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Automation in the Welding Industry : Incorporating Artificial Intelligence, Machine Learning and Other Technologies
Automation in the Welding Industry : Incorporating Artificial Intelligence, Machine Learning and Other Technologies
Autore Moinuddin Syed Quadir
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (307 pages)
Altri autori (Persone) SahebShaik Himam
DewanganAshok Kumar
CheepuMurali Mohan
BalamuruganS
Collana Industry 5. 0 Transformation Applications Series
ISBN 1-394-17294-X
1-394-17293-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction to Industry 5.0 -- 1.1 Introduction -- 1.2 Industry 4.0 -- 1.3 Industry 5.0 -- References -- Chapter 2 Advancements in Welding Technologies -- 2.1 Introduction -- 2.2 Quality of Weld Joint -- 2.3 Pulsed Current GMAW -- 2.4 P-GMAW Process Stability Factors -- 2.5 Suitable Pulse Parameters of Selection -- 2.6 Effect of Pulse Parameters -- 2.6.1 Weld Bead Geometry -- 2.6.2 Weld Dilution -- 2.6.3 Weld Microstructure -- 2.7 Pulsed Current GMAW Advances -- 2.8 Double-Pulsed GMAW -- 2.9 Synergic Control -- 2.10 Self-Regulating Control -- 2.11 Microcomputer Control -- 2.12 GMAW Shielding Gas Flow -- 2.13 Particle Image Velocimetry (PIV) -- 2.14 The Measurement of Oxygen (O2) Concentration -- 2.15 Spectroscopic Measurements of Plasma Temperature -- 2.16 P-GMAW Numeric Simulation -- 2.16.1 Approach-1 -- 2.16.2 Approach-II -- References -- Chapter 3 Automation in Welding Industries -- 3.1 Introduction -- 3.1.1 Types of Automatic Welding -- 3.1.2 Challenges of Automatic Welding -- 3.1.3 Benefits of Automatic Welding -- 3.2 Automation Trends -- 3.2.1 Production Monitoring -- 3.2.2 Adaptive Welding Advancements -- 3.2.3 Upstream Practices -- 3.2.4 Collaborative Technology -- 3.2.5 Easier Programming of Automation Systems -- 3.3 Plasma Welding -- 3.4 Laser Welding -- 3.5 Arc Welding -- 3.6 MIG Welding -- 3.7 Resistance Welding -- 3.8 Conclusions -- References -- Chapter 4 Digitalization of Welding Processes -- 4.1 Introduction -- 4.2 Techniques for Process Monitoring -- 4.2.1 Electrical Process Tests: Voltage and Current for Welding -- 4.2.2 Thermal Measurement -- 4.2.3 Optical Measurement -- 4.2.4 Acoustic Measurement -- 4.2.5 Measurement of Displacement and Velocity -- 4.2.6 Measurement of Force -- 4.3 Process Monitoring Applications.
4.3.1 Measurement of Current and Voltage -- 4.3.2 Thermal Measurement -- 4.3.3 Optical Measurement -- 4.3.4 Acoustic Measurement -- 4.3.5 Displacement and Velocity Measurement -- 4.3.6 Measurement of Force -- 4.3.7 EMF Measurement -- 4.4 Future Directions -- References -- Chapter 5 AI and ML in Welding Technologies -- Nomenclature -- 5.1 Introduction -- 5.2 Enhancing the Welding Industry -- 5.3 Machine Learning Algorithm Types -- 5.4 Background of AI and ML -- 5.5 Weld Defects -- 5.6 Level of Weld Quality -- 5.6.1 Mining Industry -- 5.6.2 Challenges in ML Practice -- 5.7 Case Studies -- 5.7.1 Use of AI Programs to Obtain CCT Welding Diagrams -- 5.7.2 Use of Algorithms to Predict the Penetration Depth in Friction Stir Spot Welding -- 5.8 Feasibility of Online Inspection of Ultrasonic Weld Quality -- 5.9 Conclusions -- References -- Chapter 6 Digital Twin in Welding -- 6.1 Introduction -- 6.2 Friction Stir Welding -- 6.2.1 FSW Parameters -- 6.3 Defects in Friction Stir Welding -- 6.3.1 DT for FSW -- 6.4 Laser Welding -- 6.4.1 Heat Conduction Welding -- 6.4.2 Deep Penetration or Keyhole Welding -- 6.4.3 Weld Process Parameters -- 6.4.3 DT for Laser Welding -- 6.5 Summary -- References -- Chapter 7 IoT in Welding Industries -- 7.1 Introduction -- 7.2 Sensing and Analyzing Welding Data via the Internet of Things (IoT) -- 7.2.1 Electrical Information -- 7.2.2 Optical Information -- 7.3 Welding Manufacture Based on IoT -- 7.3.1 Example 1: Arc Quality Management with IoT -- 7.3.2 Example 2: Case Study on IoT-Enabled Molten Metal Temperature Sensing System for Welding -- 7.3.3 Example 3: IoT-Based Safety Monitoring System During Welding Operations -- 7.3.4 Example 4: IoT-Based Monitoring of Submerged Arc Welding Process -- 7.4 Conclusion -- References -- Chapter 8 VR and AR in Welding Technologies -- 8.1 Introduction -- 8.1.1 Virtual Reality (VR).
