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Socio-Inspired Optimization Methods for Advanced Manufacturing Processes de Apoorva Shastri

Socio-Inspired Optimization Methods for Advanced Manufacturing Processes: Springer Series in Advanced Manufacturing

Autor Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
en Limba Engleză Paperback – 13 aug 2021
This book discusses comprehensively the advanced manufacturing processes, including illustrative examples of the processes, mathematical modeling, and the need to optimize associated parameter problems. In addition, it describes in detail the cohort intelligence methodology and its variants along with illustrations, to help readers gain a better understanding of the framework. The theoretical and statistical rigor is validated by comparing the solutions with evolutionary algorithms, simulation annealing, response surface methodology, the firefly algorithm, and experimental work. Lastly, the book critically reviews several socio-inspired optimization methods. 
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Specificații

ISBN-13: 9789811577994
ISBN-10: 9811577994
Ilustrații: X, 128 p. 45 illus., 22 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.2 kg
Ediția:1st ed. 2021
Editura: Springer Nature Singapore
Colecția Springer
Seria Springer Series in Advanced Manufacturing

Locul publicării:Singapore, Singapore

Cuprins

Introduction.- A Brief Review of Socio-Inspired Metaheuristics.- Multi Cohort Intelligence Algorithm.- Optimization of Electric Discharge Machining (EDM).- Optimization of Abrasive Water Jet Machining (AWJM).- Optimization of Micro Milling Process.- Optimization of Micro Drilling Process.- Optimization of Cutting Forces in Micro Drilling of CFRP Composites for Aerospace Applications.- Optimization of Micro Turning Process.- Optimization of Machining Process Parameters of Titanium Alloy Under (MQL) Environment.

Notă biografică

​Apoorva S Shastri holds a Master of Technology (M.Tech) in VLSI Design and Bachelor of Engineering in Electronics & Product Design Technology from R.T.M.N.U, Nagpur. She has also done Diploma from the Govt. Polytechnic, Nagpur. She worked as a guest faculty at Centre for Development of Advanced Computing (C-DAC), Pune. Currently, she is Assistant Professor at the Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune. She is also pursuing PhD in Optimization Algorithms and Applications from Symbiosis International (Deemed University). Her research interests include development of optimization algorithms, VLSI design, multi-objective optimization, continuous, discrete and combinatorial optimization, complex systems, probability collectives and self-organizing systems. Apoorva developed socio-inspired optimization methodologies such as Multi-Cohort Intelligence Algorithm and Expectation Algorithm. Apoorva has published several research papers in peer-reviewed journals, chapters and conferences.

Aniket Nargundkar holds a Master of Technology (MTech) in Manufacturing Technology from National Institute of Technology, Tiruchirappalli, India, and a Bachelor of Engineering from Shivaji University, India. He has worked as Manufacturing Technologist with Danfoss Industries Pvt Ltd, providing technological and process innovation solutions and executing it with an aim to improve market competitiveness and achieve operational excellence. He has worked in Denmark, Poland, and Mexico over a span of two years, together with professionals from Technology and Innovation, Lean Manufacturing, Production, Procurement & Quality in cross-functional teams, on Manufacturing, Supply Chain Problems & opportunities at Danfoss plants. Currently, he is an Assistant Professor at the Mechanical Engineering Department at Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU). He is also pursuing a PhD in Optimization Algorithms and Applications from SIU. His research interests include optimization algorithms and applications, multi-objective optimization, continuous, discrete and combinatorial optimization, multi-agent systems, complex systems, Manufacturing Processes and Technology, Supply Chain Analytics, Mechatronics, and Automation. Aniket has published numerous research papers in top quality peer-reviewed journals, chapters, and international conferences.

Anand J Kulkarni holds a PhD in Distributed Optimization from Nanyang Technological University, Singapore, MS in Artificial Intelligence from University of Regina, Canada, Bachelor of Engineering from Shivaji University, India and Diploma from the Board of Technical Education, Mumbai. He worked as a Research Fellow on a Cross-border Supply-chain Disruption project at Odette School of Business, University of Windsor, Canada. Anand was Head of the Mechanical Engineering Department at Symbiosis International (Deemed University) (SIU), Pune, India for three years. Currently, he is Associate Professor at the Symbiosis Center for Research and Innovation, SIU. His research interests include optimization algorithms, multi-objective optimization, continuous, discrete and combinatorial optimization, multi-agent systems, complex systems, probability collectives, swarm optimization, game theory, self-organizing systems and fault-tolerant systems. Anand pioneered socio-inspired optimization methodologies such as Cohort Intelligence, Ideology Algorithm, Expectation Algorithm, Socio Evolution & Learning Optimization algorithm. He is the founder and chairman of the Optimization and Agent Technology (OAT) Research Lab and has published over 50 research papers in peer-reviewed journals, chapters and conferences along with 3 authored and 5 edited books.

Textul de pe ultima copertă

This book discusses comprehensively the advanced manufacturing processes, including illustrative examples of the processes, mathematical modeling, and the need to optimize associated parameter problems. In addition, it describes in detail the cohort intelligence methodology and its variants along with illustrations, to help readers gain a better understanding of the framework. The theoretical and statistical rigor is validated by comparing the solutions with evolutionary algorithms, simulation annealing, response surface methodology, the firefly algorithm, and experimental work. Lastly, the book critically reviews several socio-inspired optimization methods. 

Caracteristici

Discusses several advanced manufacturing processes along with mathematical formulations, and includes numerous illustrations Offers solutions to the complex parameter optimization of these advanced manufacturing processes using several variants of AI-based, socio-cultural inspired methodology, referred to as cohort intelligence Explains variants of the cohort intelligence method and their mathematical formulation, and presents illustrative examples Describes the solutions using over 50 experimentally achieved plots, figures, and illustrations, along with over 25 tables