This book provides a detailed understanding of optimization methods as they are implemented in a variety of manufacturing, fabrication and machining processes. It covers the implementation of statistical methods, multi-criteria decision making methods and evolutionary techniques for single and multi-objective optimization to improve quality, productivity, and sustainability in manufacturing. It reports on the theoretical aspects, special features, recent research and latest development in the field.  Optimization of Manufacturing Processes is a valuable source of information for researchers and practitioners, as it fills the gap where no dedicated book is available on intelligent manufacturing/modeling and optimization in manufacturing. Readers will develop an understanding of the implementation of statistical and evolutionary techniques for modeling and optimization in manufacturing.
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This book provides a detailed understanding of optimization methods as they are implemented in a variety of manufacturing, fabrication and machining processes.
Part I: Intelligent Manufacturing Using Statistical Techniques.- Part II: Intelligent Manufacturing using Multi-criteria Decision Making.- Part III: Intelligent Manufacturing using Evolutionary Techniques.
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This book provides a detailed understanding of optimization methods as they are implemented in a variety of manufacturing, fabrication and machining processes. It covers the implementation of statistical methods, multi-criteria decision making methods and evolutionary techniques for single and multi-objective optimization to improve quality, productivity, and sustainability in manufacturing. It reports on the theoretical aspects, special features, recent research and latest development in the field. Intelligent Manufacturing is a valuable source of information for researchers and practitioners, as it fills the gap where no dedicated book is available on intelligent manufacturing/modeling and optimization in manufacturing. Readers will develop an understanding of the implementation of statistical and evolutionary techniques for modeling and optimization in manufacturing
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Details state-of-the-art optimization techniques and their application to manufacturing Covers statistical methods, multi-criteria decision making and evolutionary techniques Reports on theoretical aspects, recent applications of optimization techniques and the latest developments in the field
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Produktdetaljer

ISBN
9783030196400
Publisert
2020-08-14
Utgiver
Vendor
Springer Nature Switzerland AG
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet

Biographical note

Professor Kapil Gupta is an associate professor in the Dept. of Mechanical and Industrial Engineering Technology at the University of Johannesburg. He obtained PhD in mechanical engineering with specialization in Advanced Manufacturing from Indian Institute of Technology Indore, India in 2014. Advanced machining processes, sustainable manufacturing, precision engineering and gear technology are the areas of his interest and specialization. Dr. Gupta published several SCI Journal articles and delivered presentations in some International Conferences. He possesses over five years of professional experience in academic and research environment. Currently, he is doing research in advanced/modern machining,  sustainable manufacturing and gear engineering. He has a good experience in implementing various optimization techniques for single and multi-objective optimization tasks in manufacturing.

Dr Munish Kumar Gupta, is an assistant professor at Chandigarh University, Gharuan, Punjab-India. He obtained a PhD from National Institute of Technology Hamirpur India in 2018. His area of interests includes sustainable machining, optimization, intelligent machining, rapid prototyping and advanced optimization methods. He has published several research papers in national and international journals and conference proceedings. He has expertise in implementation of statistical and evolutionary optimization techniques in manufacturing.