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Advanced Welding Technologies

Edited by Sandip Kunar and Gurudas Mandal
Series: Advances in Production Engineering
Copyright: 2025   |   Expected Pub Date:2025/03/30
ISBN: 9781394331895  |  Hardcover  |  
606 pages

One Line Description
Advanced Welding Technologies serves as a vital resource that transforms the
perception of welding from a mere skill-based practice to a cutting-edge industrial method, offering comprehensive insights into its fundamental processes, research advancements, and diverse applications across technological and biomedical domains.

Audience
Engineers, welders, researchers, academics, and students involved in advanced welding technologies, manufacturing, and materials science.

Description
Welding has traditionally been considered more of a skill-based proficiency than a technological industrial method. The reliance on highly trained human operators, along with the high cost and low reproducibility of many welding processes, has contributed to this perception. Extensive research has now been conducted on the fundamentals of welding processes, and while it remains a complex, multidisciplinary subject, the basic concepts are well understood.
Advanced Welding Technologies is a comprehensive collection of the processes and applications of advanced welding technologies, giving a basic understanding of each process, research advancements, and their applications in various technological and biomedical domains for improving machining accuracy and quality. This volume will extend the possibilities of research in various areas of advanced welding technologies with basic and hybrid approaches, making it an easy reference for students and researchers working in this critical field.
Readers will find the book:
•Covers technologies for successful improvement in advanced processes and the application of advanced welding technologies;
•Serves as a valuable reference to students and researchers involved in working with advanced welding technologies;
•Discusses successful fabrication of multipurpose advanced welding technologies, sustainability of advanced welding technologies, materials and processes, applications of machine learning in advanced welding technologies, and future scopes and challenges of advanced welding technologies.

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Author / Editor Details
Sandip Kunar, PhD, is an assistant professor in the Department of Mechanical Engineering, Aditya Engineering College, A.P., India. He has published more than 50 research papers in various reputed international journals, national and international conference proceedings, 16 book chapters, and nine books, as well as two patents. His research interests include non-conventional machining processes, micromachining processes, advanced manufacturing technology, and industrial engineering.

Gurudas Mandal, PhD, is an assistant professor in the Department of Metallurgical Engineering at Kazi Nazrul University, West Bengal, India. He has 20 technical research publications in reputed international journals, eight conference proceedings, nine book chapters, and one patent. He also serves as a reviewer for several reputed journals and has organized and chaired numerous national and international conferences. His research interests include alloy steel, cast iron, composite materials, magnetic materials, hydrogen energy, and archaeometallurgy.

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Table of Contents
Preface
Acknowledgements
Part I: Advanced Welding Processes
1. Introduction to Advanced Welding Processes
Sandip Kunar, Jagadeesha T., Chika Oliver Ujah, Norfazillah Talib, Gurudas Mandal, Akhilesh Kumar Singh and Aezeden Mohamed
1.1 Introduction
1.2 Defining Advanced Manufacturing
1.3 Technologies Enabling Sophisticated Manufacturing
1.3.1 Additive Manufacturing
1.3.2 Robotics and Automation
1.3.3 Data Analytics and Industrial IoT
1.3.4 Benefits and Impact
1.3.5 Applications and Industries
1.4 Welding Processes
1.4.1 Laser Welding
1.4.2 Electron Beam Welding
1.4.3 Friction Stir Welding
1.4.4 Ultrasonic Welding
1.4.5 Additive Manufacturing and Welding
1.4.6 Robotic Welding and Automation
1.4.7 Hybrid Welding Processes
1.4.8 Atomic Hydrogen Welding
1.4.9 Magnetic Pulse Welding
1.4.10 Explosion Welding
1.4.11 Plasma Arc Welding
1.4.12 High-Energy-Density Welding Processes
1.4.13 Narrow-Gap Welding Techniques
1.4.14 AI and ML in Welding Technologies
1.4.15 IoT in Welding Industries
1.4.16 Microwave Welding
1.5 Effect of Post Weld Heat Treatment
1.6 Challenges and Future Directions
1.7 Conclusion
References
2. Developments and Trends in Welding Development
Sandip Kunar, Jagadeesha T., Gurudas Mandal, Norfazillah Talib, Adduri S. S. M. Sitaramamurty, Akhilesh Kumar Singh and K. Rakesh Varma
2.1 Introduction
2.1.1 Cost Effectiveness
2.2 Safety and Environmental Factors
2.3 Skill and Training Requirements
2.4 Areas for Development
2.5 Process Application Trends
2.5.1 Consumable Trends
2.5.2 Automation
2.5.3 New Processes
2.5.4 Advanced Materials
2.6 Summary
References
3. Welding Power Source Technology
Sakthimurugan D., Thavasilingam K., Praveen Kumar A., Karthikeyan K. and Nagachandrika K.K.
