Smart Technologies for Improved Performance of Manufacturing Systems and Services
portes grátis
Smart Technologies for Improved Performance of Manufacturing Systems and Services
Moharana, Bikash Ranjan; Muduli, Kamalakanta; Islam, Sardar M.N.; Behera, Bikash Chandra
Taylor & Francis Ltd
01/2025
194
Mole
9781032387598
Pré-lançamento - envio 15 a 20 dias após a sua edição
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1. Enhancement of Manufacturing Sector Performance with the Application of Industrial Internet of Things (IIoT). 2. Reliability Prediction Using Machine Learning Approach. 3. Quality Control in the Era of IoT and Automation in the Context of Developing Nations. 4. Precision Positioning of Robotic Manipulators in Manufacturing Processes through PID Controller to Contribute Towards Sustainability. 5. Roll of Additive Manufacturing in Industry 4.0. 6. Challenges and Prospects of Welding 4.0 Adoption: Implication for Emerging Economics. 7. An Approach to Friction Stir Additive Manufacturing of Light Weight Metal Alloys. 8. Analysis and Improvement Performance of Manufacturing in Friction Stir Welding. 9. Multi-response Optimization of Process Parameters in Friction Stir Additive Manufacturing of Magnesium Alloy. 10. Ultrasonic Welding for Light-Weight Structural Applications: An Industry Perspective. 11. Supervised Machine Learning Algorithms for Machinability Assessment of Graphene Reinforced Aluminium Metal Matrix Composites. 12. Focused Ion Beam Machining as a Technology for long term sustainability.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Industry 4.0;Predictive Maintenance;Process Planning;Artificial Intelligence;Logistics System;Inventory System;Machine Learning;FSW;Friction Stir;IoT Device;Friction Stir Processing;Additive Manufacturing;Tool Rotation Speed;Magnesium Alloys;CBS;Traverse Speed;SEM Image;RFR Model;Train Data;LMIS;PID Controller;Weld Time;DTR;Stir Casting;Fib System;Grey Relational;Maximum Ut;Reinforced Aluminum Metal Matrix Composites;Friction Stir Process;Fib;Welding Process
1. Enhancement of Manufacturing Sector Performance with the Application of Industrial Internet of Things (IIoT). 2. Reliability Prediction Using Machine Learning Approach. 3. Quality Control in the Era of IoT and Automation in the Context of Developing Nations. 4. Precision Positioning of Robotic Manipulators in Manufacturing Processes through PID Controller to Contribute Towards Sustainability. 5. Roll of Additive Manufacturing in Industry 4.0. 6. Challenges and Prospects of Welding 4.0 Adoption: Implication for Emerging Economics. 7. An Approach to Friction Stir Additive Manufacturing of Light Weight Metal Alloys. 8. Analysis and Improvement Performance of Manufacturing in Friction Stir Welding. 9. Multi-response Optimization of Process Parameters in Friction Stir Additive Manufacturing of Magnesium Alloy. 10. Ultrasonic Welding for Light-Weight Structural Applications: An Industry Perspective. 11. Supervised Machine Learning Algorithms for Machinability Assessment of Graphene Reinforced Aluminium Metal Matrix Composites. 12. Focused Ion Beam Machining as a Technology for long term sustainability.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Industry 4.0;Predictive Maintenance;Process Planning;Artificial Intelligence;Logistics System;Inventory System;Machine Learning;FSW;Friction Stir;IoT Device;Friction Stir Processing;Additive Manufacturing;Tool Rotation Speed;Magnesium Alloys;CBS;Traverse Speed;SEM Image;RFR Model;Train Data;LMIS;PID Controller;Weld Time;DTR;Stir Casting;Fib System;Grey Relational;Maximum Ut;Reinforced Aluminum Metal Matrix Composites;Friction Stir Process;Fib;Welding Process
