Rotation invariant classification of rough surfaces

Rotation of a rough, textured surface will not produce a simple rotation of the image texture. It follows that where image texture is a function of surface topography, existing rotation invariant texture classification algorithms are not robust to surface rotation. The effect of surface rotation on the observed image is analysed using an existing theory, a novel scheme to stabilise the classification accuracy is proposed and evaluated. The scheme uses photometric stereo to estimate the surface derivatives, which are then used as the input to a classifier. Simulations indicate that, where the level of image noise is moderate or low, the approach is successful in maintaining classification accuracy. Furthermore, in some circumstances, the extra information used by the algorithm allows classification accuracy superior to that based on one image alone, even without rotation     

A probabilistic approach to predict the very high-cycle fatigue behaviour of spheroidal graphite cast iron structures

A new probabilistic approach is developed to study structures made of spheroidal graphite cast iron and subjected to very high-cycle fatigue. Until now, the probabilistic approach was based on S–N curves obtained from experiments carried out only until 10⁷ cycles. To validate this approach, failure predictions relating to the safety of components are computed and compared to experimental results. In addition to this development, an extension is proposed in order to improve the very long life assessment of complex structures. An extrapolation of the previous fatigue results to 10⁹–10¹¹ cycles illustrates the error made on cumulative failure probabilities. Finally, the respective influence of the casting flaw distribution, volume and stress field heterogeneity within specimens and industrial components is studied.