Feature extraction on machined surface texture image of tool wear based on fractional brown motion

The monitoring of tool wear state by the texture of the turning surfaces is studied in this paper. A microscopic image acquisition system was established to capture the images from turning surface and tool flank, then the relation between turning surface texture and tool flank wear state was studied based on Fractional Brown motion model. Specifically, logarithm power spectrum (LPS) of each surface texture image was earned by using the Fourier transform, five sets of data was selected from the LPS on five different directions (namely, X-axis direction, 30 degrees direction, 45 degrees direction, 60 degrees direction and Y-axis direction), fit-slope and fractal dimension of earned data were calculated by linear fitting method. Finally, fractal dimension on X-axis direction, found to be highly correlated with the trend of flank wear, can be regarded as the texture feature parameter of tool wear state monitoring.