TY  - JOUR
AU  - Marković, Gordana
AU  - Manojlović, Vaso
AU  - Ruzic, Jovana
AU  - Sokić, Miroslav
PY  - 2023
UR  - https://ritnms.itnms.ac.rs/handle/123456789/925
AB  - Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys with a low Young’s modulus, and without the presence of cytotoxic alloying elements. Machine learning was used with aim to analyze biocompatible titanium alloys and predict the composition of Ti alloys with a low Young’s modulus. A database was created using experimental data for alloy composition, Young’s modulus, and mechanical and thermal properties of biocompatible titanium alloys. The Extra Tree Regression model was built to predict the Young’s modulus of titanium alloys. By processing data of 246 alloys, the specific heat was discovered to be the most influential parameter that contributes to the lowering of the Young’s modulus of titanium alloys. Further, the Monte Carlo method was used to predict the composition of future alloys with the desired properties. Simulation results of ten million samples, with predefined conditions for obtaining titanium alloys with a Young’s modulus lower than 70 GPa, show that it is possible to obtain several multicomponent alloys, consisting of five main elements: titanium, zirconium, tin, manganese and niobium.
PB  - MDPI
T2  - Materials
T1  - Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning
IS  - 19
VL  - 16
DO  - 10.3390/ma16196355
ER  - 

@article{
author = "Marković, Gordana and Manojlović, Vaso and Ruzic, Jovana and Sokić, Miroslav",
year = "2023",
abstract = "Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys with a low Young’s modulus, and without the presence of cytotoxic alloying elements. Machine learning was used with aim to analyze biocompatible titanium alloys and predict the composition of Ti alloys with a low Young’s modulus. A database was created using experimental data for alloy composition, Young’s modulus, and mechanical and thermal properties of biocompatible titanium alloys. The Extra Tree Regression model was built to predict the Young’s modulus of titanium alloys. By processing data of 246 alloys, the specific heat was discovered to be the most influential parameter that contributes to the lowering of the Young’s modulus of titanium alloys. Further, the Monte Carlo method was used to predict the composition of future alloys with the desired properties. Simulation results of ten million samples, with predefined conditions for obtaining titanium alloys with a Young’s modulus lower than 70 GPa, show that it is possible to obtain several multicomponent alloys, consisting of five main elements: titanium, zirconium, tin, manganese and niobium.",
publisher = "MDPI",
journal = "Materials",
title = "Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning",
number = "19",
volume = "16",
doi = "10.3390/ma16196355"
}

Marković, G., Manojlović, V., Ruzic, J.,& Sokić, M.. (2023). Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning. in Materials
MDPI., 16(19).
https://doi.org/10.3390/ma16196355

Marković G, Manojlović V, Ruzic J, Sokić M. Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning. in Materials. 2023;16(19).
doi:10.3390/ma16196355 .

Marković, Gordana, Manojlović, Vaso, Ruzic, Jovana, Sokić, Miroslav, "Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning" in Materials, 16, no. 19 (2023),
https://doi.org/10.3390/ma16196355 . .