Determination of tribological properties at CuSn10 alloy journal bearings by experimental and means of artificial neural networks method

Purpose - It is important to know the friction coefficient and wear loss for determination of tribological conditions at journal bearings. Tribological events that influence wear and its variations affect experimental results. The purpose of this paper is to determine friction coefficient and wear loss at CuSn10 alloy radial bearings by a new approach. In experiments, effects of bearings have been examined at dry and lubricated conditions and at different loads and velocities. Design/methodology/approach - In this study, friction coefficient and wear losses of journal and bearing have been determined by a new approach with a radial journal bearing test rig and artificial neural networks (ANNs) method. The ANN typifies a learning technique that enables the hidden input-output relationship to be mapped accurately. Bronze-based materials have been used as bearing material. Effects of friction coefficient and wear losses have been examined at same load and velocity and at dry and lubricated conditions. Findings - The results obtained in ANN application are close to friction test results for dry and lubricated conditions. Therefore, by using trained ANN values, the intermediate results that were not obtained in the tests can be calculated. Experimental studies will be increased and research with ANN will be continued. Originality/value - By using trained ANN values, the intermediate results that were not obtained in the tests can be calculated. The training finished on 30 min whereas experimental study had continued day after day.