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.
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.