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Intelligent Manufacturing Management Systems

Operational Applications of Evolutionary Digital Technologies in Mechanical and Industrial Engineering

Edited by Kamalakanta Muduli, V. P. Kommula, Devendra K. Yadav, M. Chithirai Pon Selvan, and Jayakrishna Kandasamy
Copyright: 2023   |   Status: Published
ISBN: 9781119836247  |  Hardcover  |  
396 pages | 99 illustrations
Price: $225 USD
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One Line Description
The book explores the latest manufacturing techniques in relation to AI and evolutionary algorithms that can monitor and control the manufacturing environment.

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.

Description
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.

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Author / Editor Details
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, 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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Table of Contents
Preface
Part I: Smart Technologies in Manufacturing
1. Smart Manufacturing Systems for Industry 4.0
Gaijinliu Gangmei and Polash Pratim Dutta
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
2. Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities
S. Deepak Kumar, G. Arun Manohar, R. Surya Teja, P. S. V. Ramana Rao, A. Mandal, Ajit Behera and P. Srinivasa Rao
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
3. IoT-Based Intelligent Manufacturing System: A Review
Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Pritam Chakraborty
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
4. 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process
Kali Charan Rath, Prasenjit Chatterjee, Pankajkumar Munibara Patro, Polaiah Bojja, Amaresh Kumar and Rashmi Prava Das
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
5. Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management
Hiranmoy Samanta and Kamal Golui
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
6. Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0
Bikash Chandra Behera, Bikash Ranjan Moharana, Matruprasad Rout and Kishore Debnath
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
7. Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies
Jagadeesha T., Thutupalli Srinivasa Advaith, Choppala Sarath Wesley, Grandhi Sri Sai Charith and Doppalapudi Manohar
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
8. Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment
K. Sujatha, R. Krishnakumar, N.P.G. Bhavani, U. Jayalatsumi, V. Srividhya, C. Kamatchi and R. Vani
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
9. Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate
Sidharth Patro, Trupti Ranjan Mahapatra, Romeo S. Fono Tamo, Allu Vikram Kishore Murty, Soumya Ranjan Parimanik and Debadutta Mishra
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
10. Edge Computing-Based Conditional Monitoring
Granville Embia, Aezeden Mohamed, Bikash Ranjan Moharana and Kamalakanta Muduli
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
Preface
Part I: Smart Technologies in Manufacturing
1. Smart Manufacturing Systems for Industry 4.0
Gaijinliu Gangmei and Polash Pratim Dutta
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
2. Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities
S. Deepak Kumar, G. Arun Manohar, R. Surya Teja, P. S. V. Ramana Rao, A. Mandal, Ajit Behera and P. Srinivasa Rao
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
3. IoT-Based Intelligent Manufacturing System: A Review
Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Pritam Chakraborty
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
4. 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process
Kali Charan Rath, Prasenjit Chatterjee, Pankajkumar Munibara Patro, Polaiah Bojja, Amaresh Kumar and Rashmi Prava Das
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
5. Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management
Hiranmoy Samanta and Kamal Golui
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
6. Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0
Bikash Chandra Behera, Bikash Ranjan Moharana, Matruprasad Rout and Kishore Debnath
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
7. Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies
Jagadeesha T., Thutupalli Srinivasa Advaith, Choppala Sarath Wesley, Grandhi Sri Sai Charith and Doppalapudi Manohar
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
8. Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment
K. Sujatha, R. Krishnakumar, N.P.G. Bhavani, U. Jayalatsumi, V. Srividhya, C. Kamatchi and R. Vani
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
9. Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate
Sidharth Patro, Trupti Ranjan Mahapatra, Romeo S. Fono Tamo, Allu Vikram Kishore Murty, Soumya Ranjan Parimanik and Debadutta Mishra
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
10. Edge Computing-Based Conditional Monitoring
Granville Embia, Aezeden Mohamed, Bikash Ranjan Moharana and Kamalakanta Muduli
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
11. Optimization Methodologies in Intelligent Manufacturing Systems: Application and Challenges
Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Atiur Alam
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
12. Challenges of Warehouse Management Towards Smart Manufacturing: A Case of an Indian Consumer Electrical Company
Natarajan Ramanathan, Neeraj Vairagi, Sakti Parida, Sushanta Tripathy, Ashok Kumar Sar, Kumar Mohanty and Alisha Lakra
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
13. The Impact of Organizational Ergonomics on Teaching Rapid Prototyping
Yaone Rapitsenyane, Patience Erick, Oanthata Jester Sealetsa and Richie Moalosi
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
14. Sustainable Manufacturing Practices through Additive Manufacturing: A Case Study on a Can-Making Manufacturer
Kiren Piso, Aezeden Mohamed, Bikash Ranjan Moharana, Kamalakanta Muduli and Noorhafiza Muhammad
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

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