Evolution of Metal Surface Topography during Fatigue

Changes in surface topography reflect the state of fatigue damage. In this paper, a new method to characterize metal surface topography during fatigue has been proposed. Firstly, we acquired surface topography images based on machine vision and separated them into roughness, waviness, and form error images through a shearlet transform. Secondly, we constructed gray co-occurrence matrixes of the obtained surface topography images and calculated the characteristic parameters, such as contrast, correlation coefficient, energy, and entropy for all the original and separated images. Then, taking a Q235 steel specimen as an example for testing, the experimental results and theoretical analysis demonstrate that the parameter contrast increases while energy, correlation coefficient and entropy decrease gradually with number of loading circles, which reach their maximum and minimums before fracture, respectively.