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Socio-inspired optimization methods ...

Kulkarni, Anand J.

Socio-inspired optimization methods for advanced manufacturing processes

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Socio-inspired optimization methods for advanced manufacturing processesby Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni.
作者:	Shastri, Apoorva.
其他作者:	Nargundkar, Aniket.
出版者:	Singapore :Springer Singapore :2021.
面頁冊數:	x, 128 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Manufacturing processesMathematical models.
電子資源:	https://doi.org/10.1007/978-981-15-7797-0
ISBN:	9789811577970$q(electronic bk.)

Socio-inspired optimization methods for advanced manufacturing processes
Shastri, Apoorva.

Socio-inspired optimization methods for advanced manufacturing processes[electronic resource] /by Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni. - Singapore :Springer Singapore :2021. - x, 128 p. :ill., digital ;24 cm. - Springer series in advanced manufacturing,1860-5168. - Springer series in advanced manufacturing..

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.

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.

ISBN: 9789811577970$q(electronic bk.)

Standard No.: 10.1007/978-981-15-7797-0doiSubjects--Topical Terms:

225386
Manufacturing processes
--Mathematical models.

LC Class. No.: TS183 / .S43 2021

Dewey Class. No.: 670.15118

Socio-inspired optimization methods for advanced manufacturing processes
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