8.1.2 Augmented Reality (AR) -- 8.1.3 Artificial Intelligence (AI) -- 8.1.4 Machine Learning (ML) -- 8.2 How Intelligent is AI When Coupled with VR/AR? -- 8.3 VR/AR Architecture -- 8.4 Welding Processes -- 8.5 Intelligent Welding Technology -- 8.6 Types of Intelligent Welding Processes -- 8.6.1 Types of Welding Positions -- 8.7 Automated Welding Examples -- 8.7.1 Computer Interface of Automated Welding Processes -- 8.8 Applications of VR and AR in Automated Welding -- 8.9 AI and ML for Visual Inspection of Welds -- 8.9.1 AI in Arc Welding -- 8.9.2 AI Detection of Welding Defects -- 8.9.3 VR/AR Welding Simulator -- 8.10 Limitations in the Existing State-of-the-Art Welding Techniques -- 8.10.1 Advantages of AR/VR -- 8.11 Conclusions -- References -- Chapter 9 Intelligent, Clean Cobot Arc Welding Cell -- 9.1 Chances for SMEs -- 9.1.1 Introduction and Goals -- 9.2 Parameters and Consumption Data -- 9.3 CO2 Footprint Methodology -- 9.4 Result Presentation -- 9.5 Conclusion -- Acknowledgments -- References -- Chapter 10 Welding-Based 3D, 4D, 5D Printing -- Nomenclature -- 10.1 Introduction -- 10.2 Differences Among 3DP, 4DP and 5DP -- 10.3 Materials Used in 3DP, 4DP and 5DP Processes -- 10.3.1 Additive Manufactured Metallic Components -- 10.4 Machinability of Welded Components -- 10.5 Concept of 4D and 5D Printing -- 10.6 FEM-Based Analysis -- 10.7 Applications -- 10.7.1 4D Printing Applications -- 10.7.2 3D Printing in the Aerospace Industry -- 10.7.3 3D Printing in Electronics -- 10.7.4 3D Printing in Electrochemical Industries -- 10.7.5 5D Printing in Dentistry -- 10.7.6 5D Printing in Orthopedics -- 10.8 Conclusions -- References -- Chapter 11 Welding and Joining of Novel Materials -- 11.1 Introduction -- 11.1.1 Concept of High Entropy Alloys (HEAs) -- 11.2 Core Effects -- 11.2.1 High Entropy Effect -- 11.2.2 Sluggish Diffusion Effect.
11.2.3 Severe Lattice Distortion Effect -- 11.2.4 Cocktail Effect -- 11.2.5 Current Status of HEAs -- 11.3 Arc Welding Techniques for HEAs -- 11.4 Solid State Welding -- 11.4.1 Friction Stir Welding (FSW) -- 11.5 Explosive Welding -- 11.5.1 Soldering and Brazing -- 11.6 EBW and EBC of HEAs -- 11.7 Laser Welding of HEAs -- 11.8 Laser Cladding of HEAs -- 11.9 Conclusion and Summary -- References -- Chapter 12 Sustainability in Welding Industries -- 12.1 Introduction -- 12.2 Critical Factors for Sustainability of Welding -- 12.3 Adoptability of Sustainable Welding -- 12.4 New Welding Standards for Sustainability -- 12.5 Resource-Conserving Techniques -- 12.5.1 Sustainable Welding in Practice -- 12.5.2 Boosting Efficiency with Special Welding Processes -- 12.6 Sustainability in Welding Training -- 12.6.1 Sustainable Technologies for Thick Metal Plate Welding -- 12.7 5S Lean Strategy for a Sustainable Welding Process -- 12.7.1 Sustainability Assessment of Shielded Metal Arc Welding (SMAW) Process -- 12.8 A-TIG Welding: A Small Step Towards Sustainable Manufacturing -- 12.8.1 Weight Space Partitions-Based Sustainable Welding -- 12.8.2 Sustainability Assessment of Welding Processes -- 12.8.3 Sustainability in Manufacturing -- 12.9 Sustainability Indices -- 12.10 Conclusion -- References -- Chapter 13 Global Welding Market Growth -- 13.1 Introduction -- 13.1.1 Overview of Global Welding Products Market -- 13.2 Patrons of Global Welding Market -- 13.3 Welding Technologies in the Global Welding Market -- 13.4 Fluxes, Wires, Electrodes, and Fillers -- 13.5 Welding Market Dynamics -- 13.6 Manpower and Labor Challenges in Global Market -- 13.7 COVID-19's Impact on Global Welding Materials Market -- 13.8 New Opportunity in the Welding Market and Advanced Applications -- 13.9 Conclusions -- References.
