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  - Ivanović, Jelena
AU  - Manojlović, Vaso
AU  - Sokić, Miroslav
PY  - 2023
UR  - https://ritnms.itnms.ac.rs/handle/123456789/1198
AB  - Користећи принципе Индустрије 4.0 и циркуларне економије, у овој студији
користи се метода машинског учења код процеса топљења челичног отпада у еле-
ктролучним пећима ради одрживе производње челика. Фокусира се на балансирање
материјалне и енергетске ефикасности, посебно на управљање деградације елемената
као што су манган и силицијум. Поред тога, овај приступ ублажава ограничења
рециклирања ефективним смањењем акумулације бакра и калаја у крајњем производу,
чиме се побољшава његов укупни квалитет. На тај начин, не само да се оптимизује
ефикасност процеса, већ се доприноси и смањењу угљеничног отиска индустрије че-
лика, усклађујући се са глобалним тежњама ка декарбонизацији и унапређењу одрживе
производне праксе.
AB  - Employing Industry 4.0 and circular economy principles, this study leverages
machine learning in Electric Arc Furnaces steel waste recycling to enhance sustainable steel
production. It focuses on balancing material and energy efficiency, particularly managing
degradation elements like Mn and Si. In addition, the approach mitigates recycling limitations
by effectively reducing the accumulation of Cu and Sn in the end product, thus enhancing its
overall quality. This approach not only optimizes the process efficiency but also contributes
to the reduction of the steel industry's carbon footprint, aligning with global decarbonization
efforts and advancing sustainable manufacturing practices.
PB  - Београд : Савез инжењера и техничара Србије
C3  - ИНДУСТРИЈА 4.0 У ЦИРКУЛАРНОЈ ЕКОНОМИЈИ И ЗАШТИТИ И ОПОРАВКУ ЖИВОТНЕ СРЕДИНЕ
T1  - Одржива производња у електрoлучним пећима користећи принципе индустрије 4.0
EP  - 109
SP  - 102
ER  - 

@conference{
author = "Ivanović, Jelena and Manojlović, Vaso and Sokić, Miroslav",
year = "2023",
abstract = "Користећи принципе Индустрије 4.0 и циркуларне економије, у овој студији
користи се метода машинског учења код процеса топљења челичног отпада у еле-
ктролучним пећима ради одрживе производње челика. Фокусира се на балансирање
материјалне и енергетске ефикасности, посебно на управљање деградације елемената
као што су манган и силицијум. Поред тога, овај приступ ублажава ограничења
рециклирања ефективним смањењем акумулације бакра и калаја у крајњем производу,
чиме се побољшава његов укупни квалитет. На тај начин, не само да се оптимизује
ефикасност процеса, већ се доприноси и смањењу угљеничног отиска индустрије че-
лика, усклађујући се са глобалним тежњама ка декарбонизацији и унапређењу одрживе
производне праксе., Employing Industry 4.0 and circular economy principles, this study leverages
machine learning in Electric Arc Furnaces steel waste recycling to enhance sustainable steel
production. It focuses on balancing material and energy efficiency, particularly managing
degradation elements like Mn and Si. In addition, the approach mitigates recycling limitations
by effectively reducing the accumulation of Cu and Sn in the end product, thus enhancing its
overall quality. This approach not only optimizes the process efficiency but also contributes
to the reduction of the steel industry's carbon footprint, aligning with global decarbonization
efforts and advancing sustainable manufacturing practices.",
publisher = "Београд : Савез инжењера и техничара Србије",
journal = "ИНДУСТРИЈА 4.0 У ЦИРКУЛАРНОЈ ЕКОНОМИЈИ И ЗАШТИТИ И ОПОРАВКУ ЖИВОТНЕ СРЕДИНЕ",
title = "Одржива производња у електрoлучним пећима користећи принципе индустрије 4.0",
pages = "109-102"
}

Ivanović, J., Manojlović, V.,& Sokić, M.. (2023). Одржива производња у електрoлучним пећима користећи принципе индустрије 4.0. in ИНДУСТРИЈА 4.0 У ЦИРКУЛАРНОЈ ЕКОНОМИЈИ И ЗАШТИТИ И ОПОРАВКУ ЖИВОТНЕ СРЕДИНЕ
Београд : Савез инжењера и техничара Србије., 102-109.

Ivanović J, Manojlović V, Sokić M. Одржива производња у електрoлучним пећима користећи принципе индустрије 4.0. in ИНДУСТРИЈА 4.0 У ЦИРКУЛАРНОЈ ЕКОНОМИЈИ И ЗАШТИТИ И ОПОРАВКУ ЖИВОТНЕ СРЕДИНЕ. 2023;:102-109..

Ivanović, Jelena, Manojlović, Vaso, Sokić, Miroslav, "Одржива производња у електрoлучним пећима користећи принципе индустрије 4.0" in ИНДУСТРИЈА 4.0 У ЦИРКУЛАРНОЈ ЕКОНОМИЈИ И ЗАШТИТИ И ОПОРАВКУ ЖИВОТНЕ СРЕДИНЕ (2023):102-109.

