TY  - CONF
AU  - Marković, Gordana
AU  - Manojlović, Vaso
AU  - Sokić, Miroslav
AU  - Ruzic, Jovana
AU  - Milojkov, Dušan
AU  - Patarić, Aleksandra
PY  - 2023
UR  - https://ritnms.itnms.ac.rs/handle/123456789/927
AB  - Titanium alloys are widely employed in various fields, particularly in biomedical engineering, due to their mechanical  and corrosion resistance properties combined with good biocompatibility. The modulus of elasticity is a distinguishing  feature of this group of materials compared to others used for similar purposes. A database of approximately 238 titanium alloys free of toxic elements was compiled for this study. The influence of different factors (such as alloy element  proportions, density, and specific heat) on the modulus of elasticity was predicted using four methods: support vector machine, XGBoost, Neural Network, and Random Forest. The Random Forest mean absolute error (MAE) of 7.33 GPa, falls within the range of experimentally obtained absolute errors in the literature (up to about 11 GPa). A strong correlation (R2 = 0.72) was observed between experimental and predicted data. Lastly, specific alloying element regions were identified for the modulus of elasticity, which can be used to design new biocompatible titanium alloys in the future.
PB  - Belgrade : Association of Metallurgical Engineers of Serbia
C3  - 5th Metallurgical & Materials Engineering Congress of South-East Europe
T1  - Predicting the modulus of elasticity of biocompatible titanium alloys using machine learning
EP  - 158
SP  - 154
ER  - 

@conference{
author = "Marković, Gordana and Manojlović, Vaso and Sokić, Miroslav and Ruzic, Jovana and Milojkov, Dušan and Patarić, Aleksandra",
year = "2023",
abstract = "Titanium alloys are widely employed in various fields, particularly in biomedical engineering, due to their mechanical  and corrosion resistance properties combined with good biocompatibility. The modulus of elasticity is a distinguishing  feature of this group of materials compared to others used for similar purposes. A database of approximately 238 titanium alloys free of toxic elements was compiled for this study. The influence of different factors (such as alloy element  proportions, density, and specific heat) on the modulus of elasticity was predicted using four methods: support vector machine, XGBoost, Neural Network, and Random Forest. The Random Forest mean absolute error (MAE) of 7.33 GPa, falls within the range of experimentally obtained absolute errors in the literature (up to about 11 GPa). A strong correlation (R2 = 0.72) was observed between experimental and predicted data. Lastly, specific alloying element regions were identified for the modulus of elasticity, which can be used to design new biocompatible titanium alloys in the future.",
publisher = "Belgrade : Association of Metallurgical Engineers of Serbia",
journal = "5th Metallurgical & Materials Engineering Congress of South-East Europe",
title = "Predicting the modulus of elasticity of biocompatible titanium alloys using machine learning",
pages = "158-154"
}

Marković, G., Manojlović, V., Sokić, M., Ruzic, J., Milojkov, D.,& Patarić, A.. (2023). Predicting the modulus of elasticity of biocompatible titanium alloys using machine learning. in 5th Metallurgical & Materials Engineering Congress of South-East Europe
Belgrade : Association of Metallurgical Engineers of Serbia., 154-158.

Marković G, Manojlović V, Sokić M, Ruzic J, Milojkov D, Patarić A. Predicting the modulus of elasticity of biocompatible titanium alloys using machine learning. in 5th Metallurgical & Materials Engineering Congress of South-East Europe. 2023;:154-158..

Marković, Gordana, Manojlović, Vaso, Sokić, Miroslav, Ruzic, Jovana, Milojkov, Dušan, Patarić, Aleksandra, "Predicting the modulus of elasticity of biocompatible titanium alloys using machine learning" in 5th Metallurgical & Materials Engineering Congress of South-East Europe (2023):154-158.

TY  - CONF
AU  - Marković, Gordana
AU  - Manojlović, Vaso
AU  - Sokić, Miroslav
AU  - Ruzic, Jovana
AU  - Milojkov, Dušan
PY  - 2023
UR  - https://ritnms.itnms.ac.rs/handle/123456789/1216
AB  - This study examines the potential of high-entropy alloys (HEAs) as promising biomaterials, with a specific
focus on the development of alloys with a low Young's modulus. Utilizing Monte Carlo simulations coupled
with machine learning techniques, the research identifies critical variables that significantly influence the
Young’s modulus, uncovering a notable correlation between specific heat and the elastic properties of the
alloys. The validation of the Extra Trees Regressor as a reliable predictive model in this study, furthermore,
facilitates the identification of promising HEAs with tailored properties. These findings provide significant
insights that are expected to guide future progresses in the development of HEAs as advanced biomaterials.
PB  - Bor : University of Belgrade, Technical Faculty in Bor
C3  - The 54th International October Conference on Mining and Metallurgy
T1  - Designing biocompatible high entropy alloys using Monte Carlo simulations
EP  - 530
SP  - 527
ER  - 

