Abstract
Fatigue properties of steels are strongly influenced by the presence of microscopic particles of oxides or foreign material known as inclusions. The size of the largest inclusion is an important determinant of fatigue strength. This paper studies the problem of estimating the sizes of large inclusions from measurements made on a two-dimensional section of the steel. The approach combines traditional stereological ideas with more recent extreme value modeling. It is shown that both classical likelihood and Bayesian approaches are useful in the inference.
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