Hybrid Modeling and Optimization of Manufacturing

Combining Artificial Intelligence and Finite Element Method

  • Ramón Quiza
  • Omar López-Armas
  • J. Paulo Davim

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Also part of the SpringerBriefs in Computational Mechanics book sub series (BRIEFSCOMPUTAT)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Ramón Quiza, Omar López-Armas, J. Paulo Davim
    Pages 1-11
  3. Ramón Quiza, Omar López-Armas, J. Paulo Davim
    Pages 13-37
  4. Ramón Quiza, Omar López-Armas, J. Paulo Davim
    Pages 39-77
  5. Ramón Quiza, Omar López-Armas, J. Paulo Davim
    Pages 79-91
  6. Back Matter
    Pages 93-95

About this book

Introduction

Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.

Keywords

AI tools optimization Bayesian probability Evolutionary algorithms FEM modeling Hybrid modeling Manufacturing processes

Authors and affiliations

  • Ramón Quiza
    • 1
  • Omar López-Armas
    • 2
  • J. Paulo Davim
    • 3
  1. 1., Department of Mechanical EngineeringUniversity of MatanzasMatanzasCuba
  2. 2., Department of Mechanical EngineeringUniversity of MatanzasMatanzasCuba
  3. 3., Department of Mechanical EngineeringUniversity of AveiroAveiroPortugal

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-28085-6
  • Copyright Information The Author(s) 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-28084-9
  • Online ISBN 978-3-642-28085-6
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • About this book
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