Data-Driven Optimization of Manufacturing Processes
Language: English
Published by Engineering Science Reference, 2020
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 287.96
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nach der Bestellung gedruckt Neuware - Printed after ordering - All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.…
Seller Inventory # 9781799872061
- Title
- Data-Driven Optimization of Manufacturing Processes
- Author
- Kanak Kalita
- Publisher
- Engineering Science Reference
- Publication year
- 2020
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1799872068
- ISBN 13
- 9781799872061
- Item weight
- 1,058 grams
- Dimensions
- 286x221x22 mm
Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.
"Synopsis" may belong to another edition of this title.
About the Author
Ranjan Kumar Ghada i received his B. Tech in Mechanical Engineering from Biju Patnaik University of Technology, Odisha, India, M.E and PhD from Indian Institute of Engineering, Science & Technology, Shibpur, India. He has over 6 years of teaching and research experience. Currently, he is working as an assistant professor in the mechanical engineering department of Sikkim Manipal Institute of Technology, Sikkim. He has published more than 35 SCI/Scopus indexed research articles. His areas of interests include thin-film coatings and its characterization, heat treatment, optimization of coatings and machining process parameters. He is on the editorial board of several peer-reviewed journals. He also serves as reviewer of many peer-reviewed journals. He has given several expert talks in many conference and workshop as a resource person.
Xiao-Zhi Gao is an esteemed academic with an extensive background in technology and computing. He commenced his academic journey at Harbin Institute of Technology, China, where he earned both his B.Sc. and M.Sc. degrees. Dr. Gao further advanced his education at the Helsinki University of Technology, now known as Aalto University, Finland, where he obtained his Ph.D. degree in 1999. With over 22 years of experience in teaching and research, Dr. Gao has established himself as a leading figure in the field. Since 2018, he has been a Professor od Data Science at the University of Eastern Finland, Kuopio, Finland, where he continues to contribute significantly to the academic community. Dr. Gao's editorial roles are remarkable. He serves as chief editor, associate editor, and a member of the editorial board for several prominent soft-computing journals, including Swarm and Evolutionary Computation, Information Sciences, and Applied Soft Computing. His scholarly output is impressive, with over 500 technical papers published in refereed journals and international conferences, and more than 400 SCI/SCOPUS research articles to his name. In addition to his extensive list of articles, Dr. Gao has authored 2 books and edited 4 books for renowned publishers such as Springer and IGI Global. His research is particularly focused on nature-inspired computing methods, with applications spanning optimization, prediction, data mining, signal processing, control, and industrial electronics. This breadth of interest underscores his deep understanding and innovative approach to complex technological challenges. Dr. Gao's academic achievements are further highlighted by his impressive Google Scholar H-index of 44, reflecting the widespread influence and high citation rate of his work. His dedication to advancing the frontiers of knowledge in computing and technology makes him a vital asset to the global academic and scientific community. His ORCID is 0000-0002-0078-5675.
"About the title" may belong to another edition of this title.
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