VENCL, Aleksander, Petr SVOBODA, Simon KLANČNIK, Adrian BUT, Miloš VORKAPIĆ, Marta HARNIČÁROVÁ a Blaža STOJANOVIĆ. Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction. Lubricants. Switzerland: MDPI, roč. 11, č. 1, s. 1-13, 14 s. ISSN 2075-4442. 2023. |
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@article{66681, author = {Vencl, Aleksander and Svoboda, Petr and Klančnik, Simon and But, Adrian and Vorkapić, Miloš and Harničárová, Marta and Stojanović, Blaža}, article_location = {Switzerland}, article_number = {1}, keywords = {ZA-27 alloy; Al2O3 nanoparticles; nanocomposites; wear; response surface methodology; artificial neural network}, language = {eng}, issn = {2075-4442}, journal = {Lubricants}, title = {Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction}, url = {https://cer.ihtm.bg.ac.rs/bitstream/id/23662/lubricants-11-00024-v2.pdf}, volume = {11}, year = {2023} }
TY - JOUR ID - 66681 AU - Vencl, Aleksander - Svoboda, Petr - Klančnik, Simon - But, Adrian - Vorkapić, Miloš - Harničárová, Marta - Stojanović, Blaža PY - 2023 TI - Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction JF - Lubricants VL - 11 IS - 1 SP - 1-13 EP - 1-13 PB - MDPI SN - 20754442 KW - ZA-27 alloy KW - Al2O3 nanoparticles KW - nanocomposites KW - wear KW - response surface methodology KW - artificial neural network UR - https://cer.ihtm.bg.ac.rs/bitstream/id/23662/lubricants-11-00024-v2.pdf N2 - 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. ER -
VENCL, Aleksander, Petr SVOBODA, Simon KLANČNIK, Adrian BUT, Miloš VORKAPI$\backslash$'C, Marta HARNIČÁROVÁ a Blaža STOJANOVI$\backslash$'C. Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction. \textit{Lubricants}. Switzerland: MDPI, roč.~11, č.~1, s.~1-13, 14 s. ISSN~2075-4442. 2023.
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