3.1 Introduction
3.2 Fundamentals of Welding Power Sources
3.2.1 Definition and Purpose
3.2.2 Basic Components
3.3 Key Parameters and Characteristics
3.4 Types of Welding Power Sources
3.4.1 Transformers
3.4.2 Rectifiers
3.4.3 Inverters
3.4.4 Hybrid Power Sources
3.5 Advanced Features and Technologies
3.5.1 Pulse Welding
3.5.2 Remote Monitoring and Control
3.5.3 Energy Efficiency and Sustainability
3.6 Future Trends and Innovations
3.7 Case Studies and Practical Applications
3.8 Conclusion
References
4. Welding Automation and Robotics
Jyoti Bhattacharjee and Subhasis Roy
4.1 Introduction
4.1.1 Early Beginnings
4.1.2 Post-War Innovations
4.1.3 Rise of Robotics in the 1960s and 1970s
4.1.4 Modern Era
4.1.5 Industrial Automation
4.2 Inspection of Welding Performance
4.2.1 Welding Robots
4.3 Robotics in Welding
4.3.1 Motivations
4.3.2 Disadvantages of Welding Robots
4.3.3 Automated Welding
4.4 Modeling of Welding Processes
4.4.1 Neural Network
4.4.2 Deep Learning
4.4.3 Finite Element Analysis (FE)
4.4.4 Regression Analysis
4.4.5 Algorithms
4.5 Robotics in Different Welding Techniques
4.5.1 Spot Welding
4.5.2 Bead Tracking, Weld Dimension, and Defect Monitoring and Control
4.6 Laser Welding and Laser–Arc Hybrid Welding
4.7 Path/Motion Planning
4.8 Challenges and Future Directions
4.9 Conclusions
References
5. Material Flow and Heat Transfer Analysis in Friction Stir Welding
Sumanta Banerjee and Prokash C. Roy
5.1 Introduction and Background
5.2 Material Flow and Heat Transfer: Review of Basic Principles
5.3 Foundations of Mathematical Modeling
5.4 Analysis of Basic Modes of Thermal Energy Transfer
5.5 Analysis of Material Flow in FSW
5.6 Analysis of Heat Generation in FSW
5.6.1 Analysis of Heat Generation from Stationary Heat Source
5.6.2 Analysis of Heat Generation from Moving Heat Source
5.7 Overview of Thermo-Mechanical Process in FSW
5.8 Conclusion: State-of-the Art and Future Directions in FSW
References
6. Atomic Hydrogen Welding
Chika Oliver Ujah, Rodolphe N’Dedji Sodokin and Daramy Vandi von Kallon
6.1 Introduction
6.2 Welding Techniques
6.3 Overview of Atomic Hydrogen Welding
6.4 Operating Principle of Atomic Hydrogen Welding
6.4.1 Application Area of Atomic Hydrogen Welding
6.4.2 The Characteristics of Atomic Hydrogen Welding
6.5 Prospects and Limitations of Atomic Hydrogen Welding
6.5.1 Prospects of Atomic Hydrogen Welding
6.5.2 Limitations of Atomic Hydrogen Welding
6.6 Further Research in Atomic Hydrogen Welding
6.7 Conclusion and Recommendation
Acknowledgment
References
7. Modeling and Designing of Ultrasonic Welding
Jagadeesha T. and Sandip Kunar
7.1 Introduction
7.2 Description of Process
7.3 Description of Equipment
7.4 Design of Ultrasonic Horns (Velocity Transformers) for Welding Applications
7.5 Process Parameters
7.6 Process Capability
7.7 Applications of USW
7.8 Advantages of USW
7.9 Disadvantages of USW
7.10 Conclusion
References
8. Microstructural Control and Heat Treatment Effects in Explosion Welding
Sonika Chauhan, Nisha Rani, Deepa Singh and Neeraj Bisht
