TY  - CONF
AU  - Milojkov, Dušan
AU  - Marković, Gordana
AU  - Sokić, Miroslav
AU  - Manojlović, Vaso
AU  - Mutavdžić, Dragosav
AU  - Janjić, Goran
PY  - 2023
UR  - https://ritnms.itnms.ac.rs/handle/123456789/933
AB  - Fluorapatite (FAP) crystals have drawn significant interest over the last few decades as important hosts matrix for optically active trivalent rare earth ions, due to the strong crystal field splitting and large transition cross-sections. Nano-sized FAP particles doped with rare earth ions have been extensively studied as luminescent materials for biomedical applications for cell labeling and bioimaging, as well as antimicrobial agents for therapeutics.
Fluorapatite nanoparticles doped with praseodymium ions (Pr3+) were prepared by the co precipitation method and characterized. The different number of Pr3+ (4f2) transitions in the ultraviolet and visible parts of the spectrum was investigated by photoluminescence spectroscopy. Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) analyses of fluorescence spectra and ab initio calculation indicated that Pr3+ ions are 
preferentially substituted Ca2 (6h) sites in FAP lattice. In addition to the substitution of cations, there is also the substitution of anionic species such as OH-, CO32-, and NO3-, which are confirmed by the CHNS method. The obtained samples were tested as bioimaging and 
antibacterial agents and can potentially be used for further biomedical research.
PB  - Belgrade : Serbian Ceramic Society
C3  - The Eleventh Serbian Ceramic Society Conference »ADVANCED CERAMICS AND APPLICATION XI«"
T1  - Luminescence transitions of Pr3+ (4f2) in fluorapatite nanocrystals for  potential biomedical application
EP  - 39
SP  - 38
ER  - 

@conference{
author = "Milojkov, Dušan and Marković, Gordana and Sokić, Miroslav and Manojlović, Vaso and Mutavdžić, Dragosav and Janjić, Goran",
year = "2023",
abstract = "Fluorapatite (FAP) crystals have drawn significant interest over the last few decades as important hosts matrix for optically active trivalent rare earth ions, due to the strong crystal field splitting and large transition cross-sections. Nano-sized FAP particles doped with rare earth ions have been extensively studied as luminescent materials for biomedical applications for cell labeling and bioimaging, as well as antimicrobial agents for therapeutics.
Fluorapatite nanoparticles doped with praseodymium ions (Pr3+) were prepared by the co precipitation method and characterized. The different number of Pr3+ (4f2) transitions in the ultraviolet and visible parts of the spectrum was investigated by photoluminescence spectroscopy. Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) analyses of fluorescence spectra and ab initio calculation indicated that Pr3+ ions are 
preferentially substituted Ca2 (6h) sites in FAP lattice. In addition to the substitution of cations, there is also the substitution of anionic species such as OH-, CO32-, and NO3-, which are confirmed by the CHNS method. The obtained samples were tested as bioimaging and 
antibacterial agents and can potentially be used for further biomedical research.",
publisher = "Belgrade : Serbian Ceramic Society",
journal = "The Eleventh Serbian Ceramic Society Conference »ADVANCED CERAMICS AND APPLICATION XI«"",
title = "Luminescence transitions of Pr3+ (4f2) in fluorapatite nanocrystals for  potential biomedical application",
pages = "39-38"
}

Milojkov, D., Marković, G., Sokić, M., Manojlović, V., Mutavdžić, D.,& Janjić, G.. (2023). Luminescence transitions of Pr3+ (4f2) in fluorapatite nanocrystals for  potential biomedical application. in The Eleventh Serbian Ceramic Society Conference »ADVANCED CERAMICS AND APPLICATION XI«"
Belgrade : Serbian Ceramic Society., 38-39.

Milojkov D, Marković G, Sokić M, Manojlović V, Mutavdžić D, Janjić G. Luminescence transitions of Pr3+ (4f2) in fluorapatite nanocrystals for  potential biomedical application. in The Eleventh Serbian Ceramic Society Conference »ADVANCED CERAMICS AND APPLICATION XI«". 2023;:38-39..

Milojkov, Dušan, Marković, Gordana, Sokić, Miroslav, Manojlović, Vaso, Mutavdžić, Dragosav, Janjić, Goran, "Luminescence transitions of Pr3+ (4f2) in fluorapatite nanocrystals for  potential biomedical application" in The Eleventh Serbian Ceramic Society Conference »ADVANCED CERAMICS AND APPLICATION XI«" (2023):38-39.

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  - 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 . .

