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  - JOUR
AU  - Sokić, Miroslav
AU  - Ilić, Ilija B.
AU  - Gulišija, Zvonko
AU  - Kamberović, Željko
AU  - Štrbac, Nada
AU  - Manojlović, Vaso
PY  - 2013
UR  - https://ritnms.itnms.ac.rs/handle/123456789/285
AB  - For the prediction of formation of non-ferrous processing and accumulating waste both model of balancing and model of correlation and regression analysis can be employed, while the dynamic model can be applied only for the accumulating waste. Estimates of the formation of certain type of metallic secondary raw materials in model of balancing are based on data of production and consumption of metals and on the numerical values of the waste formation coefficients. Applying of correlation and regression analysis method on the assessment of metallic waste formation is based on finding the functional dependence between the creation of metallic waste and quantity of waste on the input stream of processing as well as correlation with metal fund for accumulation waste. The dynamic model are linking the effect of the products materials content and design on the amount of the quantity of the used products which are recycled over time, including various external influential factors.
PB  - University of East Sarajevo, Faculty of Technology
T2  - Journal of Engineering & Processing Management
T1  - Prediction models of non-ferrous secondary raw materials formation
EP  - 65
IS  - 1
SP  - 55
VL  - 5
UR  - conv_1205
ER  - 

@article{
author = "Sokić, Miroslav and Ilić, Ilija B. and Gulišija, Zvonko and Kamberović, Željko and Štrbac, Nada and Manojlović, Vaso",
year = "2013",
abstract = "For the prediction of formation of non-ferrous processing and accumulating waste both model of balancing and model of correlation and regression analysis can be employed, while the dynamic model can be applied only for the accumulating waste. Estimates of the formation of certain type of metallic secondary raw materials in model of balancing are based on data of production and consumption of metals and on the numerical values of the waste formation coefficients. Applying of correlation and regression analysis method on the assessment of metallic waste formation is based on finding the functional dependence between the creation of metallic waste and quantity of waste on the input stream of processing as well as correlation with metal fund for accumulation waste. The dynamic model are linking the effect of the products materials content and design on the amount of the quantity of the used products which are recycled over time, including various external influential factors.",
publisher = "University of East Sarajevo, Faculty of Technology",
journal = "Journal of Engineering & Processing Management",
title = "Prediction models of non-ferrous secondary raw materials formation",
pages = "65-55",
number = "1",
volume = "5",
url = "conv_1205"
}

Sokić, M., Ilić, I. B., Gulišija, Z., Kamberović, Ž., Štrbac, N.,& Manojlović, V.. (2013). Prediction models of non-ferrous secondary raw materials formation. in Journal of Engineering & Processing Management
University of East Sarajevo, Faculty of Technology., 5(1), 55-65.
conv_1205

Sokić M, Ilić IB, Gulišija Z, Kamberović Ž, Štrbac N, Manojlović V. Prediction models of non-ferrous secondary raw materials formation. in Journal of Engineering & Processing Management. 2013;5(1):55-65.
conv_1205 .

Sokić, Miroslav, Ilić, Ilija B., Gulišija, Zvonko, Kamberović, Željko, Štrbac, Nada, Manojlović, Vaso, "Prediction models of non-ferrous secondary raw materials formation" in Journal of Engineering & Processing Management, 5, no. 1 (2013):55-65,
conv_1205 .