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Socio-inspired optimization methods ...

Shastri, Apoorva.

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Socio-inspired optimization methods for advanced manufacturing processes

Record Type:	Electronic resources : Monograph/item
Title/Author:	Socio-inspired optimization methods for advanced manufacturing processes/ by Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni.
Author:	Shastri, Apoorva.
other author:	Nargundkar, Aniket.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	x, 128 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Manufacturing processes - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-981-15-7797-0
ISBN:	9789811577970

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

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

920932
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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245 1 0 $a Socio-inspired optimization methods for advanced manufacturing processes $h [electronic resource] / $c by Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a x, 128 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in advanced manufacturing, $x 1860-5168
505 0 $a 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.
520 $a 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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650 0 $a Manufacturing processes $x Computer simulation. $3 841343
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650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Optimization. $3 891104
700 1 $a Nargundkar, Aniket. $3 3485933
700 1 $a Kulkarni, Anand J. $3 3295739
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830 0 $a Springer series in advanced manufacturing. $3 1566004
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