- Tytuł:
- Application of artificial neural networks in tribology - prediction and classification models
- Autorzy:
-
Gocman, K.
Kałdoński, T. - Powiązania:
- https://bibliotekanauki.pl/articles/244086.pdf
- Data publikacji:
- 2009
- Wydawca:
- Instytut Techniczny Wojsk Lotniczych
- Tematy:
-
tribology
boundary friction
ubricity
modelling of tribological processes
artificial neural networks - Opis:
- The influence of load and rotational speed on wear and moment of friction is presented in this paper. The tests were carried out under both constant and increasing load and at wide range of rotational speed. During the tests moment offriction, oil temperature and weather conditions were registered. On the basis of obtained results neural models for prediction of wear, moment of friction and friction classifiers were created. The different kinds of artificial neural networks and different training algorithms were applied in order to obtain the best generalisation and quality of created models. All researches showed that artificial neural networks are useful as prediction and classification models. Because of too small teaching data models were limited only to two inputs - load and rotational speed and one output — wear, moment offriction or state. The best models achieved very good precision — testing error lower than 5%. It was also proved, that various types of networks have different usefulness for different applications. MLP networks turned out to be the best wear models, GRNN networks gave the best results as models of moment offriction and RBF networks were proved to be the best classifiers. To obtain model which will give better characterization of processes proceeded in tribological pairs, much more experiments to increase teaching data have to be conducted.
- Źródło:
-
Journal of KONES; 2009, 16, 1; 137-144
1231-4005
2354-0133 - Pojawia się w:
- Journal of KONES
- Dostawca treści:
- Biblioteka Nauki
Artykuł