Intelligent Manufacturing Management Systems

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.

• Preface xviiPart I: Smart Technologies in Manufacturing 11 Smart Manufacturing Systems for Industry 4.0 3Gaijinliu Gangmei and Polash Pratim DuttaAbbreviations 31.1 Introduction 41.2 Research Methodology 51.3 Pillars of Smart Manufacturing 61.3.1 Manufacturing Technology and Processes 61.3.2 Materials 71.3.3 Data 81.3.4 Sustainability 81.3.5 Resource Sharing and Networking 91.3.6 Predictive Engineering 91.3.7 Stakeholders 101.3.8 Standardization 101.4 Enablers and Their Applications 111.4.1 Smart Design 121.4.2 Smart Machining 121.4.3 Smart Monitoring 131.4.4 Smart Control 131.4.5 Smart Scheduling 141.5 Assessment of Smart Manufacturing Systems 141.6 Challenges in Implementation of Smart Manufacturing Systems 151.6.1 Technological Issue 161.6.2 Methodological Issue 161.7 Implications of the Study for Academicians and Practitioners 171.8 Conclusion 17References 182 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities 23S. Deepak Kumar, G. Arun Manohar, R. Surya Teja, P. S. V. Ramana Rao, A. Mandal, Ajit Behera and P. Srinivasa RaoAbbreviations 242.1 Introduction to Smart Manufacturing 242.1.1 Background of SM 242.1.2 Traditional Manufacturing versus Smart Manufacturing 252.1.3 Concept and Evolution of Industry 4.0 252.1.4 Motivations for Research in Smart Manufacturing 282.1.5 Objectives and Need of Industry 4.0 292.1.6 Research Methodology 302.1.7 Principles of I4. 0 302.1.8 Benefits/Advantages of Industry 4.0 312.2 Technology Pillars of Industry 4.0 312.2.1 Automation in Industry 4.0 332.2.1.1 Need of Automation 332.2.1.2 Components of Automation 332.2.1.3 Applications of Automation 342.2.2 Robots in Industry 4.0 342.2.2.1 Need of Robots 352.2.2.2 Advantages of Robots 352.2.2.3 Applications of Robots 372.2.2.4 Advances Robotics 372.2.3 Additive Manufacturing (AM) 382.2.3.1 Additive Manufacturing’s Potential Applications 392.2.4 Big Data Analytics 402.2.5 Cloud Computing 412.2.6 Cyber Security 432.2.6.1 Cyber-Security Challenges in Industry 4.0 432.2.7 Augmented Reality and Virtual Reality 442.2.8 Simulation 462.2.8.1 Need of Simulation in Smart Manufacturing 462.2.8.2 Advantages of Simulation 472.2.8.3 Simulation and Digital Twin 472.2.9 Digital Twins 472.2.9.1 Integration of Horizontal and Vertical Systems 482.2.10 IoT and IIoT in Industry 4.0 482.2.11 Artificial Intelligence in Industry 4.0 492.2.12 Implications of the Study for Academicians and Practitioners 512.3 Summary and Conclusions 512.3.1 Benefits of Industry 4.0 512.3.2 Challenges in Industry 4.0 522.3.3 Future Directions 52Acknowledgement 53References 533 IoT-Based Intelligent Manufacturing System: A Review 59Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Pritam Chakraborty3.1 Introduction 603.2 Literature Review 603.3 Research Procedure 643.3.1 The Beginning and Advancement of SM/IM 643.3.2 Beginning of SM/IM 643.3.3 Defining SM/IM 653.3.4 Potential of SM/IM 663.3.5 Statistical Analysis of SM/IM 683.3.6 Future Endeavour of SM/IM 683.3.7 Necessary Components of IoT Framework 693.3.8 Proposed System Based on IoT 713.3.9 Development of IoT in Industry 4.0 723.4 Smart Manufacturing 733.4.1 Re-Configurability Manufacturing System 733.4.2 RMS Framework Based Upon IoT 753.4.3 Machine Control 