Chapter 14 Quality Assurance and Control in Welding and Additive Manufacturing -- 14.1 Introduction -- 14.2 Quality Issues in Welding -- 14.3 Quality Issues in 3D Printing -- 14.4 Conclusion -- References -- Chapter 15 Welding Practices in Industry 5.0: Opportunities, Challenges, and Applications -- 15.1 Introduction -- 15.2 Manufacturing Trends -- 15.3 Welding Technology -- 15.3.1 Classification of Welding -- 15.4 Variety of Materials Used by Welding for Industry 5.0 -- 15.4.1 Advantages of Welding -- 15.4.2 Applications -- 15.4.3 Automation -- 15.4.4 Welding-Based AM -- 15.4.5 Welding Trends in Aeronautic Industry -- 15.4.6 Robotic and Automated Welding -- 15.5 Virtual Reality (VR) for Welders -- 15.6 Challenges and Opportunities in Nuclear Reactor -- 15.7 Challenges of AM-Based Functionally Graded Materials Through LDED -- 15.8 Conclusion -- References -- Index -- EULA.
Record Nr. UNINA-9910830287403321
Moinuddin Syed Quadir  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Automation in the Welding Industry : Incorporating Artificial Intelligence, Machine Learning and Other Technologies
Automation in the Welding Industry : Incorporating Artificial Intelligence, Machine Learning and Other Technologies
Autore Moinuddin Syed Quadir
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (307 pages)
Altri autori (Persone) SahebShaik Himam
DewanganAshok Kumar
CheepuMurali Mohan
BalamuruganS
Collana Industry 5. 0 Transformation Applications Series
ISBN 1-394-17294-X
1-394-17293-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction to Industry 5.0 -- 1.1 Introduction -- 1.2 Industry 4.0 -- 1.3 Industry 5.0 -- References -- Chapter 2 Advancements in Welding Technologies -- 2.1 Introduction -- 2.2 Quality of Weld Joint -- 2.3 Pulsed Current GMAW -- 2.4 P-GMAW Process Stability Factors -- 2.5 Suitable Pulse Parameters of Selection -- 2.6 Effect of Pulse Parameters -- 2.6.1 Weld Bead Geometry -- 2.6.2 Weld Dilution -- 2.6.3 Weld Microstructure -- 2.7 Pulsed Current GMAW Advances -- 2.8 Double-Pulsed GMAW -- 2.9 Synergic Control -- 2.10 Self-Regulating Control -- 2.11 Microcomputer Control -- 2.12 GMAW Shielding Gas Flow -- 2.13 Particle Image Velocimetry (PIV) -- 2.14 The Measurement of Oxygen (O2) Concentration -- 2.15 Spectroscopic Measurements of Plasma Temperature -- 2.16 P-GMAW Numeric Simulation -- 2.16.1 Approach-1 -- 2.16.2 Approach-II -- References -- Chapter 3 Automation in Welding Industries -- 3.1 Introduction -- 3.1.1 Types of Automatic Welding -- 3.1.2 Challenges of Automatic Welding -- 3.1.3 Benefits of Automatic Welding -- 3.2 Automation Trends -- 3.2.1 Production Monitoring -- 3.2.2 Adaptive Welding Advancements -- 3.2.3 Upstream Practices -- 3.2.4 Collaborative Technology -- 3.2.5 Easier Programming of Automation Systems -- 3.3 Plasma Welding -- 3.4 Laser Welding -- 3.5 Arc Welding -- 3.6 MIG Welding -- 3.7 Resistance Welding -- 3.8 Conclusions -- References -- Chapter 4 Digitalization of Welding Processes -- 4.1 Introduction -- 4.2 Techniques for Process Monitoring -- 4.2.1 Electrical Process Tests: Voltage and Current for Welding -- 4.2.2 Thermal Measurement -- 4.2.3 Optical Measurement -- 4.2.4 Acoustic Measurement -- 4.2.5 Measurement of Displacement and Velocity -- 4.2.6 Measurement of Force -- 4.3 Process Monitoring Applications.