8.1 Introduction
8.1.1 Types of Explosion Welding
8.2 Factors Affecting Explosion Welding
8.2.1 Stand-Off Distance
8.2.2 Formation of Jet
8.2.3 Detonation Velocity
8.2.4 Angle of Collision (β)
8.3 Microstructures at the Junction of Materials Fused by Explosion Welding
8.4 Effect of Heat Treatment on Weld Interfaces
8.5 Underwater Explosion Welding
8.6 Applications of Explosion Welding
8.7 Conclusions
References
9. Hybrid Welding Processes
S. Ajithkumar, B. Arulmurugan, D. Balaji, V. Manoj Mohan Prasath and S. Idhayaraja
9.1 Introduction
9.2 Hybrid Laser–Arc Welding Process Development
9.3 Hybrid Laser Arc Welding Methods
9.3.1 Hybrid Laser GMAW Approach
9.3.2 Hybrid PAW Method
9.3.3 Hybrid Laser GTAW Approach
9.4 Hybrid Laser Arc Welding Process Variables
9.4.1 Welding Speed
9.4.2 Laser Power
9.4.3 Laser Beam and Electrode Relative Positioning
9.4.4 Wire Feed Rate
9.4.5 Selection of the Focus Area
9.4.6 Laser-to-Electrode Distance
9.5 Industrial Applications of Hybrid Laser Arc Welding
9.6 Conclusion
References
10. Ultrasonic Welding: Foundations, Influential Factors, and Material Applications
Marxim Rahula Bharathi B., Akhilesh Kumar Singh, D.V.S.S.S.V. Prasad, A. Ramesh, P. V. Elumalai and N.S. Balaji
10.1 Introduction
10.2 Principle of Ultrasonic Welding
10.2.1 Equipment for Ultrasonic Welding
10.2.1.1 Ultrasonic Generator
10.2.1.2 Transducer and Booster
10.2.1.3 Ultrasonic Horn
10.3 Factors Influencing Ultrasonic Welding
10.3.1 Welding Power
10.3.2 Amplitude
10.3.3 Welding Time
10.3.4 Welding Pressure
10.3.5 Energy Directors
10.4 Ultrasonic Welding on Various Materials
10.4.1 The Impact of Ultrasonic Welding on Aluminum Alloys
10.4.2 Steel and Its Alloys
10.4.3 Magnesium
10.4.4 Titanium
10.4.5 Fiber-Reinforced Composites
10.4.6 Thermoplastics
10.5 Applications
10.5.1 Medical Devices and Equipment
10.5.2 Automotive Industry
10.5.3 Electronics and Electrical Components
10.5.4 Packaging Industry
10.5.5 Textile and Apparel Industry
10.5.6 Consumer Electronics and Appliances
10.5.7 Renewable Energy
10.5.8 Aerospace Industry
10.6 Advantages and Limitations
10.6.1 Advantages
10.6.2 Limitations
References
Part II: Applications of Computational Techniques and Sustainability in Welding
11. Review and Analysis of Intelligent Welding Using Automation and Machine Learning Tools
Protyasha Kundu, Swarnadeep Saha, Sumanta Banerjee and Anindita Kundu
11.1 Introduction
11.2 Review of Welding Techniques and Literature Survey
11.2.1 Tungsten Inert Gas (TIG) Welding: An Overview
11.2.2 Friction Stir Welding (FSW): An Overview
11.2.3 Laser Welding: An Overview
11.2.4 Resistance Welding: An Overview
11.2.5 Metal Inert Gas Welding (MIG): An Overview
11.2.6 Plasma Arc Welding (PAW): An Overview
11.3 Machine Learning Models
11.3.1 Decision Trees
11.3.2 Random Forest Classifier
11.3.3 Support Vector Machine
11.3.4 Artificial Neural Networks (ANNs)
11.4 Predicting Current in Tungsten Inert Gas (TIG) Welding
11.4.1 Methodology
11.4.2 Gaussian Process Regression
