Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter

  • Pavel Krömer
  • Tomáš Novosád
  • Václav Snášel
  • Vicente Vera
  • Beatriz Hernando
  • Laura García-Hernández
  • Héctor Quintián
  • Emilio Corchado
  • Raquel Redondo
  • Javier Sedano
  • Alvaro E. García
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 188)

Abstract

This multidisciplinary study presents the application of two well known soft computing methods – flexible neural trees, and evolutionary fuzzy rules – for the prediction of the error parameter 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

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pavel Krömer
    • 1
    • 2
  • Tomáš Novosád
    • 1
  • Václav Snášel
    • 1
    • 2
  • Vicente Vera
    • 4
  • Beatriz Hernando
    • 4
  • Laura García-Hernández
    • 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 OstravaOstravaCzech Republic
  2. 2.IT4InnovationsVŠB-Technical University of OstravaOstravaCzech Republic
  3. 3.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain
  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 CordobaCordobaSpain

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