Advanced Multiresponse Process Optimisation

An Intelligent and Integrated Approach

Nonfiction, Science & Nature, Technology, Manufacturing, Computers, Advanced Computing, Artificial Intelligence
Cover of the book Advanced Multiresponse Process Optimisation by Tatjana V. Šibalija, Vidosav D. Majstorović, Springer International Publishing
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Author: Tatjana V. Šibalija, Vidosav D. Majstorović ISBN: 9783319192550
Publisher: Springer International Publishing Publication: July 25, 2015
Imprint: Springer Language: English
Author: Tatjana V. Šibalija, Vidosav D. Majstorović
ISBN: 9783319192550
Publisher: Springer International Publishing
Publication: July 25, 2015
Imprint: Springer
Language: English

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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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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