Prediction of the wear characteristics of ZA-27/SiC nanocomposites using the artificial neural network
Апстракт
The zinc-aluminium casting alloy ZA-27 is well-established and is a frequently used material for plain bearing sleeves. It has good physical, mechanical and tribological properties. Its tribological properties can be improved further by adding hard ceramic particles to the alloy. The tested nanocomposites were produced by the compocasting process with mechanical alloying preprocessing (ball milling). Three different amounts of SiC nanoparticles, with the same average size of 50 nm, were used as reinforcement, i.e. 0.2, 0.3 and 0.5 wt. %. Tests were performed on a block-on-disc tribometer (line contact) under lubricated sliding conditions, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m. The prediction of wear rate was performed with the use of an artificial neural network (ANN). After training the ANN with architecture 3-4-1, the regression coefficient for the network was 0.99973. The experimental values and values obtained by ap...plying the Taguchi method were compared with the predicted values, showing that ANN is more efficient in predicting wear.
Кључне речи:
artificial neural network / nanocomposites / nanoparticles / wear / ZA-27 alloyИзвор:
Proceedings of the 6th International Scientific Conference "COMETa 2022", East Sarajevo, B&H, RS 17th – 19th November, 2022, 2022, 107-114Издавач:
- University of East Sarajevo, Faculty of Mechanical Engineering
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200105 (Универзитет у Београду, Машински факултет) (RS-MESTD-inst-2020-200105)
- Развој триболошких микро/нано двокомпонентних и хибридних самоподмазујућих композита (RS-MESTD-Technological Development (TD or TR)-35021)
- Bilateral project 337-00-00111/2020-09/50 and BI-RS/20-21-047 between Republic of Serbia and Republic of Slovenia
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Vencl, Aleksandar AU - Stojanović, Blaža AU - Miladinović, Slavica AU - Klobčar, Damjan PY - 2022 UR - https://machinery.mas.bg.ac.rs/handle/123456789/4351 AB - The zinc-aluminium casting alloy ZA-27 is well-established and is a frequently used material for plain bearing sleeves. It has good physical, mechanical and tribological properties. Its tribological properties can be improved further by adding hard ceramic particles to the alloy. The tested nanocomposites were produced by the compocasting process with mechanical alloying preprocessing (ball milling). Three different amounts of SiC nanoparticles, with the same average size of 50 nm, were used as reinforcement, i.e. 0.2, 0.3 and 0.5 wt. %. Tests were performed on a block-on-disc tribometer (line contact) under lubricated sliding conditions, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m. The prediction of wear rate was performed with the use of an artificial neural network (ANN). After training the ANN with architecture 3-4-1, the regression coefficient for the network was 0.99973. The experimental values and values obtained by applying the Taguchi method were compared with the predicted values, showing that ANN is more efficient in predicting wear. PB - University of East Sarajevo, Faculty of Mechanical Engineering C3 - Proceedings of the 6th International Scientific Conference "COMETa 2022", East Sarajevo, B&H, RS 17th – 19th November, 2022 T1 - Prediction of the wear characteristics of ZA-27/SiC nanocomposites using the artificial neural network EP - 114 SP - 107 UR - https://hdl.handle.net/21.15107/rcub_machinery_4351 ER -
@conference{ author = "Vencl, Aleksandar and Stojanović, Blaža and Miladinović, Slavica and Klobčar, Damjan", year = "2022", abstract = "The zinc-aluminium casting alloy ZA-27 is well-established and is a frequently used material for plain bearing sleeves. It has good physical, mechanical and tribological properties. Its tribological properties can be improved further by adding hard ceramic particles to the alloy. The tested nanocomposites were produced by the compocasting process with mechanical alloying preprocessing (ball milling). Three different amounts of SiC nanoparticles, with the same average size of 50 nm, were used as reinforcement, i.e. 0.2, 0.3 and 0.5 wt. %. Tests were performed on a block-on-disc tribometer (line contact) under lubricated sliding conditions, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m. The prediction of wear rate was performed with the use of an artificial neural network (ANN). After training the ANN with architecture 3-4-1, the regression coefficient for the network was 0.99973. The experimental values and values obtained by applying the Taguchi method were compared with the predicted values, showing that ANN is more efficient in predicting wear.", publisher = "University of East Sarajevo, Faculty of Mechanical Engineering", journal = "Proceedings of the 6th International Scientific Conference "COMETa 2022", East Sarajevo, B&H, RS 17th – 19th November, 2022", title = "Prediction of the wear characteristics of ZA-27/SiC nanocomposites using the artificial neural network", pages = "114-107", url = "https://hdl.handle.net/21.15107/rcub_machinery_4351" }
Vencl, A., Stojanović, B., Miladinović, S.,& Klobčar, D.. (2022). Prediction of the wear characteristics of ZA-27/SiC nanocomposites using the artificial neural network. in Proceedings of the 6th International Scientific Conference "COMETa 2022", East Sarajevo, B&H, RS 17th – 19th November, 2022 University of East Sarajevo, Faculty of Mechanical Engineering., 107-114. https://hdl.handle.net/21.15107/rcub_machinery_4351
Vencl A, Stojanović B, Miladinović S, Klobčar D. Prediction of the wear characteristics of ZA-27/SiC nanocomposites using the artificial neural network. in Proceedings of the 6th International Scientific Conference "COMETa 2022", East Sarajevo, B&H, RS 17th – 19th November, 2022. 2022;:107-114. https://hdl.handle.net/21.15107/rcub_machinery_4351 .
Vencl, Aleksandar, Stojanović, Blaža, Miladinović, Slavica, Klobčar, Damjan, "Prediction of the wear characteristics of ZA-27/SiC nanocomposites using the artificial neural network" in Proceedings of the 6th International Scientific Conference "COMETa 2022", East Sarajevo, B&H, RS 17th – 19th November, 2022 (2022):107-114, https://hdl.handle.net/21.15107/rcub_machinery_4351 .