11.4.3 Kernel Ridge Regression
11.4.4 Results
11.5 Predicting Conditions for Void Formation in Friction Stir Welding
11.5.1 Methodology
11.5.2 Gaussian Process Classifiers
11.5.3 Results
11.6 Conclusion and Future Scope
References
12. Machine Learning in Welding Technologies
D.V.S.S.S.V. Prasad, Akhilesh Kumar Singh, Marxim Rahula Bharathi B., Yerrapragada K.S.S. Rao and V.V. Kamesh
12.1 Introduction
12.1.1 The Evolution of Welding Technologies
12.1.2 Importance of Welding in Industry
12.1.3 Machine Learning: An Overview
12.1.4 Relevance of Machine Learning to Industrial Applications
12.1.5 Machine Learning in Welding Technologies
12.2 Fundamentals of Welding Technologies
12.2.1 Types of Welding Processes
12.2.2 Recent Advances
12.2.3 Key Parameters in Welding
12.2.4 Challenges in Welding
12.3 Basic Concepts of Machine Learning
12.3.3 Machine Learning Workflow
12.4 Applications of Machine Learning in Welding
12.5 Challenges and Future Directions
12.5.1 Challenges in Implementing Machine Learning in Welding
12.5.2 Future Trends in Machine Learning for Welding
12.5.3 Ethical and Practical Considerations in Machine Learning for Welding
12.6 Conclusion
References
13. Artificial Intelligence and Machine Learning in Welding Technologies
Thavasilingam K., Giridharan K., Yuvaraj S., Gopi Kannan K. and Prashanth M.
13.1 Introduction to AI and ML in Welding
13.2 Quality Control and Defect Detection in Welding
13.3 Automation in Challenging Environments in Welding
13.4 Data-Driven Approach in Welding
13.5 Conclusion
References
14. Internet of Things in Welding Industries
Alok Kumar and Ravi Shankar Rai
14.1 Introduction
14.1.1 IoT-Based System
14.1.2 Internet of Things (IoT)
14.1.3 Welding-Based Revolution Industry 4.0
14.2 Functioning of Arc Welding
14.2.1 Voltage Effect in Welding Machine (WM)
14.2.2 Functioning of Voltage Sensors (VS)
14.2.3 Different Types of Probes
14.2.4 Voltage Data Collection From Internet Cloud
14.2.5 Welding Manufacture Based on IoT
14.2.6 Sensing and Analyzing of Welding Information
14.2.6.1 Electrical Information
14.2.6.2 Optical Information With Spectrum Emission
14.2.6.3 Vision Information (VI)
14.2.6.4 Welding Sound Information
14.2.6.5 Other Welding Information
14.3 Structure of Welding
14.3.1 Structure
14.3.2 Circuit Diagram
14.3.3 Expert Welding System
14.3.4 Data Collected From IoT-Based Welding Expert System
14.3.5 Data Design Structure of a Welding Expert System
14.4 Welding Technologies and Resources
14.4.1 Digitalized Welding Science
14.4.2 Modern Welding Power Sources
14.4.3 Data Storage Communication
14.4.4 Data Storage Welding Parameter
14.4.5 Data Security
14.4.6 Identification of Welding Torch Position
14.4.7 Interpretation of WM and Humans
14.4.8 Virtual Welding
14.5 Monitoring System of IoT-Based Welding
14.5.1 Online Process Monitoring
14.5.2 Temperature Measurement Monitoring
14.5.3 Current–Voltage Monitoring
14.5.4 Acoustic Monitoring
14.5.5 IoT Monitoring
14.5.6 Sensor Process Monitoring
14.5.7 Website System Monitoring
14.6 Applications of IoT-Based Welding
