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Computational Methods for Application in Industry 4.0

  • Nikolaos E. Karkalos
  • Angelos P. Markopoulos
  • J. Paulo Davim

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

Also part of the Manufacturing and Surface Engineering book sub series (BRIEFSMANUFACT)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Nikolaos E. Karkalos, Angelos P. Markopoulos, J. Paulo Davim
    Pages 1-10
  3. Nikolaos E. Karkalos, Angelos P. Markopoulos, J. Paulo Davim
    Pages 11-31
  4. Nikolaos E. Karkalos, Angelos P. Markopoulos, J. Paulo Davim
    Pages 33-55
  5. Nikolaos E. Karkalos, Angelos P. Markopoulos, J. Paulo Davim
    Pages 57-67

About this book

Introduction

This book presents computational and statistical methods used by intelligent systems within the concept of Industry 4.0. The methods include among others evolution-based and swarm intelligence-based methods. Each method is explained in its fundamental aspects, while some notable bibliography is provided for further reading. This book describes each methods' principles and compares them. It is intended for researchers who are new in computational and statistical methods but also to experienced users.

Keywords

Optimization Methods Evolutionary Algorithmas Swarm Intelligence Simulated Annealing Statistical Methods Manufacturing Optimization

Authors and affiliations

  • Nikolaos E. Karkalos
    • 1
  • Angelos P. Markopoulos
    • 2
  • J. Paulo Davim
    • 3
  1. 1.Laboratory of Manufacturing Technology, School of Mechanical EngineeringNational Technical University of AthensAthensGreece
  2. 2.Laboratory of Manufacturing Technology, School of Mechanical EngineeringNational Technical University of AthensAthensGreece
  3. 3.Department of Mechanical EngineeringUniversity of AveiroAveiroPortugal

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-92393-2
  • Copyright Information The Author(s) 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-92392-5
  • Online ISBN 978-3-319-92393-2
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • Buy this book on publisher's site