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Open AccessFeature PaperArticle

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

1
Christian Doppler Laboratory for Manufacturing Process based Component Design, Chair of Mechanical Engineering, Montanuniversität Leoben, Franz-Josef-Straße 18, 8700 Leoben, Austria
2
Chair of Mathematics and Statistics, Montanuniversität Leoben, Franz-Josef-Straße 18, 8700 Leoben, Austria
*
Author to whom correspondence should be addressed.
Metals 2020, 10(5), 616; https://doi.org/10.3390/met10050616
Received: 20 March 2020 / Revised: 30 April 2020 / Accepted: 5 May 2020 / Published: 9 May 2020
(This article belongs to the Special Issue Technological Aspects in Fatigue Design of Metallic Structures)
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 a 1 and a 2 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 ≤ S v ≤ 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 S v i and equivalent notch root radius ρ ¯ i . Summing up, a statistically driven fatigue strength assessment tool of sand cast aluminium surfaces has been developed and features an engineering friendly design method. View Full-Text
Keywords: cast aluminium; fatigue strength assessment; surface layer porosity; areal roughness parameter; hot isostatic pressing; extreme value statistics; probabilistic long life fatigue strength cast aluminium; fatigue strength assessment; surface layer porosity; areal roughness parameter; hot isostatic pressing; extreme value statistics; probabilistic long life fatigue strength
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MDPI and ACS Style

Pomberger, S.; Oberreiter, M.; Leitner, M.; Stoschka, M.; Thuswaldner, J. Probabilistic Surface Layer Fatigue Strength Assessment of EN AC-46200 Sand Castings. Metals 2020, 10, 616.

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