4.3.1 Measurement of Current and Voltage -- 4.3.2 Thermal Measurement -- 4.3.3 Optical Measurement -- 4.3.4 Acoustic Measurement -- 4.3.5 Displacement and Velocity Measurement -- 4.3.6 Measurement of Force -- 4.3.7 EMF Measurement -- 4.4 Future Directions -- References -- Chapter 5 AI and ML in Welding Technologies -- Nomenclature -- 5.1 Introduction -- 5.2 Enhancing the Welding Industry -- 5.3 Machine Learning Algorithm Types -- 5.4 Background of AI and ML -- 5.5 Weld Defects -- 5.6 Level of Weld Quality -- 5.6.1 Mining Industry -- 5.6.2 Challenges in ML Practice -- 5.7 Case Studies -- 5.7.1 Use of AI Programs to Obtain CCT Welding Diagrams -- 5.7.2 Use of Algorithms to Predict the Penetration Depth in Friction Stir Spot Welding -- 5.8 Feasibility of Online Inspection of Ultrasonic Weld Quality -- 5.9 Conclusions -- References -- Chapter 6 Digital Twin in Welding -- 6.1 Introduction -- 6.2 Friction Stir Welding -- 6.2.1 FSW Parameters -- 6.3 Defects in Friction Stir Welding -- 6.3.1 DT for FSW -- 6.4 Laser Welding -- 6.4.1 Heat Conduction Welding -- 6.4.2 Deep Penetration or Keyhole Welding -- 6.4.3 Weld Process Parameters -- 6.4.3 DT for Laser Welding -- 6.5 Summary -- References -- Chapter 7 IoT in Welding Industries -- 7.1 Introduction -- 7.2 Sensing and Analyzing Welding Data via the Internet of Things (IoT) -- 7.2.1 Electrical Information -- 7.2.2 Optical Information -- 7.3 Welding Manufacture Based on IoT -- 7.3.1 Example 1: Arc Quality Management with IoT -- 7.3.2 Example 2: Case Study on IoT-Enabled Molten Metal Temperature Sensing System for Welding -- 7.3.3 Example 3: IoT-Based Safety Monitoring System During Welding Operations -- 7.3.4 Example 4: IoT-Based Monitoring of Submerged Arc Welding Process -- 7.4 Conclusion -- References -- Chapter 8 VR and AR in Welding Technologies -- 8.1 Introduction -- 8.1.1 Virtual Reality (VR).
8.1.2 Augmented Reality (AR) -- 8.1.3 Artificial Intelligence (AI) -- 8.1.4 Machine Learning (ML) -- 8.2 How Intelligent is AI When Coupled with VR/AR? -- 8.3 VR/AR Architecture -- 8.4 Welding Processes -- 8.5 Intelligent Welding Technology -- 8.6 Types of Intelligent Welding Processes -- 8.6.1 Types of Welding Positions -- 8.7 Automated Welding Examples -- 8.7.1 Computer Interface of Automated Welding Processes -- 8.8 Applications of VR and AR in Automated Welding -- 8.9 AI and ML for Visual Inspection of Welds -- 8.9.1 AI in Arc Welding -- 8.9.2 AI Detection of Welding Defects -- 8.9.3 VR/AR Welding Simulator -- 8.10 Limitations in the Existing State-of-the-Art Welding Techniques -- 8.10.1 Advantages of AR/VR -- 8.11 Conclusions -- References -- Chapter 9 Intelligent, Clean Cobot Arc Welding Cell -- 9.1 Chances for SMEs -- 9.1.1 Introduction and Goals -- 9.2 Parameters and Consumption Data -- 9.3 CO2 Footprint Methodology -- 9.4 Result Presentation -- 9.5 Conclusion -- Acknowledgments -- References -- Chapter 10 Welding-Based 3D, 4D, 5D Printing -- Nomenclature -- 10.1 Introduction -- 10.2 Differences Among 3DP, 4DP and 5DP -- 10.3 Materials Used in 3DP, 4DP and 5DP Processes -- 10.3.1 Additive Manufactured Metallic Components -- 10.4 Machinability of Welded Components -- 10.5 Concept of 4D and 5D Printing -- 10.6 FEM-Based Analysis -- 10.7 Applications -- 10.7.1 4D Printing Applications -- 10.7.2 3D Printing in the Aerospace Industry -- 10.7.3 3D Printing in Electronics -- 10.7.4 3D Printing in Electrochemical Industries -- 10.7.5 5D Printing in Dentistry -- 10.7.6 5D Printing in Orthopedics -- 10.8 Conclusions -- References -- Chapter 11 Welding and Joining of Novel Materials -- 11.1 Introduction -- 11.1.1 Concept of High Entropy Alloys (HEAs) -- 11.2 Core Effects -- 11.2.1 High Entropy Effect -- 11.2.2 Sluggish Diffusion Effect.
