Optimization and prediction of aluminium composite wear using Taguchi design and artificial neural network
2016
Authors
Stojanović, BlažaVeličković, Sandra
Vencl, Aleksandar
Babić, Miroslav
Petrović, Nenad
Miladinović, Slavica
Cherkezova-Zheleva, Zara
Article (Published version)
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This paper analyses wear behaviour of Al-Si alloy A356 (AlSi7Mg) based composite reinforced with 10 wt. % SiC, and compare it with the base A356 alloy. Composite are obtained using the compocasting procedure. Tribological testing have been conducted on a block-on-disc tribometer with three varying loads (10, 20 and 30 N) and three sliding speeds (0.25, 0.5 and 1 m/s), under dry sliding conditions. Sliding distance of 300 m was constant. The goal of the paper was to optimize the influencing parameters in order to minimize specific wear rate using the Taguchi method. The analysis showed that the sliding speed has the greatest influence on specific wear rate (39.5 %), followed by the load (23.6 %), and the interaction between sliding speed and load (19.4 %). A regression analysis and experiment corroboration was conducted in order to verify the results of the optimization. Specific wear rate prediction was done using artificial neural network (ANN).
Keywords:
A356 / SiC / Taguchi / specific wear rate / ANNSource:
Tribological Journal BULTRIB, 2016, 6, 38-45Publisher:
- Sofia : Technical University
Funding / projects:
- Development of the tribological micro/nano two component and hybrid selflubricating composites (RS-MESTD-Technological Development (TD or TR)-35021)
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Institution/Community
Mašinski fakultetTY - JOUR AU - Stojanović, Blaža AU - Veličković, Sandra AU - Vencl, Aleksandar AU - Babić, Miroslav AU - Petrović, Nenad AU - Miladinović, Slavica AU - Cherkezova-Zheleva, Zara PY - 2016 UR - https://machinery.mas.bg.ac.rs/handle/123456789/4313 AB - This paper analyses wear behaviour of Al-Si alloy A356 (AlSi7Mg) based composite reinforced with 10 wt. % SiC, and compare it with the base A356 alloy. Composite are obtained using the compocasting procedure. Tribological testing have been conducted on a block-on-disc tribometer with three varying loads (10, 20 and 30 N) and three sliding speeds (0.25, 0.5 and 1 m/s), under dry sliding conditions. Sliding distance of 300 m was constant. The goal of the paper was to optimize the influencing parameters in order to minimize specific wear rate using the Taguchi method. The analysis showed that the sliding speed has the greatest influence on specific wear rate (39.5 %), followed by the load (23.6 %), and the interaction between sliding speed and load (19.4 %). A regression analysis and experiment corroboration was conducted in order to verify the results of the optimization. Specific wear rate prediction was done using artificial neural network (ANN). PB - Sofia : Technical University T2 - Tribological Journal BULTRIB T1 - Optimization and prediction of aluminium composite wear using Taguchi design and artificial neural network EP - 45 SP - 38 VL - 6 UR - https://hdl.handle.net/21.15107/rcub_machinery_4313 ER -
@article{ author = "Stojanović, Blaža and Veličković, Sandra and Vencl, Aleksandar and Babić, Miroslav and Petrović, Nenad and Miladinović, Slavica and Cherkezova-Zheleva, Zara", year = "2016", abstract = "This paper analyses wear behaviour of Al-Si alloy A356 (AlSi7Mg) based composite reinforced with 10 wt. % SiC, and compare it with the base A356 alloy. Composite are obtained using the compocasting procedure. Tribological testing have been conducted on a block-on-disc tribometer with three varying loads (10, 20 and 30 N) and three sliding speeds (0.25, 0.5 and 1 m/s), under dry sliding conditions. Sliding distance of 300 m was constant. The goal of the paper was to optimize the influencing parameters in order to minimize specific wear rate using the Taguchi method. The analysis showed that the sliding speed has the greatest influence on specific wear rate (39.5 %), followed by the load (23.6 %), and the interaction between sliding speed and load (19.4 %). A regression analysis and experiment corroboration was conducted in order to verify the results of the optimization. Specific wear rate prediction was done using artificial neural network (ANN).", publisher = "Sofia : Technical University", journal = "Tribological Journal BULTRIB", title = "Optimization and prediction of aluminium composite wear using Taguchi design and artificial neural network", pages = "45-38", volume = "6", url = "https://hdl.handle.net/21.15107/rcub_machinery_4313" }
Stojanović, B., Veličković, S., Vencl, A., Babić, M., Petrović, N., Miladinović, S.,& Cherkezova-Zheleva, Z.. (2016). Optimization and prediction of aluminium composite wear using Taguchi design and artificial neural network. in Tribological Journal BULTRIB Sofia : Technical University., 6, 38-45. https://hdl.handle.net/21.15107/rcub_machinery_4313
Stojanović B, Veličković S, Vencl A, Babić M, Petrović N, Miladinović S, Cherkezova-Zheleva Z. Optimization and prediction of aluminium composite wear using Taguchi design and artificial neural network. in Tribological Journal BULTRIB. 2016;6:38-45. https://hdl.handle.net/21.15107/rcub_machinery_4313 .
Stojanović, Blaža, Veličković, Sandra, Vencl, Aleksandar, Babić, Miroslav, Petrović, Nenad, Miladinović, Slavica, Cherkezova-Zheleva, Zara, "Optimization and prediction of aluminium composite wear using Taguchi design and artificial neural network" in Tribological Journal BULTRIB, 6 (2016):38-45, https://hdl.handle.net/21.15107/rcub_machinery_4313 .