Use of Artificial Neural Network in Determination of Shade, Light Curing Unit, and Composite Parameters’ Effect on Bottom/Top Vickers Hardness Ratio of Composites

Objective. To assess the influence of light emitting diode (LED) and quartz tungsten halogen (QTH) light curing unit (LCU) on the bottom/top (B/T) Vickers Hardness Number (VHN) ratio of different composites with different shades and determination of the most significant effect on B/T VHN ratio of composites by shade, light curing unit, and composite parameters using artificial neural network. Method. Three composite resin materials [Clearfil Majesty Esthetic (CME), Tetric N Ceram (TNC), and Tetric Evo Ceram (TEC)] in different shades (HO, A2, B2, Bleach L, Bleach M) were used. The composites were polymerized with three different LED LCUs (Elipar S10, Bluephase 20i, Valo) and halogen LCU (Hilux). Vickers hardness measurements were made at a load of 100 g for 10 sec on the top and bottom surfaces and B/T VHN ratio calculated. The data were statistically analyzed with three-way ANOVA and Tukey test at a significance level of 0.05. The obtained measurements and data were then fed to a neural network to establish the correlation between the inputs and outputs. Results. There were no significant differences between the B/T VHN ratio of LCUs for the HO and B shades of CME (p>0.05), but there were significant differences between the B/T VHN ratio of LCUs for shade A2 (p0.05). For TEC, there was no significant difference between the B/T VHN ratio of halogen and LED LCUs (p>0.05), but a significant difference was determined among the LED LCUs (p

https://ift.tt/2QBGpFt