14.6.1 Ongoing Applications of the IoT
14.6.2 Smart Materials
14.7 Conclusions
References
15. Evolution of Technology in Testing and Inspection of Welding: From Traditional to Modern Methods
Akhilesh Kumar Singh, Sandip Kunar, M. Zubairuddin, Marxim Rahula Bharathi B., D.V.S.S.S.V. Prasad and Yarrapragada K.S.S. Rao
15.1 Introduction
15.2 Stages of Weld Inspection and Testing
15.3 Weld Testing and Inspection After Fabrication Can Be Classified Into Two Main Categories
15.4 Purposes of Material Testing
15.5 Overview of Welding as a Critical Process in Manufacturing and Construction
15.6 Importance of Testing and Inspection in Ensuring Weld Quality and Integrity
15.6.1 Evolution of Non-Destructive Testing (NDT) Technology in Testing and Inspection of Welding
15.7 Impact of Technological Advancements
15.8 Conclusions
References
16. Sustainability in Welding Industries
P. Sivasankaran
16.1 Introduction
16.1.1 In Practice: Sustainable Welding
16.1.2 Improving Effort Using Welding Procedure
16.1.3 Expertise of Endurance in Welding
16.2 Strategies for Implementing Environmentally Friendly Welding
16.3 Eco-Friendly Welding Procedures
16.4 Research Advancement in Sustainability in Welding Industries
16.4.1 Automation in Sustainable Welding
16.5 Future Scope of Sustainability in Welding
16.6 Conclusion
References
17. Mechanical and Structural Evaluation of Friction Stir Welded AA6063 and AA6069 Aluminum Alloys for Marine and Aerospace Applications
M. Arockia Jaswin and R. Geetha
17.1 Introduction
17.2 Materials and Methodology
17.2.1 Aluminum Alloys
17.2.2 Experimental Methods
17.2.2.1 Mechanical Tests—Tensile Strength
17.2.2.2 Bending Strength Test
17.2.2.3 Hardness Test
17.2.2.4 Microstructure Analysis
17.3 Results and Discussions
17.3.1 Tensile Strength
17.3.2 Bending Test
17.3.3 Hardness
17.3.4 Optical Microscopy Studies
17.4 Conclusion
References
18. Experimental Analysis and Optimization of Microwave Welding Parameters for Improved Joint Strength
Kishor Jyoti Deka, Shubhajit Das and Sangeeta Das
18.1 Introduction
18.2 Review of MW for Polymer-Based Materials
18.3 Experimental Analysis
18.4 Effect of Input Process Parameters on the Lap Shear Strength and Elongation
18.4.1 Response Surface Methodology (RSM) Modeling for Lap Shear Strength and Elongation
18.4.2 Parametric Variation of Lap Shear Strength and Elongation
18.4.3 ANOVA for Lap Shear Strength and Elongation
18.5 Multi Objective Optimization Using Desirability Analysis
18.6 Conclusion
References
19. Metaheuristic Approaches for the Optimized Friction Stir Welding Process
Pritam Pain and Supriyo Roy
19.1 Introduction
19.2 Experimental Setup
19.3 Taguchi Analysis
19.3.1 Result Calculation Using ANOVA
19.3.1.1 Analysis to Enhance σ
19.3.1.2 Analysis to Maximize δl Using ANOVA
19.3.2 Result Calculation Using S/N Ratio Using ANOVA
19.3.2.1 Analysis to Maximize σ Using S/N Ratio
19.3.2.2 Analysis to Maximize δl Using S/N Ratio
19.4 ANN (Artificial Neural Network)
19.4.1 Analysis to Maximize σ Using ANN Model