763.4.4 Machine Intelligence 773.4.5 Innovation and the IIoT 783.4.6 Wireless Technology 783.4.7 IP Mobility 783.4.8 Network Functionality Virtualization (NFV) 793.5 Academia Industry Collaboration 793.6 Conclusions 80References 814 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process 85Kali Charan Rath, Prasenjit Chatterjee, Pankajkumar Munibara Patro, Polaiah Bojja, Amaresh Kumar and Rashmi Prava DasAbbreviations 864.1 Introduction and Literature Reviews 864.1.1 Motivation Behind the Study 884.1.2 Objective of the Chapter 894.2 Network in Smart Manufacturing System 894.2.1 Challenges for Smart Manufacturing Industries 904.2.2 Smart Manufacturing Current Market Scenario 934.3 Data Drives in Smart Manufacturing 934.3.1 Benefits of Data-Driven Manufacturing 944.4 Manufacturing of Product Through 3D Printing Process 974.4.1 3D Printing Technology 994.4.2 3D Printing Technologies Classification 1004.4.3 3D Printer Parameters 1014.4.4 Significance of Honeycomb Structure 1024.4.5 Acrylonitrile Butadiene Styrene (ABS) Thermoplastic Polymer Used for Honeycomb Structures Model 1034.4.6 3D Printing Parameters and Their Descriptions 1074.5 Conclusion 107References 1095 Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management 113Hiranmoy Samanta and Kamal Golui5.1 Introduction 1145.2 Objectives 1145.3 Research Methodology 1145.4 Literature Review 1155.5 Components of SIM 1165.5.1 Supply Chain Management (SCM) 1165.5.2 Inventory Management System (IMS) 1175.5.3 Internet of Things (IoT) 1205.5.4 RFID System 1215.5.5 Maintenance, Repair, and Operations 1235.5.6 Deep Reinforcement Learning 1255.6 Framework 1275.7 Optimization 1305.7.1 Inventory Optimization 1305.8 Results and Discussion 1315.9 A Mirror to Researchers and Managers 1325.10 Conclusions 1335.11 Future Scope 133References 1346 Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0 141Bikash Chandra Behera, Bikash Ranjan Moharana, Matruprasad Rout and Kishore Debnath6.1 Introduction 1426.2 Machine Learning 1436.3 Smart Factory 1466.4 Intelligent Machining 1486.5 Machine Learning Processes Used in Machining Process 1506.6 Performance Improvement of Machine Structure Using Machine Learning 1526.7 Conclusions 153References 1537 Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies 157Jagadeesha T., Thutupalli Srinivasa Advaith, Choppala Sarath Wesley, Grandhi Sri Sai Charith and Doppalapudi ManoharAbbreviations 1587.1 Introduction 1587.2 Literature Review 1597.3 Methodology 1617.3.1 Dataset Preparation 1617.3.2 CWRU Dataset 1617.3.3 Methodology Flow Chart 1617.3.4 Data Pre-Processing 1627.3.5 Models Deployed 1637.3.6 Training and Testing 1637.4 Analysis 1647.4.1 Datasets 1647.4.2 Feature Extraction 1687.4.3 Splitting of Data into Samples 1687.4.4 Algorithms Used 1697.4.4.1 Multinomial Logistic Regression 1697.4.4.2 K-Nearest Neighbors 1707.4.4.3 Decision Tree 1727.4.4.4 Support Vector Machine (SVM) 1737.4.4.5 Random Forest 1757.5 Results and Discussion 1777.5.1 Importance of Classification Reports 1777.5.2 Importance of Confusion Matrices 1777.5.3 Decision Tree 1787.5.4 Random Forest 1807.5.5 K-Nearest Neighbors 1827.5.6 Logistic Regression 1857.5.7 Support Vector Machine 1857.5.8 Comparison of the Algorithms 1887.5.8.1 Accuracies 1887.5.8.2 Precision and Recall 1887.6 Conclusions 1917.7 Scope of Future Work 