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Prediction of Dental Milling Time-Error by Flexible Neural Trees and Fuzzy Rules

  • Pavel Krömer
  • Tomáš Novosád
  • Václav Snášel
  • Vicente Vera
  • Beatriz Hernando
  • Laura García-Hernandez
  • Héctor Quintián
  • Emilio Corchado
  • Raquel Redondo
  • Javier Sedano
  • Alvaro E. García
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7435)

Abstract

This multidisciplinary study presents the application of two soft computing methods utilizing the artificial evolution of symbolic structures – evolutionary fuzzy rules and flexible neural trees – for the prediction of dental milling time-error, i.e. the error between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.

Keywords

dental milling prediction evolutionary algorithms flexible neural trees fuzzy rules industrial applications 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pavel Krömer
    • 1
    • 2
  • Tomáš Novosád
    • 1
  • Václav Snášel
    • 1
    • 2
  • Vicente Vera
    • 4
  • Beatriz Hernando
    • 4
  • Laura García-Hernandez
    • 7
  • Héctor Quintián
    • 3
  • Emilio Corchado
    • 2
    • 3
  • Raquel Redondo
    • 5
  • Javier Sedano
    • 6
  • Alvaro E. García
    • 4
  1. 1.Dept. of Computer ScienceVŠB-Technical University of OstravaCzech Republic
  2. 2.IT4InnovationsOstravaCzech Republic
  3. 3.Departamento de Informática y AutomáticaUniversidad de SalamancaSpain
  4. 4.Facultad de OdontologíaUCMMadridSpain
  5. 5.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  6. 6.Dept. of AI & Applied ElectronicsCastilla y León Technological InstituteBurgosSpain
  7. 7.Area of Project EngineeringUniversity of CordobaSpain

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