A neural model of friction material behaviour
Само за регистроване кориснике
2006
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The neural computation ability to model complex non-linear relationships directly from experimental data, without any prior assumptions about nature of the input/output relationships, has been used in this paper. An artificial neural network technique was used to develop a neural model for predicting the friction materials behavior under prescribed testing conditions. By means of neural modeling of the friction materials behavior, the relationship between 26 input parameters and one output parameter (brake factor C) has been established. The input parameters are defined by the friction material formulation (18 parameters), manufacturing conditions (5 parameters), and testing conditions (3 parameters). Prediction abilities of the neural model have been evaluated by comparison the real cold performance obtained during friction material testing on the single end full-scale inertia dynamometer and predicted ones.
Извор:
SAE Technical Papers, 2006Издавач:
- SAE International
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Aleksendrić, Dragan AU - Duboka, Čedomir PY - 2006 UR - https://machinery.mas.bg.ac.rs/handle/123456789/553 AB - The neural computation ability to model complex non-linear relationships directly from experimental data, without any prior assumptions about nature of the input/output relationships, has been used in this paper. An artificial neural network technique was used to develop a neural model for predicting the friction materials behavior under prescribed testing conditions. By means of neural modeling of the friction materials behavior, the relationship between 26 input parameters and one output parameter (brake factor C) has been established. The input parameters are defined by the friction material formulation (18 parameters), manufacturing conditions (5 parameters), and testing conditions (3 parameters). Prediction abilities of the neural model have been evaluated by comparison the real cold performance obtained during friction material testing on the single end full-scale inertia dynamometer and predicted ones. PB - SAE International C3 - SAE Technical Papers T1 - A neural model of friction material behaviour DO - 10.4271/2006-01-3200 ER -
@conference{ author = "Aleksendrić, Dragan and Duboka, Čedomir", year = "2006", abstract = "The neural computation ability to model complex non-linear relationships directly from experimental data, without any prior assumptions about nature of the input/output relationships, has been used in this paper. An artificial neural network technique was used to develop a neural model for predicting the friction materials behavior under prescribed testing conditions. By means of neural modeling of the friction materials behavior, the relationship between 26 input parameters and one output parameter (brake factor C) has been established. The input parameters are defined by the friction material formulation (18 parameters), manufacturing conditions (5 parameters), and testing conditions (3 parameters). Prediction abilities of the neural model have been evaluated by comparison the real cold performance obtained during friction material testing on the single end full-scale inertia dynamometer and predicted ones.", publisher = "SAE International", journal = "SAE Technical Papers", title = "A neural model of friction material behaviour", doi = "10.4271/2006-01-3200" }
Aleksendrić, D.,& Duboka, Č.. (2006). A neural model of friction material behaviour. in SAE Technical Papers SAE International.. https://doi.org/10.4271/2006-01-3200
Aleksendrić D, Duboka Č. A neural model of friction material behaviour. in SAE Technical Papers. 2006;. doi:10.4271/2006-01-3200 .
Aleksendrić, Dragan, Duboka, Čedomir, "A neural model of friction material behaviour" in SAE Technical Papers (2006), https://doi.org/10.4271/2006-01-3200 . .