191References 1928 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment 195K. Sujatha, R. Krishnakumar, N.P.G. Bhavani, U. Jayalatsumi, V. Srividhya, C. Kamatchi and R. Vani8.1 Introduction 1968.1.1 Color Image Processing 1978.1.2 Motivation 1998.1.3 Objectives 1998.2 Literature Review 2008.2.1 Gas Turbine Power Plants 2008.2.2 Artificial Intelligent Methods 2018.3 Materials and Methods 2028.3.1 Feature Extraction 2028.3.2 Classification 2038.4 Results and Discussion 2048.4.1 Fisher’s Linear Discriminant Function (FLDA) and Curvelet 2048.5 Conclusion 2198.5.1 Future Scope of Work 220References 2219 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate 223Sidharth Patro, Trupti Ranjan Mahapatra, Romeo S. Fono Tamo, Allu Vikram Kishore Murty, Soumya Ranjan Parimanik and Debadutta MishraAbbreviations 2249.1 Introduction 2249.2 Numerical Experimentation Program 2279.3 Discussion of the Results 2399.4 Conclusion 244Acknowledgements 245References 245Part II: Integration of Digital Technologies to Operations 24910 Edge Computing-Based Conditional Monitoring 251Granville Embia, Aezeden Mohamed, Bikash Ranjan Moharana and Kamalakanta Muduli10.1 Introduction 25210.1.1 Problem Statement 25210.2 Literature Review 25310.3 Edge Computing 25710.4 Methodology 25910.5 Discussion 26310.5.1 Predictive Maintenance 26310.5.2 Energy Efficiency Management 26410.5.3 Smart Manufacturing 26510.5.4 Conditional Monitoring via Edge Computing Locally 26610.5.5 Lesson Learned 26610.6 Conclusion 267References 26711 Optimization Methodologies in Intelligent Manufacturing Systems: Application and Challenges 271Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Atiur Alam11.1 Introduction 27211.2 Literature Review 27311.3 Intelligent Manufacturing System Framework 27511.3.1 Principles of Developing Industry 4.0 Solutions 27711.3.2 Quantitative Analysis 27911.3.2.1 Optimization Characteristics and Requirements of Industry 4.0 27911.3.3 Optimization Methodologies and Algorithms 28111.4 Bayesian Networks (BNs) 28711.4.1 Instance-Based Learning (IBL) 28811.4.2 The IB1 Algorithm 28811.4.3 Artificial Neural Networks 28911.4.4 A Comparison Between Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) 29111.5 Problems of Implementing Machine Learning in Manufacturing 29311.6 Conclusions 293References 29412 Challenges of Warehouse Management Towards Smart Manufacturing: A Case of an Indian Consumer Electrical Company 297Natarajan Ramanathan, Neeraj Vairagi, Sakti Parida, Sushanta Tripathy, Ashok Kumar Sar, Kumar Mohanty and Alisha Lakra12.1 Introduction 29812.2 Literature Review 30012.2.1 Shortage of Space 30112.2.2 Non-Moving Materials 30112.2.3 Lack of Action on Liquidation 30212.2.4 Defective Material from Both Ends 30212.2.5 Gap Between the Demand and the Supply 30212.2.6 Multiple Price Revision 30312.2.7 More Manual Timing for Loading and Unloading 30312.2.8 Operational Challenges for Seasonal Products 30312.2.9 Lack of Automation 30312.2.10 Manpower Balancing Between Peak and Off 30412.3 The Proposed ISM Methodology 30412.3.1 Establishment of the Structural Self-Interaction Matrix (SSIM) 30612.3.2 Creation of the Reachability Matrix 30712.3.3 Implementation of the Level Partitions 30812.3.4 Classification of the Selected Challenges 30912.3.5 Development of the Final ISM Model 31012.4 Results