Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning
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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.
Keywords:
Extra Tree Regression / machine learning / Monte Carlo method / titanium alloys / Young’s modulusSource:
Materials, 2023, 16, 19, 6355-Publisher:
- MDPI
Funding / projects:
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200135 (University of Belgrade, Faculty of Technology and Metallurgy) (RS-MESTD-inst-2020-200135)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200023 (Institute of Technology of Nuclear and Other Mineral Row Materials - ITNMS, Belgrade) (RS-MESTD-inst-2020-200023)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200017 (University of Belgrade, Institute of Nuclear Sciences 'Vinča', Belgrade-Vinča) (RS-MESTD-inst-2020-200017)
Institution/Community
Tehnološko-metalurški fakultetTY - JOUR AU - Marković, Gordana AU - Manojlović, Vaso AU - Ružić, Jovana AU - Sokić, Miroslav PY - 2023 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/6729 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 SP - 6355 VL - 16 DO - 10.3390/ma16196355 ER -
@article{ author = "Marković, Gordana and Manojlović, Vaso and Ružić, 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", pages = "6355", volume = "16", doi = "10.3390/ma16196355" }
Marković, G., Manojlović, V., Ružić, J.,& Sokić, M.. (2023). Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning. in Materials MDPI., 16(19), 6355. https://doi.org/10.3390/ma16196355
Marković G, Manojlović V, Ružić J, Sokić M. Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning. in Materials. 2023;16(19):6355. doi:10.3390/ma16196355 .
Marković, Gordana, Manojlović, Vaso, Ružić, Jovana, Sokić, Miroslav, "Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning" in Materials, 16, no. 19 (2023):6355, https://doi.org/10.3390/ma16196355 . .