國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Advanced multiresponse process optim...

Majstorovic, Vidosav D.

Advanced multiresponse process optimisationan intelligent and integrated approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced multiresponse process optimisationby Tatjana V. Sibalija, Vidosav D. Majstorovic.
其他題名:	an intelligent and integrated approach /
作者:	Sibalija, Tatjana V.
其他作者:	Majstorovic, Vidosav D.
出版者:	Cham :Springer International Publishing :2016.
面頁冊數:	xvii, 284 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Manufacturing processes.
電子資源:	http://dx.doi.org/10.1007/978-3-319-19255-0
ISBN:	9783319192550$q(electronic bk.)

Advanced multiresponse process optimisationan 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:

185481
Manufacturing processes.

LC Class. No.: TS183

Dewey Class. No.: 670

Advanced multiresponse process optimisationan intelligent and integrated approach /
LDR:02311nmm a2200313 a 4500 001 480956
003 DE-He213
005 20160712170843.0
006 m d
007 cr nn 008maaau
008 161007s2016 gw s 0 eng d
020 $a 9783319192550$q(electronic bk.)
020 $a 9783319192543$q(paper)
024 7 $a 10.1007/978-3-319-19255-0 $2 doi
035 $a 978-3-319-19255-0
040 $a GP $c GP
041 0 $a eng
050 4 $a TS183
072 7 $a TGXT $2 bicssc
072 7 $a TEC020000 $2 bisacsh
082 0 4 $a 670 $2 23
090 $a TS183 $b .S563 2016
100 1 $a Sibalija, Tatjana V. $3 736664
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.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
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.
650 0 $a Manufacturing processes. $3 185481
650 0 $a Sustainable engineering. $3 266168
650 1 4 $a Engineering. $3 210888
650 2 4 $a Manufacturing, Machines, Tools. $3 273747
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Robotics and Automation. $3 357111
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Operation Research/Decision Theory. $3 585050
700 1 $a Majstorovic, Vidosav D. $3 736665
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-19255-0
950 $a Engineering (Springer-11647)