Details
Modelling and Optimisation of Laser Assisted Oxygen (LASOX) Cutting: A Soft Computing Based Approach
SpringerBriefs in Applied Sciences and Technology
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53,49 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 15.12.2018 |
| ISBN/EAN: | 9783030049034 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
This book presents the basics of the Laser Assisted Oxygen (LASOX) cutting process, its development, advantages and shortcomings, together with detailed information on the research work carried out to date regarding the modelling and optimization of the process. It introduces two integrated soft computing-based models consisting of Artificial Neural Networks (ANN-GA and ANN SA) for the modelling and optimization of LASOX cutting. It also includes an in-depth discussion on the basic working algorithms of soft computing tools such as Artificial Neural Networks, Genetic Algorithms, Simulated Annealing etc. The book not only provides an approach to optimizing LASOX by means of soft computing-based integrated models, but also illustrates the practical implementation of the proposed models.
Preface.- Chapter 1. LASOX Cutting: Principles and Evolution.- Chapter 2. Integrated Soft Computing Based Methodologies for Modelling.- Chapter 3. Modelling and Optimisation of LASOX Cutting of Mild Steel: A Case Study.
<p>Sudipto Chaki received his B.Tech. degree in Mechanical Engineering from North Eastern Regional Institute of Science and Technology, Itanagar, India in 2003, his M.Tech. degree in Manufacturing Technology from the National Institute of Technical Teachers’ Training and Research, Kolkata, India in 2007, and his Ph.D. in Engineering from Jadavpur University in 2013. He is presently working as an Associate Professor and Head of the Automobile Engineering Department at MCKV Institute of Engineering, West Bengal, India. He has published 28 research papers so far, in national and international journals and conference proceedings. His research interests include applications of Soft Computing techniques for the modelling and optimization of manufacturing processes.</p>
Sujit Ghosal received his B.Eng. degree in Mechanical Engineering from Jadavpur University, India in 1976, his M.Tech. degree in Mechanical Engineering from the same institute in 1988, and his Ph.D. in Engineering from the Indian Institute of Technology, Kharagpur, India in 1993. He retired as a Professor at the Mechanical Engineering Department of Jadavpur University, India in 2018 and at present working as Professor, Mechanical Engineering Department, Netaji Subhas Engineering College, Garia, Kolkata, India. He has more than 25 years of teaching and research experience and has published large number of research papers in national and international journals and conference proceedings. He is also working as a technical advisor for various Government and multi-national organizations. His research interest includes CFD, droplet combustion, optimization and hydraulic systems.
Sujit Ghosal received his B.Eng. degree in Mechanical Engineering from Jadavpur University, India in 1976, his M.Tech. degree in Mechanical Engineering from the same institute in 1988, and his Ph.D. in Engineering from the Indian Institute of Technology, Kharagpur, India in 1993. He retired as a Professor at the Mechanical Engineering Department of Jadavpur University, India in 2018 and at present working as Professor, Mechanical Engineering Department, Netaji Subhas Engineering College, Garia, Kolkata, India. He has more than 25 years of teaching and research experience and has published large number of research papers in national and international journals and conference proceedings. He is also working as a technical advisor for various Government and multi-national organizations. His research interest includes CFD, droplet combustion, optimization and hydraulic systems.
Presents the basics, advantages and shortcomings of the LASOX cutting process, together with research on modelling and optimizing it Introduces two integrated soft computing-based models consisting of Artificial Neural Networks Includes a detailed discussion on the basic working algorithms of soft computing tools Illustrates the practical implementation of the proposed models
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