Application of artificial neural networks in tribology - prediction and classification models

• Autorzy: Gocman K. , Kałdoński T.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

tribology; boundary friction; ubricity; modelling of tribological processes; artificial neural networks

• Wydawca: Łukasiewicz Research Network - Institute of Aviation ; European Science Society of Powertrain and Transport KONES Poland ; Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONES
• Rocznik: 2009
• Tom: Vol. 16, No. 1
• Strony: 137--144
• Opis fizyczny: Bibliogr. 6 poz., rys.

Twórcy

autor

Gocman K.

autor

Kałdoński T.

• Military University of Technology, Faculty of Mechanical Engineering Institute of Motor Vehicles and Transportation Gen. S. Kaliskiego 2, 00-908 Warsaw, Poland tel: [phone],

Bibliografia

• [1] Kałdoński, T., Tribologia i płyny eksploatacyjne. Cz. I. Wybrane problemy tribologii. Warszawa, 1995.
• [2] Trzos, M., Modele neuronowe do prognozowania właściwości przeciwzużyciowych olejów smarnych. Tribologia 2004 nr 4, s.273-283, 2004.
• [3] Tadeusiewicz, R., Lula, P., Wójtowicz, P., Sieci neuronowe. Materiał kursowy.
• [4] Polish standards: PN-76/C-04147, PN-83/M-86452, PN-77/C-96080.
• [5] Giemza, B.,Gocman, K., Kałdoński T., Neural networks model of wear under boundary lubrication. International Tribology Conference Austrib 2006, Brisbane, Australia, 2006.
• [6] Giemza, B., Gocman, K., Kałdoński, T., Modelling of Moment of Friction Under Increasing Load Using Artificial Neural Networks. Solid State Phenomena Vol. 144 (2009) pp 130-135, 2009.