11.2.3 Severe Lattice Distortion Effect -- 11.2.4 Cocktail Effect -- 11.2.5 Current Status of HEAs -- 11.3 Arc Welding Techniques for HEAs -- 11.4 Solid State Welding -- 11.4.1 Friction Stir Welding (FSW) -- 11.5 Explosive Welding -- 11.5.1 Soldering and Brazing -- 11.6 EBW and EBC of HEAs -- 11.7 Laser Welding of HEAs -- 11.8 Laser Cladding of HEAs -- 11.9 Conclusion and Summary -- References -- Chapter 12 Sustainability in Welding Industries -- 12.1 Introduction -- 12.2 Critical Factors for Sustainability of Welding -- 12.3 Adoptability of Sustainable Welding -- 12.4 New Welding Standards for Sustainability -- 12.5 Resource-Conserving Techniques -- 12.5.1 Sustainable Welding in Practice -- 12.5.2 Boosting Efficiency with Special Welding Processes -- 12.6 Sustainability in Welding Training -- 12.6.1 Sustainable Technologies for Thick Metal Plate Welding -- 12.7 5S Lean Strategy for a Sustainable Welding Process -- 12.7.1 Sustainability Assessment of Shielded Metal Arc Welding (SMAW) Process -- 12.8 A-TIG Welding: A Small Step Towards Sustainable Manufacturing -- 12.8.1 Weight Space Partitions-Based Sustainable Welding -- 12.8.2 Sustainability Assessment of Welding Processes -- 12.8.3 Sustainability in Manufacturing -- 12.9 Sustainability Indices -- 12.10 Conclusion -- References -- Chapter 13 Global Welding Market Growth -- 13.1 Introduction -- 13.1.1 Overview of Global Welding Products Market -- 13.2 Patrons of Global Welding Market -- 13.3 Welding Technologies in the Global Welding Market -- 13.4 Fluxes, Wires, Electrodes, and Fillers -- 13.5 Welding Market Dynamics -- 13.6 Manpower and Labor Challenges in Global Market -- 13.7 COVID-19's Impact on Global Welding Materials Market -- 13.8 New Opportunity in the Welding Market and Advanced Applications -- 13.9 Conclusions -- References.
Chapter 14 Quality Assurance and Control in Welding and Additive Manufacturing -- 14.1 Introduction -- 14.2 Quality Issues in Welding -- 14.3 Quality Issues in 3D Printing -- 14.4 Conclusion -- References -- Chapter 15 Welding Practices in Industry 5.0: Opportunities, Challenges, and Applications -- 15.1 Introduction -- 15.2 Manufacturing Trends -- 15.3 Welding Technology -- 15.3.1 Classification of Welding -- 15.4 Variety of Materials Used by Welding for Industry 5.0 -- 15.4.1 Advantages of Welding -- 15.4.2 Applications -- 15.4.3 Automation -- 15.4.4 Welding-Based AM -- 15.4.5 Welding Trends in Aeronautic Industry -- 15.4.6 Robotic and Automated Welding -- 15.5 Virtual Reality (VR) for Welders -- 15.6 Challenges and Opportunities in Nuclear Reactor -- 15.7 Challenges of AM-Based Functionally Graded Materials Through LDED -- 15.8 Conclusion -- References -- Index -- EULA.
Record Nr. UNINA-9910876866203321
Moinuddin Syed Quadir  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Convergence of Deep Learning in Cyber-IoT Systems and Security
Convergence of Deep Learning in Cyber-IoT Systems and Security
Autore Chakraborty Rajdeep
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (472 pages)
Altri autori (Persone) GhoshAnupam
MandalJyotsna Kumar
BalamuruganS
Collana Artificial Intelligence and Soft Computing for Industrial Transformation Ser.
Soggetto genere / forma Electronic books.
