Influence of Al2O3 nanoparticles addition in ZA-27 alloy-based nanocomposites and soft computing prediction
2023
Autori
Vencl, AleksandarSvoboda, Petr
Klančnik, Simon
But, Adrian
Vorkapić, Miloš
Harničárová, Marta
Stojanović, Blaža
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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 t...hat ANN is more efficient in predicting wear.
Ključne reči:
ZA-27 alloy / Al2O3 nanoparticles / nanocomposites / wear / response surface methodology / artificial neural networkIzvor:
Lubricants, 2023, 11, 1, 24-Finansiranje / projekti:
- bilateral Project 337-00-577/2021-09/16 between Republic of Serbia and Republic of Austria
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200105 (Univerzitet u Beogradu, Mašinski fakultet) (RS-MESTD-inst-2020-200105)
Kolekcije
Institucija/grupa
Mašinski fakultetTY - JOUR AU - Vencl, Aleksandar AU - Svoboda, Petr AU - Klančnik, Simon AU - But, Adrian AU - Vorkapić, Miloš AU - Harničárová, Marta AU - Stojanović, Blaža PY - 2023 UR - https://www.mdpi.com/2075-4442/11/1/24 UR - https://machinery.mas.bg.ac.rs/handle/123456789/7339 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. T2 - Lubricants T1 - Influence of Al2O3 nanoparticles addition in ZA-27 alloy-based nanocomposites and soft computing prediction IS - 1 SP - 24 VL - 11 DO - 10.3390/lubricants11010024 ER -
@article{ author = "Vencl, Aleksandar and Svoboda, Petr and Klančnik, Simon and But, Adrian and Vorkapić, Miloš and Harničárová, 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.", journal = "Lubricants", title = "Influence of Al2O3 nanoparticles addition in ZA-27 alloy-based nanocomposites and soft computing prediction", number = "1", pages = "24", volume = "11", doi = "10.3390/lubricants11010024" }
Vencl, A., Svoboda, P., Klančnik, S., But, A., Vorkapić, M., Harničárová, M.,& Stojanović, B.. (2023). Influence of Al2O3 nanoparticles addition in ZA-27 alloy-based nanocomposites and soft computing prediction. in Lubricants, 11(1), 24. https://doi.org/10.3390/lubricants11010024
Vencl A, Svoboda P, Klančnik S, But A, Vorkapić M, Harničárová 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, Harničárová, 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 . .