Smart Technologies for Improved Performance of Manufacturing Systems and Services

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.