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  - Pantović Spajić, Katarina
AU  - Marković, Branislav
AU  - Sokić, Miroslav
AU  - Bugarčić, Mladen
AU  - Jovanović, Gvozden
AU  - Manojlović, Vaso
AU  - Stojanović, Ksenija
PY  - 2021
UR  - https://ritnms.itnms.ac.rs/handle/123456789/1122
AB  - All over the world, huge amounts of coal are available and it is utilized in large quantities for different
purposes. The coal combustion causes environmental problems, such as the release of toxic metals
and other pollutants into wastewaters, emission of noxious gases, produce of ash dumps, etc. One of
the solutions for the reduction of environment pollution, caused by coal combustion, is
demineralization and desulphurization of coal. In that sense, treatment of coal by different chemical
reagents becomes important. A subbituminous coal, used in this study was taken from the Bogovina -
East field (Lower Miocene » 20-16 Ma) of the Bogovina Basin, which is located in Eastern Serbia. The
sample was selected based on the previous studies of Bogovina - East field which indicated a high
amount of sulphur, relatively high percent of mineral matter and considerably amount of liptinites
for humic coal, which represent the most reactive maceral group. The aim of the study was an
attempt to reduce the amount of ash and sulphur in coal, keeping the organic matter unaltered as
possible, using simple and cheap method e.g. treatment with hydrochloric acid (HCl). Ash and total
sulphur content was determined before and after HCl leaching. In addition characteristics of initial
and treated coal were tracked by X-ray diffraction (XRD) analysis and Fourier-transform infrared
(FTIR) spectroscopy. The obtained results showed that the high percentage of deashing (» 80 %) was
achieved with cheap hydrochloric acid. XRD analysis of ash before and after sample treatment
provides more information about mineral phases in coal and effects of chemical leaching. FTIR
analysis indicates almost no changes in structure of coal organic matter after treatment by HCl,
which is important for further coal usage (e.g. combustion). On the other hand, the applied chemical
leaching with HCl had low impact on the sulphur content in Bogovina coal (desulphurization
percentage » 8 %). Therefore, in future research other reagents for efficient desulphurization should
be investigated.
PB  - Sisak : Univerzitet u Zagrebu, Metalurški Fakultet
C3  - 19th INTERNATIONAL FOUNDRYMEN CONFERENCE
T1  - "Chemical leaching of subbituminous coal from the Bogovina - East field (Bogovina basin, Serbia) using hydrochloric acid"
EP  - 440
SP  - 435
ER  - 

@conference{
author = "Pantović Spajić, Katarina and Marković, Branislav and Sokić, Miroslav and Bugarčić, Mladen and Jovanović, Gvozden and Manojlović, Vaso and Stojanović, Ksenija",
year = "2021",
abstract = "All over the world, huge amounts of coal are available and it is utilized in large quantities for different
purposes. The coal combustion causes environmental problems, such as the release of toxic metals
and other pollutants into wastewaters, emission of noxious gases, produce of ash dumps, etc. One of
the solutions for the reduction of environment pollution, caused by coal combustion, is
demineralization and desulphurization of coal. In that sense, treatment of coal by different chemical
reagents becomes important. A subbituminous coal, used in this study was taken from the Bogovina -
East field (Lower Miocene » 20-16 Ma) of the Bogovina Basin, which is located in Eastern Serbia. The
sample was selected based on the previous studies of Bogovina - East field which indicated a high
amount of sulphur, relatively high percent of mineral matter and considerably amount of liptinites
for humic coal, which represent the most reactive maceral group. The aim of the study was an
attempt to reduce the amount of ash and sulphur in coal, keeping the organic matter unaltered as
possible, using simple and cheap method e.g. treatment with hydrochloric acid (HCl). Ash and total
sulphur content was determined before and after HCl leaching. In addition characteristics of initial
and treated coal were tracked by X-ray diffraction (XRD) analysis and Fourier-transform infrared
(FTIR) spectroscopy. The obtained results showed that the high percentage of deashing (» 80 %) was
achieved with cheap hydrochloric acid. XRD analysis of ash before and after sample treatment
provides more information about mineral phases in coal and effects of chemical leaching. FTIR
analysis indicates almost no changes in structure of coal organic matter after treatment by HCl,
which is important for further coal usage (e.g. combustion). On the other hand, the applied chemical
leaching with HCl had low impact on the sulphur content in Bogovina coal (desulphurization
percentage » 8 %). Therefore, in future research other reagents for efficient desulphurization should
be investigated.",
publisher = "Sisak : Univerzitet u Zagrebu, Metalurški Fakultet",
journal = "19th INTERNATIONAL FOUNDRYMEN CONFERENCE",
title = ""Chemical leaching of subbituminous coal from the Bogovina - East field (Bogovina basin, Serbia) using hydrochloric acid"",
pages = "440-435"
}

Pantović Spajić, K., Marković, B., Sokić, M., Bugarčić, M., Jovanović, G., Manojlović, V.,& Stojanović, K.. (2021). "Chemical leaching of subbituminous coal from the Bogovina - East field (Bogovina basin, Serbia) using hydrochloric acid". in 19th INTERNATIONAL FOUNDRYMEN CONFERENCE
Sisak : Univerzitet u Zagrebu, Metalurški Fakultet., 435-440.

Pantović Spajić K, Marković B, Sokić M, Bugarčić M, Jovanović G, Manojlović V, Stojanović K. "Chemical leaching of subbituminous coal from the Bogovina - East field (Bogovina basin, Serbia) using hydrochloric acid". in 19th INTERNATIONAL FOUNDRYMEN CONFERENCE. 2021;:435-440..

Pantović Spajić, Katarina, Marković, Branislav, Sokić, Miroslav, Bugarčić, Mladen, Jovanović, Gvozden, Manojlović, Vaso, Stojanović, Ksenija, ""Chemical leaching of subbituminous coal from the Bogovina - East field (Bogovina basin, Serbia) using hydrochloric acid"" in 19th INTERNATIONAL FOUNDRYMEN CONFERENCE (2021):435-440.