacturing 363 14.6.2 Environmental Aspects of Sustainable Manufacturing 364 14.6.3 Economical Aspect of Sustainable Manufacturing 364 14.7 Sustainable Additive Manufacturing 365 14.7.1 Energy 365 14.7.2 Cost 366 14.7.2.1 Downtime Cost 366 14.7.3 Supply Chain 368 14.7.4 Maintenance with Additive Manufacturing 368 14.8 Additive Manufacturing with IFC CMD: A Case Study 369 14.9 Contribution of Additive Manufacturing Towards Sustainability 370 14.10 Limitations of Additive Manufacturing 372 14.11 Conclusions and Recommendations 373 References 373 Index 377

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Author Information

Kamalakanta Muduli, PhD, is an associate professor in the Department of Mechanical Engineering, Papua New Guinea University of Technology, Papua New Guinea. He has over 15 years of academic and research experience and has published 40 papers in peer-reviewed international journals. V. P. Kommula, PhD, is an associate professor in the Department of Mechanical Engineering, University of Botswana. He has over 21 years of teaching experience and served in various positions with different universities in many countries. Kommula’s research is in the area of lean manufacturing and productivity improvement by adopting digital technologies. He has published 42 research articles in peer-reviewed international journals. Devendra K. Yadav, PhD, is an assistant professor in the Department of Mechanical Engineering, National Institute of Technology Calicut, Kerala, India. His current research interests include supply chain management, logistics performance measurement, and Industry 4.0 applications in supply chain domains. Chithirai Pon Selvan, PhD, is an associate professor at Curtin University, Dubai. He has over 21 years of experience in teaching and has published more than 100 research articles in journals. His research interests are in the areas of machine design, optimization techniques, and manufacturing practices. Jayakrishna Kandasamy, PhD, is an associate professor in the School of Mechanical Engineering, Vellore Institute of Technology University, India. He has published 47 journal articles in leading SCI journals, 22 book chapters, 85 contributions to refereed conference proceedings, and one edited book. Dr. Jayakrishna’s research is focused on the design and management of manufacturing systems and supply chains to enhance efficiency, productivity, and sustainability performance.

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