TY  - JOUR
AU  - Manojlović, Vaso
AU  - Marković, Gordana
PY  - 2023
UR  - https://ritnms.itnms.ac.rs/handle/123456789/659
AB  - Titanium alloys are widely applied, particularly in biomedical engineering, due to their
exceptional combination of mechanical strength, corrosion resistance, and biocompatibility.
The low modulus of elasticity of these titanium alloys in comparison to other materials used
in medical applications is a main characteristic. However, some of these alloys’ components,
such as aluminum and vanadium, can have adverse effects on the human body. Consequently,
new titanium alloys with low modulus of elasticity and no toxic alloying elements are currently
being developed. In this research, 238 titanium alloys were collected, almost entirely composed
of biocompatible alloying elements. The primary motivation behind creating such a database
is to establish a foundation for designing new alloys using machine learning methods. The
database can assist researchers, engineers, and biomedical professionals in developing
titanium alloys for various medical applications, thereby improving health outcomes and
driving advancements in biomaterials and biomedical engineering.
PB  - Belgrade : Association of Metallurgical Engineers of Serbia
T2  - Metallurgical and Materials Data
T1  - Titanium alloys database for medical applications
EP  - 6
IS  - 1
SP  - 1
VL  - 1
DO  - 10.30544/MMD5
ER  - 

@article{
author = "Manojlović, Vaso and Marković, Gordana",
year = "2023",
abstract = "Titanium alloys are widely applied, particularly in biomedical engineering, due to their
exceptional combination of mechanical strength, corrosion resistance, and biocompatibility.
The low modulus of elasticity of these titanium alloys in comparison to other materials used
in medical applications is a main characteristic. However, some of these alloys’ components,
such as aluminum and vanadium, can have adverse effects on the human body. Consequently,
new titanium alloys with low modulus of elasticity and no toxic alloying elements are currently
being developed. In this research, 238 titanium alloys were collected, almost entirely composed
of biocompatible alloying elements. The primary motivation behind creating such a database
is to establish a foundation for designing new alloys using machine learning methods. The
database can assist researchers, engineers, and biomedical professionals in developing
titanium alloys for various medical applications, thereby improving health outcomes and
driving advancements in biomaterials and biomedical engineering.",
publisher = "Belgrade : Association of Metallurgical Engineers of Serbia",
journal = "Metallurgical and Materials Data",
title = "Titanium alloys database for medical applications",
pages = "6-1",
number = "1",
volume = "1",
doi = "10.30544/MMD5"
}

Manojlović, V.,& Marković, G.. (2023). Titanium alloys database for medical applications. in Metallurgical and Materials Data
Belgrade : Association of Metallurgical Engineers of Serbia., 1(1), 1-6.
https://doi.org/10.30544/MMD5

Manojlović V, Marković G. Titanium alloys database for medical applications. in Metallurgical and Materials Data. 2023;1(1):1-6.
doi:10.30544/MMD5 .

Manojlović, Vaso, Marković, Gordana, "Titanium alloys database for medical applications" in Metallurgical and Materials Data, 1, no. 1 (2023):1-6,
https://doi.org/10.30544/MMD5 . .

TY  - CONF
AU  - Manojlović, Vaso
AU  - Marković, Gordana
PY  - 2023
UR  - https://ritnms.itnms.ac.rs/handle/123456789/926
AB  - Titanium and its various alloys have been used for decades as for numerous dental and orthopedic devices. What makes it suitable for these applications is the excellent combination of biocompatibility, corrosion resistance, low modulus of elasticity and specific strenght. However, recent reasrches have linked some of the main alloying elements, aluminium and vanadium, and several other elements besides them, with a very harmful effect on human body. Stress shielding is another possible side effect due to the still insufficiently matched elastic modulus of the alloy and bone. These issues have demanded the exploration for alternative alloys, characterized by non-toxic components and low elastic modulus. The design of titanium alloys involves a variety of tehniques, such as Mo equvivalent method, the electron-to-stom ratio (e/a), d electron based alloy design, experimental tehniques, and cutting-edge machine learning approches. The study leverges the Extra Tree Regression from machine learning to analize the most influential parameters for the elastic modulus, identifying the specific heat and shear of the silicon in alloy as significant factors. Multi-component diagrams were subsequently constructed to guide the development of alloys with a low elastic modulus. Also, employing the development model with the Monte Carlo experimental design method we found optimal compositions for high entropy alloys with a low Young’s modulus. These finding provide a solid soundation for future experimental studies on biocompatible titanium alloys.
PB  - Kosovska Mitrovica : Fakultet Tehničkih nauka
C3  - Jesenji simpozijum o termodinamici i faznim dijagramima
T1  - Designing biocompatible titanium alloys: machine learning approach
EP  - 12
SP  - 11
ER  - 

