Estimation of chemical resistance of dental ceramics by neural network.
OBJECTIVE
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).


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.
Zivko-Babić
Jasenka
J
Department of Prosthodontics, School of Dental Medicine, University of Zagreb, Gundulićeva 5, Zagreb, Croatia.
Lisjak
Dragutin
D
Curković
Lidija
L
Jakovac
Marko
M
D000465:Algorithms; D003776:Dental Porcelain / Q000737:chemistry*; D003799:Dental Stress Analysis / Q000379:methods; D006851:Hydrochloric Acid; D007477:Ions; D008422:Materials Testing / Q000379:methods; D008956:Models, Chemical; D016571:Neural Networks, Computer; D012044:Regression Analysis
D007477:Ions; D003776:Dental Porcelain; D006851:Hydrochloric Acid