Detail publikace
Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction
VENCL, A. SVOBODA, P. KLANČNIK, S. BUT, A. VORKAPIĆ, M. HARNIČÁROVÁ, M. STOJANOVIĆ, B.
Anglický název
Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
en
Originální abstrakt
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 pro-cesses. 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. Ap-propriate 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.
Anglický abstrakt
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 pro-cesses. 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. Ap-propriate 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.
Klíčová slova anglicky
ZA-27 alloy; Al2O3 nanoparticles; nanocomposites; wear; response surface methodology; artificial neural network
Vydáno
07.01.2023
Nakladatel
MDPI
ISSN
2075-4442
Ročník
11
Číslo
24
Počet stran
13
BIBTEX
@article{BUT180507,
author="Aleksandar {Vencl} and Petr {Svoboda} and Simon {Klančnik} and Adrian {But} and Miloš {Vorkapić} and Marta {Harničárová} and Blaža {Stojanović},
title="Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction ",
year="2023",
volume="11",
number="24",
month="January",
publisher="MDPI",
issn="2075-4442"
}