Prediction of brake friction materials speed sensitivity
Само за регистроване кориснике
2009
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Prediction of the brake friction material performance versus changes of its composition, manufacturing, and operation conditions is considered as an important step in the friction materials development. Due to complex synergy effects of these influencing factors on the friction coefficient stability, an analytical model of the brake friction materials performance is difficult to obtain. That is why in this paper artificial neural networks have been used for modelling and predicting the effects of these influencing factors on the brake friction materials speed sensitivity. A two hidden-layer neural network model, trained by the Bayesian Regulation algorithm, has been developed with inherent abilities to generalize the complex influences on the speed sensitivity performance of the brake friction materials.
Извор:
SAE Technical Papers, 2009Издавач:
- SAE International
Финансирање / пројекти:
- Истраживање и развој возила ЗАСТАВА 10 на компримовани природни гас (RS-MESTD-MPN2006-2010-14006)
- Развој методе за процену безбедности према стању возила (RS-MESTD-MPN2006-2010-15012)
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Aleksendrić, Dragan PY - 2009 UR - https://machinery.mas.bg.ac.rs/handle/123456789/896 AB - Prediction of the brake friction material performance versus changes of its composition, manufacturing, and operation conditions is considered as an important step in the friction materials development. Due to complex synergy effects of these influencing factors on the friction coefficient stability, an analytical model of the brake friction materials performance is difficult to obtain. That is why in this paper artificial neural networks have been used for modelling and predicting the effects of these influencing factors on the brake friction materials speed sensitivity. A two hidden-layer neural network model, trained by the Bayesian Regulation algorithm, has been developed with inherent abilities to generalize the complex influences on the speed sensitivity performance of the brake friction materials. PB - SAE International C3 - SAE Technical Papers T1 - Prediction of brake friction materials speed sensitivity DO - 10.4271/2009-01-3008 ER -
@conference{ author = "Aleksendrić, Dragan", year = "2009", abstract = "Prediction of the brake friction material performance versus changes of its composition, manufacturing, and operation conditions is considered as an important step in the friction materials development. Due to complex synergy effects of these influencing factors on the friction coefficient stability, an analytical model of the brake friction materials performance is difficult to obtain. That is why in this paper artificial neural networks have been used for modelling and predicting the effects of these influencing factors on the brake friction materials speed sensitivity. A two hidden-layer neural network model, trained by the Bayesian Regulation algorithm, has been developed with inherent abilities to generalize the complex influences on the speed sensitivity performance of the brake friction materials.", publisher = "SAE International", journal = "SAE Technical Papers", title = "Prediction of brake friction materials speed sensitivity", doi = "10.4271/2009-01-3008" }
Aleksendrić, D.. (2009). Prediction of brake friction materials speed sensitivity. in SAE Technical Papers SAE International.. https://doi.org/10.4271/2009-01-3008
Aleksendrić D. Prediction of brake friction materials speed sensitivity. in SAE Technical Papers. 2009;. doi:10.4271/2009-01-3008 .
Aleksendrić, Dragan, "Prediction of brake friction materials speed sensitivity" in SAE Technical Papers (2009), https://doi.org/10.4271/2009-01-3008 . .