Dental Materials
Volume 24, Issue 1 , Pages 18-27, January 2008

Estimation of chemical resistance of dental ceramics by neural network

  • Jasenka Živko-Babić

      Affiliations

    • Department of Prosthodontics, School of Dental Medicine, University of Zagreb, Gundulićeva 5, 10000 Zagreb, Croatia
    • ,
  • Dragutin Lisjak

      Affiliations

    • Department of Materials, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000 Zagreb, Croatia
    • ,
  • Lidija Ćurković

      Affiliations

    • Department of Materials, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000 Zagreb, Croatia
    • ,
  • Marko Jakovac

      Affiliations

    • Department of Prosthodontics, School of Dental Medicine, University of Zagreb, Gundulićeva 5, 10000 Zagreb, Croatia
    • Corresponding Author InformationCorresponding author. Tel.: +385 14802135; fax: +385 14802159.

Received 23 January 2006; received in revised form 11 January 2007; accepted 19 January 2007.

Abstract 

Objectives

The purpose of this research was to determine the mass concentrations of ions eluted from dental ceramic after an exposure to hydrochloric acid and, drawing on those results, to develop a feedforward backpropagation neural network (NN).

Materials and methods

Four dental ceramics were selected for this study. The experimental measurement was conducted after 1, 2, 3, 6 and 12 months of exposure to hydrochloric acid. The results of the 1, 2, 6 and 12 months of immersion were used for training a 13-13-5 model of NN. For evaluating NN efficiency, the regression analysis of input variables obtained by the experiment and output variables provided by the trained network was used.

Results

The measured data from the 3-month acid exposure and data obtained by the neural network estimation were compared.

High correlation coefficient (R) and low normalized root mean square error (NRMSE) between the measured and estimated output values were observed.

Conclusions

It could be concluded that the artificial neural network has a great potential as an additional method in investigating the properties of dental materials.

Keywords: Dental ceramics, Neural network, Chemical resistance

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PII: S0109-5641(07)00042-5

doi:10.1016/j.dental.2007.01.008

Dental Materials
Volume 24, Issue 1 , Pages 18-27, January 2008

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