Intelligent Manufacturing Management Systems: Operational Applications of Evolutionary Digital Technologies in Mechanical and Industrial Engineering

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
12.4 Results and Discussion
12.5 Practical Implications
12.6 Conclusions
References
Chapter 13 The Impact of Organizational Ergonomics on Teaching Rapid Prototyping
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
Chapter 14 Sustainable Manufacturing Practices through Additive Manufacturing: A Case Study on a Can-Making Manufacturer
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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