@conference{
author = "Manojlović, Vaso and Marković, Gordana",
year = "2023",
abstract = "Titanium and its various alloys have been used for decades as for numerous dental and orthopedic devices. What makes it suitable for these applications is the excellent combination of biocompatibility, corrosion resistance, low modulus of elasticity and specific strenght. However, recent reasrches have linked some of the main alloying elements, aluminium and vanadium, and several other elements besides them, with a very harmful effect on human body. Stress shielding is another possible side effect due to the still insufficiently matched elastic modulus of the alloy and bone. These issues have demanded the exploration for alternative alloys, characterized by non-toxic components and low elastic modulus. The design of titanium alloys involves a variety of tehniques, such as Mo equvivalent method, the electron-to-stom ratio (e/a), d electron based alloy design, experimental tehniques, and cutting-edge machine learning approches. The study leverges the Extra Tree Regression from machine learning to analize the most influential parameters for the elastic modulus, identifying the specific heat and shear of the silicon in alloy as significant factors. Multi-component diagrams were subsequently constructed to guide the development of alloys with a low elastic modulus. Also, employing the development model with the Monte Carlo experimental design method we found optimal compositions for high entropy alloys with a low Young’s modulus. These finding provide a solid soundation for future experimental studies on biocompatible titanium alloys.",
publisher = "Kosovska Mitrovica : Fakultet Tehničkih nauka",
journal = "Jesenji simpozijum o termodinamici i faznim dijagramima",
title = "Designing biocompatible titanium alloys: machine learning approach",
pages = "12-11"
}

Manojlović, V.,& Marković, G.. (2023). Designing biocompatible titanium alloys: machine learning approach. in Jesenji simpozijum o termodinamici i faznim dijagramima
Kosovska Mitrovica : Fakultet Tehničkih nauka., 11-12.

Manojlović V, Marković G. Designing biocompatible titanium alloys: machine learning approach. in Jesenji simpozijum o termodinamici i faznim dijagramima. 2023;:11-12..

Manojlović, Vaso, Marković, Gordana, "Designing biocompatible titanium alloys: machine learning approach" in Jesenji simpozijum o termodinamici i faznim dijagramima (2023):11-12.

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.

TY  - CONF
AU  - Patarić, Aleksandra
AU  - Marković, Gordana
AU  - Đorđević, Nataša
AU  - Mihajlović, Slavica
AU  - Mihailović, Marija
PY  - 2023
UR  - https://ritnms.itnms.ac.rs/handle/123456789/924
AB  - Cobalt–chromium–molybdenum (CoCrMo) alloys are known for medical use due to their biocompatibility, corrosion and 
wear resistance. The chemical and phase composition, as well as microstructure of the alloy directly affect the mechanical  properties. In this investigation, CoCrMo alloy samples were obtained by vacuum precise casting. The procedure of  melting and casting process as well as their parameters are given. Molds fabricated of copper, gray iron, steel, ceramics  and graphite were used during the casting process. In this way, the cooling rate influence on the obtained microstructure  was examined. Besides, different casting temperatures (1400°C, 1450°C and 1500°C) were applied for each kind of mold.  After metallographic preparation, the microstructure was examined on the cross section of samples by optical microscopy.  The obtained results show that by increasing the cooling rate, the microstructure of samples become finer and more  homogeneous.
PB  - Belgrade : Association of Metallurgical Engineers of Serbia
C3  - 5th Metallurgical & Materials Engineering Congress of South-East Europe
T1  - Microstructure assessment of co alloy intended for dentistry
EP  - 225
SP  - 221
ER  - 

@conference{
author = "Patarić, Aleksandra and Marković, Gordana and Đorđević, Nataša and Mihajlović, Slavica and Mihailović, Marija",
year = "2023",
abstract = "Cobalt–chromium–molybdenum (CoCrMo) alloys are known for medical use due to their biocompatibility, corrosion and 
wear resistance. The chemical and phase composition, as well as microstructure of the alloy directly affect the mechanical  properties. In this investigation, CoCrMo alloy samples were obtained by vacuum precise casting. The procedure of  melting and casting process as well as their parameters are given. Molds fabricated of copper, gray iron, steel, ceramics  and graphite were used during the casting process. In this way, the cooling rate influence on the obtained microstructure  was examined. Besides, different casting temperatures (1400°C, 1450°C and 1500°C) were applied for each kind of mold.  After metallographic preparation, the microstructure was examined on the cross section of samples by optical microscopy.  The obtained results show that by increasing the cooling rate, the microstructure of samples become finer and more  homogeneous.",
publisher = "Belgrade : Association of Metallurgical Engineers of Serbia",
journal = "5th Metallurgical & Materials Engineering Congress of South-East Europe",
title = "Microstructure assessment of co alloy intended for dentistry",
pages = "225-221"
}

Patarić, A., Marković, G., Đorđević, N., Mihajlović, S.,& Mihailović, M.. (2023). Microstructure assessment of co alloy intended for dentistry. in 5th Metallurgical & Materials Engineering Congress of South-East Europe
Belgrade : Association of Metallurgical Engineers of Serbia., 221-225.

Patarić A, Marković G, Đorđević N, Mihajlović S, Mihailović M. Microstructure assessment of co alloy intended for dentistry. in 5th Metallurgical & Materials Engineering Congress of South-East Europe. 2023;:221-225..

Patarić, Aleksandra, Marković, Gordana, Đorđević, Nataša, Mihajlović, Slavica, Mihailović, Marija, "Microstructure assessment of co alloy intended for dentistry" in 5th Metallurgical & Materials Engineering Congress of South-East Europe (2023):221-225.