Abstract
The global objective of this study was to investigate the best features of the surface topography for fatigue-damage detection and classification. The presence of the stress concentration in valleys of the surface topography causes a grain slip and a crack initiation at the surface of the machined structure and finally leads to fatigue failures. Therefore, the surface topography has a major influence on the fatigue strength of the machined structure. An optical confocal measurement system (Alicona) was applied to measure six surface topography parameters. In this paper, feature selection using the Pearson correlation method was adopted to select the best surface textures that provide best the neural network (NN) model performance. The NN model is capable of detecting and classifying the damage with an accuracy of up to similar to$$ \sim $$ 94.4%.