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Advanced Multiresponse Process Optimisation

An Intelligent and Integrated Approach

  • Book
  • © 2016

Overview

Authors:
  1. Tatjana V. Šibalija

    1. Faculty of Information Technology, Metropolitan University, Belgrade, Serbia

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  2. Vidosav D. Majstorović

    1. Faculty of Mechanical Engineering, University of Belgrade, Belgrade, Serbia

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    You can also search for this author in PubMed   Google Scholar

  • Offers a comprehensive model for multiresponse optimization of complex industrial processes based on artificial intelligence techniques

  • Presents instructive case studies relating to high-tech industries and advanced, non-conventional processes

  • Highly relevant to contemporary research directions in the manufacturing domain

  • Includes supplementary material: sn.pub/extras

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Introduction

    • Tatjana V. Šibalija, Vidosav D. Majstorović
    Pages 1-20

  3. Review of Multiresponse Process Optimisation Methods

    • Tatjana V. Šibalija, Vidosav D. Majstorović
    Pages 21-63

  4. An Intelligent, Integrated, Problem-Independent Method for Multiresponse Process Optimisation

    • Tatjana V. Šibalija, Vidosav D. Majstorović
    Pages 65-164

  5. Implementation of the Intelligent Method for Multiresponse Process Optimisation

    • Tatjana V. Šibalija, Vidosav D. Majstorović
    Pages 165-260

  6. Discussion and Future Research

    • Tatjana V. Šibalija, Vidosav D. Majstorović
    Pages 261-283

  7. Concluding Remarks

    • Tatjana V. Šibalija, Vidosav D. Majstorović
    Pages 285-289

  8. Back Matter

    Pages 291-298
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Keywords

About this book

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.

Authors and Affiliations

  • Faculty of Information Technology, Metropolitan University, Belgrade, Serbia

    Tatjana V. Šibalija

  • Faculty of Mechanical Engineering, University of Belgrade, Belgrade, Serbia

    Vidosav D. Majstorović

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