Prediction of brake friction materials recovery performance using artificial neural networks
Само за регистроване кориснике
2010
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The brake friction materials in an automotive brake system are considered as one of the key components for overall braking performance of a vehicle. The sensitivity of friction material performance and accordingly brake performance, versus different operating regimes, has always been an important aspect of its functioning. In this paper, the influences not only on the brake operation conditions but also on the formulation and manufacturing conditions of friction materials have been investigated regarding friction materials recovery performance by means of artificial neural networks. A new neural network model of friction material recovery performance, trained by the Bayesian Regulation algorithm, has been developed.
Кључне речи:
Recovery performance / Friction materials / Artificial neural networksИзвор:
Tribology International, 2010, 43, 11, 2092-2099Издавач:
- Elsevier Sci Ltd, Oxford
DOI: 10.1016/j.triboint.2010.05.013
ISSN: 0301-679X
WoS: 000282924900015
Scopus: 2-s2.0-77956184776
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Aleksendrić, Dragan AU - Barton, David C. AU - Vasić, Branko PY - 2010 UR - https://machinery.mas.bg.ac.rs/handle/123456789/1134 AB - The brake friction materials in an automotive brake system are considered as one of the key components for overall braking performance of a vehicle. The sensitivity of friction material performance and accordingly brake performance, versus different operating regimes, has always been an important aspect of its functioning. In this paper, the influences not only on the brake operation conditions but also on the formulation and manufacturing conditions of friction materials have been investigated regarding friction materials recovery performance by means of artificial neural networks. A new neural network model of friction material recovery performance, trained by the Bayesian Regulation algorithm, has been developed. PB - Elsevier Sci Ltd, Oxford T2 - Tribology International T1 - Prediction of brake friction materials recovery performance using artificial neural networks EP - 2099 IS - 11 SP - 2092 VL - 43 DO - 10.1016/j.triboint.2010.05.013 ER -
@article{ author = "Aleksendrić, Dragan and Barton, David C. and Vasić, Branko", year = "2010", abstract = "The brake friction materials in an automotive brake system are considered as one of the key components for overall braking performance of a vehicle. The sensitivity of friction material performance and accordingly brake performance, versus different operating regimes, has always been an important aspect of its functioning. In this paper, the influences not only on the brake operation conditions but also on the formulation and manufacturing conditions of friction materials have been investigated regarding friction materials recovery performance by means of artificial neural networks. A new neural network model of friction material recovery performance, trained by the Bayesian Regulation algorithm, has been developed.", publisher = "Elsevier Sci Ltd, Oxford", journal = "Tribology International", title = "Prediction of brake friction materials recovery performance using artificial neural networks", pages = "2099-2092", number = "11", volume = "43", doi = "10.1016/j.triboint.2010.05.013" }
Aleksendrić, D., Barton, D. C.,& Vasić, B.. (2010). Prediction of brake friction materials recovery performance using artificial neural networks. in Tribology International Elsevier Sci Ltd, Oxford., 43(11), 2092-2099. https://doi.org/10.1016/j.triboint.2010.05.013
Aleksendrić D, Barton DC, Vasić B. Prediction of brake friction materials recovery performance using artificial neural networks. in Tribology International. 2010;43(11):2092-2099. doi:10.1016/j.triboint.2010.05.013 .
Aleksendrić, Dragan, Barton, David C., Vasić, Branko, "Prediction of brake friction materials recovery performance using artificial neural networks" in Tribology International, 43, no. 11 (2010):2092-2099, https://doi.org/10.1016/j.triboint.2010.05.013 . .