ISBN 1-119-85768-6
1-119-85767-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910632496103321
Chakraborty Rajdeep  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Intelligent Robots and Cobots : Industry 5. 0 Applications
Intelligent Robots and Cobots : Industry 5. 0 Applications
Autore Ramasamy V
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (0 pages)
Altri autori (Persone) BalamuruganS
PengSheng-Lung
Collana Industry 5. 0 Transformation Applications Series
ISBN 9781394198252
1394198256
9781394198245
1394198248
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgement -- Part 1: Fundamentals -- Chapter 1 Cobots for Industry 5.0 Transformation -- 1.1 Introduction -- 1.2 Related Works -- 1.3 IoT for Industries -- 1.4 Issues with Cobots in Industry 5.0 -- 1.5 Cobots in Industries -- 1.6 Automation and Cobots -- 1.7 Conclusion -- References -- Chapter 2 Cobots as an Enabling Technique for Industry 5.0: A Conceptual Framework -- 2.1 Introduction -- 2.2 Industry 5.0 at a Glance -- 2.3 Industry 4.0 vs. Industry 5.0 -- 2.4 Key Differences Between Robots and Cobots -- 2.5 Cobots as an Enabling Technique for Industry 5.0 -- 2.6 The Contribution of Cobots Across Different Sectors -- 2.6.1 Manufacturing -- 2.6.2 Healthcare -- 2.6.3 Packaging -- 2.6.4 Aerospace and Electronics -- 2.6.5 Textile -- 2.6.6 Agriculture -- 2.6.7 Construction -- 2.6.8 Logistics -- 2.6.9 Automotive -- 2.6.10 Food Processing -- 2.7 A Conceptual Cobot-Based Cyber-Physical System -- 2.7.1 Need for Cobot: Problem Formulation and its Analysis -- 2.7.2 Synthesizing Cobot: Characteristics of Design -- 2.7.3 Cobot Selection -- 2.7.4 Selection of the Gripper -- 2.7.5 Tentative Design Proposal, Simulation, Conditional Prediction, and Evaluation -- 2.7.6 Design of Cobot as a Multi-Perspective System Viewpoint -- 2.8 The Risk and Security Issues with Respect to Cobots and Their Mitigations -- 2.8.1 Safety-Rated Monitored Stop -- 2.8.2 Hand-Guiding -- 2.8.3 Speed and Separation Monitoring -- 2.8.4 Power and Force Limitation -- 2.9 Conclusion -- References -- Chapter 3 Role of Cobots and Industrial Robots in Industry 5.0 -- 3.1 Introduction -- 3.2 Role of Cobots -- 3.3 Programming Flowchart -- 3.3.1 Steps Involved -- 3.4 Objectives of Research in Cobots -- 3.5 Capabilities and Features of Cobots for Industrial Applications.
3.6 Industrial Developments and Different Degrees of Collaboration by Cobots -- 3.7 Cobot Applications -- 3.7.1 Assembly -- 3.7.2 Pick and Place -- 3.7.3 Packaging and Palletizing -- 3.7.4 Quality Control -- 3.7.5 Welding -- 3.8 Challenges Faced by Cobots -- 3.9 Economic Impact of Cobots -- 3.10 Components Required -- 3.10.1 Robot Arm -- 3.10.2 End Effector -- 3.10.3 Sensors -- 3.10.4 Control System -- 3.10.5 Power Source -- 3.10.6 Communication System -- 3.10.7 Mounting Structure -- 3.10.8 Mobility -- 3.11 Integration of Cobots with Other Technologies -- 3.12 Discussion -- 3.13 Future Scope -- 3.14 Conclusion -- References -- Chapter 4 The Evolution of Cobots in Intelligent Transportation Systems -- 4.1 Introduction -- 4.2 Uncovering Challenges in Intelligent Transportation System -- 4.3 The Role and Application of Cobots in Manufacturing and Logistics -- 4.4 Advancing Technologies Facilitating Robot and Cobot Operations in Intelligent Transportation Systems -- 4.5 Redefining Smart Transportation: The Synergy of Robotics, Cobots, and Predictive Analytics in ITS -- 4.5.1 Enhancing Urban Mobility with Robotic-Enabled Route Optimization -- 4.5.2 Revolutionizing Parking Efficiency with Robotic and Cobot Assistance -- 4.5.3 Enhancing Street Lighting with Robotic and Cobot Integration -- 4.5.4 Robotic Intervention in Accident Detection and Prevention -- 4.5.5 Robotic Solutions for Road Anomalies Detection -- 4.5.6 Advanced Vehicle Tracking or Transportation Monitoring -- 4.6 A Comparative Analysis of Cobot and Predictive Protocols in Enhancing Safety and Sustainability in ITS -- 4.6.1 Advancing Eco-Friendly Transportation Through Robotic and Cobot Integration -- 4.6.2 Robotic and Cobot Enhanced Collision Avoidance in Traffic -- 4.6.3 Revolutionizing Transportation: Robotic-Driven Autonomous Vehicles.
