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Advanced multiresponse process optim...

Sibalija, Tatjana V.

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Advanced multiresponse process optimisation = an intelligent and integrated approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced multiresponse process optimisation/ by Tatjana V. Sibalija, Vidosav D. Majstorovic.
Reminder of title:	an intelligent and integrated approach /
Author:	Sibalija, Tatjana V.
other author:	Majstorovic, Vidosav D.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xvii, 284 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Review of multiresponse optimisation approaches -- An intelligent, integrated, problem-independent method for multiresponse process optimisation -- Implementation of an intelligent, integrated, problem-independent method to multiresponse process optimisation -- Case studies -- Conclusion.
Contained By:	Springer eBooks
Subject:	Manufacturing processes. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-19255-0
ISBN:	9783319192550$q(electronic bk.)

Advanced multiresponse process optimisation = an intelligent and integrated approach /
Sibalija, Tatjana V.

Advanced multiresponse process optimisationan intelligent and integrated approach /[electronic resource] :by Tatjana V. Sibalija, Vidosav D. Majstorovic. - Cham :Springer International Publishing :2016. - xvii, 284 p. :ill. (some col.), digital ;24 cm.

Introduction -- Review of multiresponse optimisation approaches -- An intelligent, integrated, problem-independent method for multiresponse process optimisation -- Implementation of an intelligent, integrated, problem-independent method to multiresponse process optimisation -- Case studies -- Conclusion.

This book presents an intelligent, integrated, problem-independent method for multiresponse process optimization. In contrast to traditional approaches, the idea of this method is to provide a unique model for the optimization of various processes, without imposition of assumptions relating to the type of process, the type and number of process parameters and responses, or interdependences among them. The presented method for experimental design of processes with multiple correlated responses is composed of three modules: an expert system that selects the experimental plan based on the orthogonal arrays; the factor effects approach, which performs processing of experimental data based on Taguchi's quality loss function and multivariate statistical methods; and process modeling and optimization based on artificial neural networks and metaheuristic optimization algorithms. The implementation is demonstrated using four case studies relating to high-tech industries and advanced, non-conventional processes.

ISBN: 9783319192550$q(electronic bk.)

Standard No.: 10.1007/978-3-319-19255-0doiSubjects--Topical Terms:

613090
Manufacturing processes.

LC Class. No.: TS183

Dewey Class. No.: 670

Advanced multiresponse process optimisation = an intelligent and integrated approach /
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245 1 0 $a Advanced multiresponse process optimisation $h [electronic resource] : $b an intelligent and integrated approach / $c by Tatjana V. Sibalija, Vidosav D. Majstorovic.
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300 $a xvii, 284 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Review of multiresponse optimisation approaches -- An intelligent, integrated, problem-independent method for multiresponse process optimisation -- Implementation of an intelligent, integrated, problem-independent method to multiresponse process optimisation -- Case studies -- Conclusion.
520 $a This book presents an intelligent, integrated, problem-independent method for multiresponse process optimization. In contrast to traditional approaches, the idea of this method is to provide a unique model for the optimization of various processes, without imposition of assumptions relating to the type of process, the type and number of process parameters and responses, or interdependences among them. The presented method for experimental design of processes with multiple correlated responses is composed of three modules: an expert system that selects the experimental plan based on the orthogonal arrays; the factor effects approach, which performs processing of experimental data based on Taguchi's quality loss function and multivariate statistical methods; and process modeling and optimization based on artificial neural networks and metaheuristic optimization algorithms. The implementation is demonstrated using four case studies relating to high-tech industries and advanced, non-conventional processes.
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