and Discussion 31112.5 Practical Implications 31212.6 Conclusions 313References 31413 The Impact of Organizational Ergonomics on Teaching Rapid Prototyping 319Yaone Rapitsenyane, Patience Erick, Oanthata Jester Sealetsa and Richie MoalosiAbbreviations 32013.1 Introduction 32013.2 Organizational Ergonomics 32213.2.1 Aim of Organizational Ergonomics 32313.3 Rapid Prototyping and Teaching Rapid Prototyping 32313.4 Industry 4.0 Factors Associated with Organizational Ergonomics in a Rapid Prototyping/Manufacturing Facility 32513.4.1 Technology 32613.4.2 Communication 32713.4.3 Teamwork 32813.4.4 Human Resource 32813.4.5 Quality Management 32913.5 Implications of Industry 4.0 on Prototyping and Prototyping Facilities in Design Schools 32913.6 The Influence of Cooperative Working Ergonomics of Distributed Manufacturing in Teaching and Learning Rapid Prototyping 33213.7 Health and Safety in Rapid Prototyping Laboratories 33313.7.1 Common Health Hazards in 3D Printing 33313.7.2 Chemical Hazards 33513.7.3 Flammable/Explosion Hazards 33613.7.4 UV and Laser Radiation Hazard 33613.7.5 Other Hazards 33613.7.6 Hazard Controls 33713.7.7 Engineering Controls 33713.7.8 Administrative Controls 33813.7.9 Personal Protective Equipment 33813.8 Impact of Digital/Rapid Prototyping on Organizational Ergonomics 33913.9 Implications of the Study for Academicians and Practitioners 34013.10 Conclusions and Future Work 341References 34314 Sustainable Manufacturing Practices through Additive Manufacturing: A Case Study on a Can-Making Manufacturer 349Kiren Piso, Aezeden Mohamed, Bikash Ranjan Moharana, Kamalakanta Muduli and Noorhafiza Muhammad14.1 Introduction 35014.2 Literature Review 35214.3 Research Set Up 35414.4 Additive Manufacturing Techniques 35614.4.1 Types of Additive Manufacturing 35614.4.1.1 Fused Deposition Modelling (FDM) 35614.4.1.2 Stereolithography (SLA) 35614.4.1.3 Selective Laser Sintering (SLS) 35714.4.1.4 Direct Energy Deposition (DED) 35714.4.1.5 Digital Light Processing (DLP) 35814.5 Strategies Used by Production Company 35814.5.1 Maintenance Strategies 35814.5.1.1 Breakdown Maintenance (BM) 35814.5.1.2 Preventive Maintenance (PM) 35814.5.1.3 Periodic Maintenance (Time Based Maintenance – TBM) 35914.5.1.4 Predictive Maintenance (PM) 35914.5.1.5 Corrective Maintenance (CM) 35914.5.1.6 Maintenance Prevention (PM) 35914.5.2 Inventory Control in Manufacturing 35914.5.2.1 Inventory Control and Maintenance in Manufacturing 36014.5.2.2 Warehouse Storages 36014.5.3 Time Factor in Manufacturing 36114.5.3.1 Breakdown Time 36114.5.3.2 Set-Up Time 36114.5.3.3 Manned Time (Available Time) 36114.5.3.4 Operating Working Time 36114.5.3.5 Operating Time 36214.5.3.6 Production Time 36214.6 Sustainable Manufacturing 36214.6.1 Social Aspect of Sustainable Manufacturing 36314.6.2 Environmental Aspects of Sustainable Manufacturing 36414.6.3 Economical Aspect of Sustainable Manufacturing 36414.7 Sustainable Additive Manufacturing 36514.7.1 Energy 36514.7.2 Cost 36614.7.2.1 Downtime Cost 36614.7.3 Supply Chain 36814.7.4 Maintenance with Additive Manufacturing 36814.8 Additive Manufacturing with IFC CMD: A Case Study 36914.9 Contribution of Additive Manufacturing Towards Sustainability 37014.10 Limitations of Additive Manufacturing 37214.11 Conclusions and Recommendations 373References 373Index 377