@conference{
author = "Marković, Gordana and Manojlović, Vaso and Sokić, Miroslav and Ruzic, Jovana and Milojkov, Dušan",
year = "2023",
abstract = "This study examines the potential of high-entropy alloys (HEAs) as promising biomaterials, with a specific
focus on the development of alloys with a low Young's modulus. Utilizing Monte Carlo simulations coupled
with machine learning techniques, the research identifies critical variables that significantly influence the
Young’s modulus, uncovering a notable correlation between specific heat and the elastic properties of the
alloys. The validation of the Extra Trees Regressor as a reliable predictive model in this study, furthermore,
facilitates the identification of promising HEAs with tailored properties. These findings provide significant
insights that are expected to guide future progresses in the development of HEAs as advanced biomaterials.",
publisher = "Bor : University of Belgrade, Technical Faculty in Bor",
journal = "The 54th International October Conference on Mining and Metallurgy",
title = "Designing biocompatible high entropy alloys using Monte Carlo simulations",
pages = "530-527"
}

Marković, G., Manojlović, V., Sokić, M., Ruzic, J.,& Milojkov, D.. (2023). Designing biocompatible high entropy alloys using Monte Carlo simulations. in The 54th International October Conference on Mining and Metallurgy
Bor : University of Belgrade, Technical Faculty in Bor., 527-530.

Marković G, Manojlović V, Sokić M, Ruzic J, Milojkov D. Designing biocompatible high entropy alloys using Monte Carlo simulations. in The 54th International October Conference on Mining and Metallurgy. 2023;:527-530..

Marković, Gordana, Manojlović, Vaso, Sokić, Miroslav, Ruzic, Jovana, Milojkov, Dušan, "Designing biocompatible high entropy alloys using Monte Carlo simulations" in The 54th International October Conference on Mining and Metallurgy (2023):527-530.

TY  - CONF
AU  - Marković, Gordana
AU  - Manojlović, Vaso
AU  - Ruzic, Jovana
AU  - Sokić, Miroslav
PY  - 2023
UR  - https://ritnms.itnms.ac.rs/handle/123456789/1169
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 of different composition, with lower modulus of elasticity, without the  presence of toxic alloying elements such as Al and V [1]. Traditional methods used to  detect dependencies between parameters, as well as alloy design, are often not  particularly effective and usually require large investments of time and resources. The study introduces the machine learning technique Extra Tree Regression, which, through  the analysis of data from 246 biocompatible titanium alloys, identifies factors associated with reduced elastic modulus [2]. The three most influential were: specific heat and mass fraction of titanium and mass fraction of titanium silicon. Using data on the most influential factors, four-component diagrams were designed where certain alloy compositions reach a modulus of up to 54 GPa. In addition, Monte Carlo simulations were used to demonstrate the feasibility of modeling multicomponent alloys with elastic modulus below 70 GPa.
PB  - Belgrade : Serbian Chemical Society
PB  - Belgrade : Serbian Young Chemists’ Club
C3  - 9th Conference of Young Chemists of Serbia
T1  - "Predicting the modulus of elasticity for biocompatible titanium  alloys"
EP  - 165
SP  - 165
ER  - 

@conference{
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 of different composition, with lower modulus of elasticity, without the  presence of toxic alloying elements such as Al and V [1]. Traditional methods used to  detect dependencies between parameters, as well as alloy design, are often not  particularly effective and usually require large investments of time and resources. The study introduces the machine learning technique Extra Tree Regression, which, through  the analysis of data from 246 biocompatible titanium alloys, identifies factors associated with reduced elastic modulus [2]. The three most influential were: specific heat and mass fraction of titanium and mass fraction of titanium silicon. Using data on the most influential factors, four-component diagrams were designed where certain alloy compositions reach a modulus of up to 54 GPa. In addition, Monte Carlo simulations were used to demonstrate the feasibility of modeling multicomponent alloys with elastic modulus below 70 GPa.",
publisher = "Belgrade : Serbian Chemical Society, Belgrade : Serbian Young Chemists’ Club",
journal = "9th Conference of Young Chemists of Serbia",
title = ""Predicting the modulus of elasticity for biocompatible titanium  alloys"",
pages = "165-165"
}

Marković, G., Manojlović, V., Ruzic, J.,& Sokić, M.. (2023). "Predicting the modulus of elasticity for biocompatible titanium  alloys". in 9th Conference of Young Chemists of Serbia
Belgrade : Serbian Chemical Society., 165-165.

Marković G, Manojlović V, Ruzic J, Sokić M. "Predicting the modulus of elasticity for biocompatible titanium  alloys". in 9th Conference of Young Chemists of Serbia. 2023;:165-165..

Marković, Gordana, Manojlović, Vaso, Ruzic, Jovana, Sokić, Miroslav, ""Predicting the modulus of elasticity for biocompatible titanium  alloys"" in 9th Conference of Young Chemists of Serbia (2023):165-165.