TY  - JOUR
AU  - Vencl, Aleksandar
AU  - Svoboda, Petr
AU  - Klančnik, Simon
AU  - But, Adrian
AU  - Vorkapić, Miloš
AU  - Harničarova, Marta
AU  - Stojanović, Blaža
PY  - 2023
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/5643
AB  - Three different and very small amounts of alumina (0.2, 0.3 and 0.5 wt. %) in two sizes
(approx. 25 and 100 nm) were used to enhance the wear characteristics of ZA-27 alloy-based nanocomposites.
Production was realised through mechanical alloying in pre-processing and compocasting
processes. Wear tests were under lubricated sliding conditions on a block-on-disc tribometer, at two
sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m.
Experimental results were analysed by applying the response surface methodology (RSM) and a
suitable mathematical model for the wear rate of tested nanocomposites was developed. Appropriate
wear maps were constructed and the wear mechanism is discussed in this paper. The accuracy of the
prediction was evaluated with the use of an artificial neural network (ANN). The architecture of the
used ANN was 4-5-1 and the obtained overall regression coefficient was 0.98729. The comparison of
the predicting methods showed that ANN is more efficient in predicting wear.
PB  - Switzerland : Multidisciplinary Digital Publishing Institute (MDPI)
T2  - Lubricants
T1  - Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction
VL  - 11
IS  - 1
SP  - 24
DO  - 10.3390/lubricants11010024
ER  - 

@article{
author = "Vencl, Aleksandar and Svoboda, Petr and Klančnik, Simon and But, Adrian and Vorkapić, Miloš and Harničarova, Marta and Stojanović, Blaža",
year = "2023",
abstract = "Three different and very small amounts of alumina (0.2, 0.3 and 0.5 wt. %) in two sizes
(approx. 25 and 100 nm) were used to enhance the wear characteristics of ZA-27 alloy-based nanocomposites.
Production was realised through mechanical alloying in pre-processing and compocasting
processes. Wear tests were under lubricated sliding conditions on a block-on-disc tribometer, at two
sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m.
Experimental results were analysed by applying the response surface methodology (RSM) and a
suitable mathematical model for the wear rate of tested nanocomposites was developed. Appropriate
wear maps were constructed and the wear mechanism is discussed in this paper. The accuracy of the
prediction was evaluated with the use of an artificial neural network (ANN). The architecture of the
used ANN was 4-5-1 and the obtained overall regression coefficient was 0.98729. The comparison of
the predicting methods showed that ANN is more efficient in predicting wear.",
publisher = "Switzerland : Multidisciplinary Digital Publishing Institute (MDPI)",
journal = "Lubricants",
title = "Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction",
volume = "11",
number = "1",
pages = "24",
doi = "10.3390/lubricants11010024"
}

Vencl, A., Svoboda, P., Klančnik, S., But, A., Vorkapić, M., Harničarova, M.,& Stojanović, B.. (2023). Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction. in Lubricants
Switzerland : Multidisciplinary Digital Publishing Institute (MDPI)., 11(1), 24.
https://doi.org/10.3390/lubricants11010024

Vencl A, Svoboda P, Klančnik S, But A, Vorkapić M, Harničarova M, Stojanović B. Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction. in Lubricants. 2023;11(1):24.
doi:10.3390/lubricants11010024 .

Vencl, Aleksandar, Svoboda, Petr, Klančnik, Simon, But, Adrian, Vorkapić, Miloš, Harničarova, Marta, Stojanović, Blaža, "Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction" in Lubricants, 11, no. 1 (2023):24,
https://doi.org/10.3390/lubricants11010024 . .