4.7 Advanced Analytics and Insights in Intelligent Transportation Systems -- 4.7.1 Robotic-Enhanced Traffic Detection -- 4.7.2 Advanced Road/Lane Detection with Robot -- 4.7.3 Elevating Precision in Navigation -- 4.7.4 Cobot-Driven Vehicle Detection -- 4.7.5 Robotics and 5G Routing for Transportation -- 4.7.6 Robotic Traffic Optimization for Efficient Commuting -- 4.7.7 Robotic Traffic Flow Prediction for Safer Commutes -- 4.7.8 Robotics and ITS Data Transformation -- 4.8 Conclusion -- References -- Chapter 5 Low/No-Code Software Development of Cobots Using Advanced Graphical User Interface -- 5.1 Introduction -- 5.1.1 Low/No Code -- 5.1.2 Analysis of Various Low/No-Code Platform -- 5.1.2.1 Microsoft Power App -- 5.1.2.2 Outsystem -- 5.1.2.3 Kissflow -- 5.1.2.4 Bubble -- 5.1.2.5 Mendix -- 5.2 Cobots -- 5.2.1 Types of Cobots -- 5.2.1.1 Uses -- 5.2.1.2 Advantages of Cobots -- 5.3 Design of Low/No-Code-Based Cobot Development -- 5.4 Graphical User Interface Features -- 5.5 RPA vs. Low Code No Code in Cobot Development: "Low Code or RPA? Who Wins?" -- 5.5.1 Working of RPA -- 5.5.2 Cobots and RPA -- 5.5.3 The Downfall of RPA -- 5.5.4 Low/No Code's Edge Over RPA -- 5.6 Conclusion -- 5.7 Pros and Cons -- 5.7.1 Pros of Cobots -- 5.7.2 Cons of Low/No Code -- 5.7.3 Cons of Cobots -- References -- Chapter 6 Future Workforce for Industry 5.0 -- 6.1 Introduction -- 6.2 Underlying Principles of Industry 5.0 -- 6.2.1 Human Centricity -- 6.2.2 Sustainability -- 6.2.3 Increased Resilience -- 6.3 Benefits for Workers in Industry 5.0 -- 6.3.1 Reduction of Human-Factor Failures -- 6.3.2 Safe and Inclusive Work Environment -- 6.3.3 Job Creation and Better Roles for Human Workers -- 6.3.4 Empowering Workers -- 6.4 Challenges for Workers in Industry 5.0 -- 6.5 Industry 5.0 and Employee Skills -- 6.5.1 Problem Solving -- 6.5.2 Working with People.
6.5.3 Use of Technology and its Development -- 6.5.4 Self-Management -- 6.5.5 Cross-Disciplinary Skills -- 6.6 Issues Related to Integration of Robots into Organizations -- 6.6.1 Learning to Work with Robots -- 6.6.2 Issues Relating to Laws and Regulations -- 6.6.3 Personal Preferences for Utilizing Robots at Work -- 6.6.4 Negative Attitude Toward Robots Due to Shrinking Human Workforce -- 6.6.5 Humans Competing with Robots or Robots Complementing Humans -- 6.6.6 Psychological Consequences of Human-Robot Co-Working -- 6.6.7 Societal Consequences of Human-Robot Collaboration -- 6.6.8 The Shifting Functions of Human Resources Departments -- 6.7 Considerations for Integration of Humans and Smart Machines in Industry 5.0 -- 6.7.1 Augmenting Workforce Through Automation -- 6.7.2 Select Tasks Carefully -- 6.7.3 Retrain and Retain -- 6.7.4 Ensuring Health and Safety -- 6.8 Reskilling and Upskilling the Workforce for Industry 5.0 -- 6.8.1 Workforce Planning -- 6.8.2 Skill Shaping -- 6.8.3 Shifting Skill Profile -- 6.9 Conclusion -- References -- Part 2: Applications -- Chapter 7 Intelligent Robots and Cobots: Concepts and Applications for Industry 5.0 Transformation -- 7.1 Introduction -- 7.1.1 Industry 5.0: Merging Humans and Technology -- 7.1.2 The Role of Intelligent Robots and Cobots -- 7.2 Systematic Review -- 7.3 Concepts of Intelligent Robots and Cobots -- 7.3.1 Definitions and Differentiation -- 7.3.2 Characteristics and Capabilities -- 7.3.3 Human-Centric Design Principles -- 7.4 Benefits of Intelligent Robots and Cobots -- 7.4.1 Enhanced Productivity and Efficiency -- 7.4.2 Improved Safety and Risk Mitigation -- 7.4.3 Workforce Augmentation and Skill Enhancement -- 7.4.4 Flexibility and Adaptability -- 7.5 Application Areas -- 7.5.1 Manufacturing and Production -- 7.5.1.1 Collaborative Assembly and Manufacturing Processes.
