Abstract:
Gears are the key components of mechanical transmission. The grinding burn affects the stability and fatigue life of gears. This paper determines the burn degree of gear burn by tracking the change of microstructure of material. The laser simulated burn gear was tested by the magnetic Barkhausen noise detector, and the Barkhausen noise signal of the burned tooth surface is collected. The characteristic value of the signal is extracted by the relationship between the envelope of the Barkhausen noise signal and the tangential magnetic field (including peak, peak position, half-width), and a prediction model for the evaluation of martensite content is established by using adaptive fuzzy neural network for training. The experimental results show that the method has the ability to detect and characterize the martensitic depth in microscopic metallographic structure, and can avoid the influence of excitation frequency on the output of Barkhausen noise signal. The fit parameter
R2=0.964 1 and mean square root error
RMSE=16.981 7 verifies accuracy of the adaptive fuzzy neural network model and can accurately obtain the martensite depth information of the burn gear, and then predict the degree of gear burn.