Dose and fading time estimation of glass ceramic by using artificial neural network method

Yıl 2021, Cilt: 12 Sayı: 1, 47 - 52, 13.01.2021

İbrahim Işık , Esme Işık , Hüseyin Toktamış

https://doi.org/10.24012/dumf.703171

Cited By: 2

Öz

Ceramic materials commonly used for dental prosthetics and restorations shows luminescent properties. Dental ceramics are considered the most natural-looking restorative materials for aesthetic rehabilitation due to their transparency. They are commonly used for dose response and fading assessment by using thermoluminescence method in various fields of dosimetric applications. In present study, we use artificial neural networks (ANN) toolbox of Matlab to predict irradiation dose and fading time using glow curve data from dental glass ceramic which is thermoluminescent (TL) dosimetric material. Temperature, dose value and fading time are used for input and TL intensity used for output component of the proposed ANN model. 18 neurons are used for hidden layer to analyze the experimental results of the model. Experimental and simulation results are compared and similarity is found as about 99 % in this present study.

Anahtar Kelimeler

Thermoluminescence, Artificial Neural Network, Glow curve, fading

Kaynakça

• 1. E. Isik and H. Toktamis, “TLD characteristic of glass, feldspathic and lithium disilicate ceramics,” Luminescence, vol. 34, no. 2, pp. 272–279, 2019, doi: 10.1002/bio.3605.
• 2. E. Işik, H. Toktamiş, and İ. Işik, “Analysis of thermoluminescence characteristics of a lithium disilicate glass ceramic using a nonlinear autoregressive with exogenous input model,” Luminescence, no. November 2019, pp. 1–8, 2020, doi: 10.1002/bio.3788.
• 3. D. Banerjee, L. Bùtter-jensen, and A. S. Murray, “Retrospective dosimetry : estimation of the dose to quartz using the single-aliquot regenerative-dose protocol,” vol. 52, pp. 831–844, 2000.
• 4. H. Oks et al., “Assessment of thermoluminescence peaks in porcelain for use in retrospective dosimetry,” Radiat. Meas., vol. 46, no. 12, pp. 1873–1877, 2011, doi: 10.1016/j.radmeas.2011.06.067.
• 5. I. Veronese, A. Galli, M. C. Cantone, M. Martini, F. Vernizzi, and G. Guzzi, “Study of TSL and OSL properties of dental ceramics for accidental dosimetry applications,” Radiat. Meas., vol. 45, no. 1, pp. 35–41, 2010, doi: 10.1016/j.radmeas.2009.11.005.
• 6. I. K. Bailiff and S. Road, “The use of luminescence techniques with ceramic materials for retrospective dosimetry,” pp. 985–994.
• 7. W. Höland, V. Rheinberger, M. Schweiger, K. F. Kelton, and B. R. Haywood, “Control of nucleation in glass ceramics,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., vol. 361, no. 1804, pp. 575–589, 2003, doi: 10.1098/rsta.2002.1152.
• 8. N. Kristianpoller, D. Weiss, and R. Chen, “Optical and dosimetric properties of zircon,” Radiat. Prot. Dosimetry, vol. 119, no. 1–4, pp. 267–270, 2006, doi: 10.1093/rpd/nci570.
• 9. D. Ekendahl, L. Judas, and L. Sukupova, “OSL and TL retrospective dosimetry with a fl uorapatite glass-ceramic used for dental restorations,” Radiat. Meas., vol. 58, pp. 138–144, 2013, doi: 10.1016/j.radmeas.2013.01.001.
• 10. Y. Go, S. Hu, and S. Road, “LUMINESCENCE CHARACTERISTICS OF DENTAL CERAMICS FOR RETROSPECTIVE DOSIMETRY :,” vol. 101, pp. 519–524, 2002.
• 11. A. Pascu, A. Timar-Gabor, and V. Simon, “Retrospective accident dosimetry using dental ceramics,” Rom. Reports Phys., vol. 68, no. November, pp. 658–666, 2015.
• 12. I. Veronese, G. Guzzi, A. Giussani, M. C. Cantone, and D. Ripamonti, “Determination of dose rates from natural radionuclides in dental materials,” J. Environ. Radioact., vol. 91, no. 1–2, pp. 15–26, 2006, doi: 10.1016/j.jenvrad.2006.08.002.
• 13. R. K. Tamrakar, D. P. Bisen, K. Upadhyay, and I. P. Sahu, “Comparative study of thermoluminescence behaviour of Gd2O3phosphor synthesized by solid state reaction and combustion method with different exposure,” Radiat. Meas., vol. 84, pp. 41–54, 2016, doi: 10.1016/j.radmeas.2015.11.006.
• 14. H. Isik, Esme; Toktamis, “Influence of storage in different ambiences on the thermoluminescence peaks of quartz ????,” Cienc. e Tec. vitinicola, vol. 33, pp. 2–16, 2018.
• 15. F. Rosenblatt, “Perceptrons and the Theory of Brain Mechanics,” Cornell Aeronaut. LAB INC BUFFALO NY., vol. VG-1196-G, p. 621, 1961.
• 16 B. N. Network, R. Hecht-nielsen, S. Diego, and L. Jolla, “the Backpropagation Neural Network,” Processing, pp. 593–605.
• 17 M. Y. Rafiq, G. Bugmann, and D. J. Easterbrook, “Neural network design for engineering applications,” Comput. Struct., vol. 79, no. 17, pp. 1541–1552, 2001, doi: 10.1016/S0045-7949(01)00039-6.
• 18 I. B. Topçu and M. Saridemir, “Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic,” Comput. Mater. Sci., vol. 41, no. 3, pp. 305–311, 2008, doi: 10.1016/j.commatsci.2007.04.009.
• 19 N. Kucuk and I. Kucuk, “Computational modeling of thermoluminescence glow curves of zinc borate crystals,” J. Inequalities Appl., vol. 2013, pp. 1–7, 2013, doi: 10.1186/1029-242X-2013-136.