Neural network prediction of disc brake performance
Само за регистроване кориснике
2009
Чланак у часопису (Објављена верзија)
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Приказ свих података о документуАпстракт
An automotive brake's performance results from the complex interrelated phenomena occurring at the contact of the friction pair. These complex braking phenomena are mostly affected by the tribochemical properties of the friction material's ingredients, the brake disc properties, and the brake's operating regimes. In this paper, the synergistic effects of the friction material's properties, defined by its composition and manufacturing conditions, and the brake's operating regimes on the disc brake factor C variation have been modelled by means of artificial neural networks. The influences of 26 input parameters, determined by the friction material composition (18 ingredients), its manufacturing conditions (5 parameters), and the brake's operating regimes (3 parameters) on the brake factor C variation, have been predicted. The neural model of the disc brake cold performance has been developed by training 18 different neural network architectures with the five different learning algorithm...s. The optimal neural model of disc brake operation has been shown to be valid for predicting the brake factor C variation of the cold disc brake over a wide range of brake's operating regimes and for different types of friction material.
Кључне речи:
Prediction / Neural network / Friction material / Disc brake performanceИзвор:
Tribology International, 2009, 42, 7, 1074-1080Издавач:
- Elsevier Sci Ltd, Oxford
DOI: 10.1016/j.triboint.2009.03.005
ISSN: 0301-679X
WoS: 000266836200008
Scopus: 2-s2.0-67349257795
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Aleksendrić, Dragan AU - Barton, David C. PY - 2009 UR - https://machinery.mas.bg.ac.rs/handle/123456789/976 AB - An automotive brake's performance results from the complex interrelated phenomena occurring at the contact of the friction pair. These complex braking phenomena are mostly affected by the tribochemical properties of the friction material's ingredients, the brake disc properties, and the brake's operating regimes. In this paper, the synergistic effects of the friction material's properties, defined by its composition and manufacturing conditions, and the brake's operating regimes on the disc brake factor C variation have been modelled by means of artificial neural networks. The influences of 26 input parameters, determined by the friction material composition (18 ingredients), its manufacturing conditions (5 parameters), and the brake's operating regimes (3 parameters) on the brake factor C variation, have been predicted. The neural model of the disc brake cold performance has been developed by training 18 different neural network architectures with the five different learning algorithms. The optimal neural model of disc brake operation has been shown to be valid for predicting the brake factor C variation of the cold disc brake over a wide range of brake's operating regimes and for different types of friction material. PB - Elsevier Sci Ltd, Oxford T2 - Tribology International T1 - Neural network prediction of disc brake performance EP - 1080 IS - 7 SP - 1074 VL - 42 DO - 10.1016/j.triboint.2009.03.005 ER -
@article{ author = "Aleksendrić, Dragan and Barton, David C.", year = "2009", abstract = "An automotive brake's performance results from the complex interrelated phenomena occurring at the contact of the friction pair. These complex braking phenomena are mostly affected by the tribochemical properties of the friction material's ingredients, the brake disc properties, and the brake's operating regimes. In this paper, the synergistic effects of the friction material's properties, defined by its composition and manufacturing conditions, and the brake's operating regimes on the disc brake factor C variation have been modelled by means of artificial neural networks. The influences of 26 input parameters, determined by the friction material composition (18 ingredients), its manufacturing conditions (5 parameters), and the brake's operating regimes (3 parameters) on the brake factor C variation, have been predicted. The neural model of the disc brake cold performance has been developed by training 18 different neural network architectures with the five different learning algorithms. The optimal neural model of disc brake operation has been shown to be valid for predicting the brake factor C variation of the cold disc brake over a wide range of brake's operating regimes and for different types of friction material.", publisher = "Elsevier Sci Ltd, Oxford", journal = "Tribology International", title = "Neural network prediction of disc brake performance", pages = "1080-1074", number = "7", volume = "42", doi = "10.1016/j.triboint.2009.03.005" }
Aleksendrić, D.,& Barton, D. C.. (2009). Neural network prediction of disc brake performance. in Tribology International Elsevier Sci Ltd, Oxford., 42(7), 1074-1080. https://doi.org/10.1016/j.triboint.2009.03.005
Aleksendrić D, Barton DC. Neural network prediction of disc brake performance. in Tribology International. 2009;42(7):1074-1080. doi:10.1016/j.triboint.2009.03.005 .
Aleksendrić, Dragan, Barton, David C., "Neural network prediction of disc brake performance" in Tribology International, 42, no. 7 (2009):1074-1080, https://doi.org/10.1016/j.triboint.2009.03.005 . .