19.4.2 Analysis to Maximize δl Using ANN Model
19.5 PSO
19.5.1 Multi Objective Optimization of UTS and TE
19.6 Conclusion
References
20. Laser–Arc Hybrid Welding
Ravindra Nath Yadav, Basant Kumar Bhuyan, Sanjeev Kumar Singh Yadav and Sanjay Mishra
20.1 Introduction
20.2 Laser–Arc Hybrid Welding
20.3 Orientation of LAHW Process
20.4 Classification of LAHW Process
20.5 Parameters of LAHW Process
20.6 Process Capability of LAHW Process
20.7 Summary
Acknowledgment
References
21. Beyond Fumes and Flux: Green Welding for a Sustainable Future
M. Abdur Rahman, G. Rajesh, S. Jeavudeen, R. Karunanithi and N. Sri Rangarajalu
21.1 Introduction
21.2 Green Welding: Techniques and Research Developments
21.2.1 Friction Stir Welding (FSW)
21.2.2 Laser Beam Welding (LBW)
21.2.3 Using Electron Beam Welding (EBW)
21.2.4 Gas Tungsten Arc Welding (GTAW) or TIG Welding
21.2.5 Cold Metal Transfer (CMT)
21.3 Exploring the Frontiers of Green Welding: Latest Scientific Studies
21.4 Welding and Industry 4.0
21.5 Welding and Industry 5.0
21.6 Conclusion
21.7 Future Scope of Green Welding Technologies
21.7.1 Technological Advancements
21.7.2 Sustainability Focus
21.7.3 Industries 4.0 and 5.0 Integration
21.7.4 Broader Applications
References
22. Toward a Greener Weld for Integrating Sustainability Into Welding Practices
Sunita Routray, Ranjita Swain and Rudra Narayan Mohapatro
22.1 Introduction
22.1.1 Arc Welding
22.1.2 Gas Welding
22.1.3 Resistance Welding
22.1.4 Solid-State Welding
22.2 Efficient Welding Process Technology in Industry
22.3 Importance of Welding in Industries in India and Worldwide
22.4 Energy Consumption in Welding
22.4.1 Reduction of Energy Consumption During Welding
22.5 Environmental Impact of Welding
22.5.1 Environmentally Friendly Welding Techniques
22.6 Industry 4.0 in Welding
22.7 New Welding Techniques to Merge in the Future
22.7.1 Magnetic Arc Welding Process
22.7.2 Explosive Welding
22.7.3 Ultrasonic Welding
22.7.4 Friction Stir Welding
22.7.5 Electron Beam Welding
22.7.6 Laser Welding
22.7.7 Hybrid Welding
22.7.8 Robotic Welding
22.7.9 Welding Cobots
22.7.10 Welding Drones
22.8 Welding as a Career
22.9 Conclusions
References
23. Advancements in Robotic Welding Sensing Technology: A Review
S. Ajithkumar, B. Arulmurugan, V. Manoj Mohan Prasath, P. Utchimahali Muthu Raja and M. Aravindh
23.1 Introduction
23.2 Sensing Technology in Robotic Welding
23.2.1 Vision-Based Sensing
23.2.2 Force Torque Sensing
23.2.3 Thermal Sensing
23.2.4 Gas Sensing
23.2.5 Acoustic and Vibration Sensing
23.3 Integration of Sensing Technology and Artificial Intelligence in Robotic Welding
23.4 Industrial Internet of Things in Robotic Welding
23.5 Future Scope
23.6 Conclusion
References
24. Robotic Welding and Automation: Cutting-Edge Technology for Industry
S. Ajithkumar, B. Arulmurugan and S. Idhayaraja
24.1 Introduction
24.2 Sensing System
24.3 Signal Monitoring
24.3.1 Welding Current Monitoring
24.3.2 Voltage Monitoring
24.3.3 Wire Feed Speed and Temperature Monitoring
