Probabilistic Surface Layer Fatigue Strength Assessment of EN AC-46200 Sand Castings

Sebastian Pomberger, Matthias Oberreiter, Martin Leitner, Michael Stoschka, Jörg Maximilian Thuswaldner

Research output: Contribution to journalArticlepeer-review


The local fatigue strength within the aluminium cast surface layer is affected strongly by surface layer porosity and cast surface texture based notches. This article perpetuates the scientific methodology of a previously published fatigue assessment model of sand cast aluminium surface layers in T6 heat treatment condition. A new sampling position with significantly different surface roughness is investigated and the model exponents a1 and a2 are re-parametrised to be suited for a significantly increased range of surface roughness values. Furthermore, the fatigue assessment model of specimens in hot isostatic pressing (HIP) heat treatment condition is studied for all sampling positions. The obtained long life fatigue strength results are approximately 6% to 9% conservative, thus proven valid within an range of 30 µm ≤ Sv ≤ 260 µm notch valley depth. To enhance engineering feasibility even further, the local concept is extended by a probabilistic approach invoking extreme value statistics. A bivariate distribution enables an advanced probabilistic long life fatigue strength of cast surface textures, based on statistically derived parameters such as extremal valley depth Svi and equivalent notch root radius ρi . Summing up, a statistically driven fatigue strength assessment tool of sand cast aluminium surfaces has been developed and features an engineering friendly design method.
Original languageEnglish
Article number616
Issue number5
Publication statusPublished - May 2020
Externally publishedYes


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