Smart Technologies for Improved Performance of Manufacturing Systems and Services
Herausgeber: Behera, Bikash Chandra; Muduli, Kamalakanta; Moharana, Bikash Ranjan
Smart Technologies for Improved Performance of Manufacturing Systems and Services
Herausgeber: Behera, Bikash Chandra; Muduli, Kamalakanta; Moharana, Bikash Ranjan
- Broschiertes Buch
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
This book discusses smart technologies and their influence in the field of manufacturing and industrial systems engineering, in the context of performability enhancement, and explores the development of the workforce for the execution of such smart and advanced technologies.
Andere Kunden interessierten sich auch für
- B S DhillonApplied Reliability for Engineers59,99 €
- Handbook of Manufacturing Systems and Design81,99 €
- Additive Manufacturing for Plastic Recycling60,99 €
- Douglas BrauerTotal Manufacturing Assurance60,99 €
- Sam A HoutManufacturing of Quality Oral Drug Products60,99 €
- Manufacturing and Industrial Engineering67,99 €
- Industrial Transformation60,99 €
-
-
-
This book discusses smart technologies and their influence in the field of manufacturing and industrial systems engineering, in the context of performability enhancement, and explores the development of the workforce for the execution of such smart and advanced technologies.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 194
- Erscheinungstermin: 30. Januar 2025
- Englisch
- Abmessung: 234mm x 156mm x 11mm
- Gewicht: 304g
- ISBN-13: 9781032387598
- ISBN-10: 1032387599
- Artikelnr.: 72542354
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 194
- Erscheinungstermin: 30. Januar 2025
- Englisch
- Abmessung: 234mm x 156mm x 11mm
- Gewicht: 304g
- ISBN-13: 9781032387598
- ISBN-10: 1032387599
- Artikelnr.: 72542354
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
Dr. Bikash Chandra Behera is an Assistant Professor at the Department of Mechanical Engineering, C.V. Raman Global University, India. He is currently working in the area Artificial Intelligence & Machine Learning applications in manufacturing processes. Dr. Behera received his doctoral degree from IIT Delhi, India. Dr. Bikash Ranjan Moharana is an Assistant Professor at the Mechanical Engineering Department, at C.V. Raman Global University, India. He received his Ph.D. degree at the Department of Mechanical Engineering, National Institute of Technology, India. His research interests include various fusion welding processes, non-traditional machining, process optimization, mechanical and metallurgical analysis. He is a fellow member in Institution of Engineers India, IIW and IWS. Dr. Kamalakanta Muduli, is an Associate Professor at the Department of Mechanical Engineering, Papua New Guinea University of Technology, Papua New Guinea. He obtained his PhD from the School of Mechanical Sciences, IIT Bhubaneswar, India. He is a recipient of the ERASMUS+ KA107 award granted by the European Union. His research interests include Materials Science, Manufacturing, Sustainable supply chain management, and Industry 4.0 applications in operations and supply chain management. Dr Muduli is a fellow of Institution of Engineers India and a senior member of Indian Institution of Industrial Engineering and member of ASME. Professor Sardar M.N. Islam, Ph. D., LL. B. (Law), is a Professor at Victoria University, Australia. He has published extensively across a broad range of disciplines and his research has attracted international acclaim, leading to a large number of appointments as a distinguished visiting professor, visiting professor, or adjunct professor in different countries, as well as a keynote speaker at many international conferences.
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.
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.
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.
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.