7.5.1.2 Quality Control and Inspection -- 7.5.2 Healthcare and Medical Assistance -- 7.5.2.1 Surgical Assistance and Rehabilitation -- 7.5.2.2 Elderly and Patient Care -- 7.5.3 Logistics and Warehouse Automation -- 7.5.4 Agriculture and Farming -- 7.5.5 Construction and Infrastructure -- 7.6 Challenges and Considerations -- 7.6.1 Safety and Risk Management -- 7.6.2 Ethical Implications and Human-Machine Interaction -- 7.6.3 Workforce Transition and Adaptation -- 7.6.4 Legal and Regulatory Frameworks -- 7.7 Future Prospects and Impacts -- 7.7.1 Advancements in Artificial Intelligence and Robotics -- 7.7.2 Human-Centered Approaches and Collaboration -- 7.7.3 Socioeconomic Effects and Employment Landscape -- 7.7.4 Potential Barriers to Adoption -- 7.8 Conclusion -- 7.8.1 Recapitulation of Key Points -- 7.8.2 Future Outlook and Industry 5.0 Transformation -- References -- Chapter 8 Artificial Intelligence-Driven Cobots for Innovative Industry 5.0 Workforce -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Revolution of Industry 5.0 -- 8.4 Robotic Collaboration -- 8.4.1 Widespread Personalization -- 8.4.2 Productivity and a Novel Human-Machine Connection -- 8.4.3 New Employment -- 8.5 Technological Issues with AI in the Cobot Age of Industry 5.0 -- 8.5.1 Real-Time Applications -- 8.5.2 Current Trends -- 8.5.3 Future Directions -- 8.5.3.1 Artificial Intelligence -- 8.5.3.2 Cobots -- 8.5.3.3 Cobots in Industry 5.0 -- 8.5.3.4 Benefits of Using AI-Driven Cobots -- 8.6 Conclusion -- References -- Chapter 9 Cobot Collaboration in the Healthcare Industry -- 9.1 Introduction -- 9.2 Cobots and Their Role -- 9.3 Impact of Cobot -- 9.4 The Challenges of Deploying Cobots at Scale -- 9.5 Cobot Background -- 9.6 Benefits of Cobots -- 9.6.1 Fast Installation -- 9.6.2 Quickly Programmed -- 9.6.3 Can be Used in Different Departments.
9.6.4 More Consistent and Accurate Than Humans.
Record Nr. UNINA-9910916981303321
Ramasamy V  
Newark : , : John Wiley & Sons, Incorporated, , 2025
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Metaheuristics for Machine Learning : Algorithms and Applications
Metaheuristics for Machine Learning : Algorithms and Applications
Autore Kalita Kanak
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (342 pages)
Disciplina 006.3/1
Altri autori (Persone) GaneshNarayanan
BalamuruganS
Collana Artificial Intelligence and Soft Computing for Industrial Transformation Series
Soggetto topico Machine learning
ISBN 9781394233953
1394233957
9781394233946
1394233949
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910876915003321
Kalita Kanak  
Newark : , : John Wiley & Sons, Incorporated, , 2024
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Metaverse and Immersive Technologies : An Introduction to Industrial, Business and Social Applications
Metaverse and Immersive Technologies : An Introduction to Industrial, Business and Social Applications
Autore A Chandrashekhar
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (501 pages)
Altri autori (Persone) SahebShaik Himam
PandaSandeep Kumar
BalamuruganS
PengSheng-Lung
Collana Artificial Intelligence and Soft Computing for Industrial Transformation Series
ISBN 1-394-17714-3
1-394-17716-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910830865803321
A Chandrashekhar  
Newark : , : John Wiley & Sons, Incorporated, , 2023
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Metaverse and Immersive Technologies : An Introduction to Industrial, Business and Social Applications
Metaverse and Immersive Technologies : An Introduction to Industrial, Business and Social Applications
Autore A Chandrashekhar
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (501 pages)
Disciplina 006.8
Altri autori (Persone) SahebShaik Himam
PandaSandeep Kumar
BalamuruganS
PengSheng-Lung
Collana Artificial Intelligence and Soft Computing for Industrial Transformation Series
Soggetto topico Metaverse
ISBN 1-394-17714-3
1-394-17716-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910877641703321
A Chandrashekhar  
Newark : , : John Wiley & Sons, Incorporated, , 2023
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Optimized Computational Intelligence Driven Decision-Making : Theory, Application and Challenges
Optimized Computational Intelligence Driven Decision-Making : Theory, Application and Challenges
Autore Tripathy Hrudaya Kumar
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (360 pages)
Disciplina 006.3
Altri autori (Persone) MishraSushruta
RoutMinakhi
BalamuruganS
MishraSamaresh
Collana Industry 5. 0 Transformation Applications Series
Soggetto topico Computational intelligence
ISBN 1-394-24256-5
1-394-24255-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910876891603321
Tripathy Hrudaya Kumar  
Newark : , : John Wiley & Sons, Incorporated, , 2024
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Tele-Healthcare : Applications of Artificial Intelligence and Soft Computing Techniques
Tele-Healthcare : Applications of Artificial Intelligence and Soft Computing Techniques
Autore Nidhya R
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (418 pages)
Altri autori (Persone) KumarManish
BalamuruganS
Soggetto genere / forma Electronic books.
ISBN 1-119-84193-3
1-119-84192-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910585797203321
Nidhya R  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
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