Multifeature Extraction Of Three-Dimensional Topography Of Carbon Steel Specimen During Fatigue Process

In order to investigate the variation of three-dimensional metal surface topography during fatigue process, a three-dimensional (3D) topography acquisition platform was built with an in situ tensile tester and a three-dimensional profilometer. Q235 steel specimens were chosen as research objects, and the three-dimensional surface topography information at various stages of fatigue damage was obtained. Through the characterization of three-dimensional roughness, combined with surface height distribution and multifractal analysis, the variations of metal surface topography in the fatigue process were described. Results show that the arithmetic mean deviation of the surface (S-a), the width of the multifractal spectrum (Delta alpha), and the mean value of surface height distribution (mu) and its standard deviation (delta) increase nonlinearly with the increase of fatigue cycles. The rate of fatigue damage is slow in the early stage and high in the middle and late stages. The surface height distribution amplitude (A) decreases with the increase of fatigue cycles, which indicates that the height data concentration decreases, and the metal surface becomes uneven. The Bayesian data fusion method was applied to establish a nonlinear mapping between the topography features and the damage, with the above five characteristic parameters (S-a, Delta alpha, A, mu, and delta) as the data layer. Finally, a surface topography feature fusion method is proposed, and a case study is conducted to verify its applicability. The research results can provide reference for fatigue damage assessment.