24.3.4 Data Acquisition and Analysis
24.4 Supervised Learning Intelligent Robotic Welding
24.5 Adaptive Robotic Welding System
24.6 Path Planning and Trajectory Optimization
24.7 Future Scope
24.8 Conclusion
References
25. Additive Manufacturing Integration with Welding: A Focus on Wire Arc Additive Manufacturing (WAAM)
M. Sivakumar, C.T. Justus Panicker, N.S. Balaji, Marxim Rahula Bharathi B. and G. Suresh
25.1 Introduction
25.2 Wire Arc Additive Manufacturing
25.2.1 Various Welding Techniques Used in WAAM
25.2.1.1 Gas Metal Arc Welding (GMAW)
25.2.2 Gas Tungsten Arc Welding (GTAW)
25.2.3 Submerged Arc Welding
25.2.4 Spin Arc Welding
25.2.5 Cold Metal Transfer (CMT)
25.2.6 Plasma Arc Welding (PAW)
25.3 Integration of Automation in WAAM
25.3.1 Transformation Through Automation
25.3.2 Enhancements in Productivity and Efficiency
25.3.3 Quality and Consistency
25.3.4 Safety Enhancements
25.3.5 Material and Resource Efficiency
25.4 Monitoring and Control Methods in WAAM
25.4.1 Quality Assurance in Additive Manufacturing
25.4.2 Sensing Techniques for Defect Detection in WAAM
25.4.3 Vision Sensing
25.4.4 Spectral Sensing
25.4.5 Acoustic Sensing
25.4.6 Electric Signal Analysis
25.4.7 Thermal Sensing
25.5 Digital Twin in WAAM
25.5.1 Digital Twin System Model for WAAM
25.5.2 External Monitoring and Sensing
25.5.3 WAAM Digital Twin Functions
25.5.3.1 Data Collection
25.5.3.2 Data Management
25.5.3.3 Data Processing
25.5.3.4 Simulation
25.5.3.5 Prediction
25.5.3.6 Automatic Decision Making
25.6 Improving WAAM Outcomes with Digital Twin and Augmented Reality Innovations
25.7 Summary
References
26. Current Scenario, Future Scope, and Challenges in Welding
N.S. Balaji, M. Sivakumar, Marxim Rahula Bharathi B. and Suresh G.
26.1 Introduction
26.2 Current Trends in Intelligent Welding Systems
26.2.1 Overview of Intelligent Welding
26.2.1.1 Progress of Welding Systems
26.2.1.2 Fundamentals of Intelligent Welding
26.2.1.3 Fundamental Intelligence Traits
26.3 Advances in Welding of High Entropy Alloys (HEAs)
26.4 Advanced Techniques in Welding Defect Identification and Categorization
26.4.1 Weld Defect Detection and Characterization
26.4.2 Machine Learning-Based Approaches
26.4.3 Employing Image-Processing Techniques
26.5 Innovations in Vision Technology for Welding Automation
26.5.1 Enhancements in Hardware Capabilities
26.5.2 Advancements in Composite Sensing Systems
26.5.3 Integration of Image Processing and Machine Learning
26.5.4 Multi-Intelligent Group Control Technology
26.6 Integration of Industry 4.0 and Cyber Physical Systems in Welding Technology
26.6.1 Industry 4.0 and Cyber Physical Systems
26.7 Complexities in Copper Welding: Emphasis on Laser Beam Welding
26.7.1 Copper Welding Difficulties
26.7.2 Challenges in Laser Beam Welding (LBW) of Copper
26.7.3 Challenges in Gas Metal Arc Welding (GMAW)
26.7.4 Challenges in Gas Tungsten Arc Welding (GTAW)
26.7.5 Challenges in Friction Stir Welding of Copper
26.8 Summary
References
Index

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