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In the present paper, a procedure for fatigue strength assessment of metals containing solidification defects is employed to analyze the fatigue behavior of a ductile cast iron (DCI) characterized by a relevant micro‐shrinkage porosity. The procedure implements (i) a statistical method deriving from extreme value theory, (ii) the
area‐parameter model, and (iii) the multiaxial critical plane‐based criterion by Carpinteri et al. According to the above statistical method, both the distribution of defects and the return period are determined. More precisely, the return period is computed by also exploiting a relationship here proposed to optimize the accuracy of the procedure in terms of fatigue strength estimation. The great potential of the present procedure is that the defect content analysis (performed by means of a statistical method deriving from extreme value theory) can be easily performed using machine learning techniques.
HIGHLIGHTS
A procedure for fatigue assessment of metals with solidification defects is employed.
The fatigue behavior of a DCI with a relevant micro‐shrinkage porosity is analyzed.
A defect content analysis is performed by means of a statistical method deriving from EVT.
The return period is computed by a relationship that optimizes the procedure accuracy.