Modelling and Optimisation of Laser Assisted Oxygen (LASOX) Cutting: A Soft Computing Based Approach

• Κύριοι συγγραφείς: Chaki, Sudipto (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Ghosal, Sujit (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: SpringerBriefs in Computational Intelligence,
• Θέματα: Computational intelligence.; Manufactures.; Artificial intelligence.; Computational Intelligence.; Manufacturing, Machines, Tools, Processes.; Artificial Intelligence.

 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.