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On the Statistical Size Effect of Cast Aluminium

Christian Doppler Laboratory for Process based Component Design, 8700 Leoben, Austria
Author to whom correspondence should be addressed.
Materials 2019, 12(10), 1578;
Received: 23 April 2019 / Revised: 6 May 2019 / Accepted: 9 May 2019 / Published: 14 May 2019
(This article belongs to the Special Issue Probabilistic Mechanical Fatigue and Fracture of Materials)
PDF [2422 KB, uploaded 17 May 2019]


Manufacturing process based imperfections can reduce the theoretical fatigue strength since they can be considered as pre-existent microcracks. The statistical distribution of fatigue fracture initiating defect sizes also varies with the highly-stressed volume, since the probability of a larger highly-stressed volume to inherit a potentially critical defect is elevated. This fact is widely known by the scientific community as the statistical size effect. The assessment of this effect within this paper is based on the statistical distribution of defect sizes in a reference volume V 0 compared to an arbitrary enlarged volume V α . By implementation of the crack resistance curve in the Kitagawa–Takahashi diagram, a fatigue assessment model, based on the volume-dependent probability of occurrence of inhomogeneities, is set up, leading to a multidimensional fatigue assessment map. It is shown that state-of-the-art methodologies for the evaluation of the statistical size effect can lead to noticeable over-sizing in fatigue design of approximately 10 % . On the other hand, the presented approach, which links the statistically based distribution of defect sizes in an arbitrary highly-stressed volume to a crack-resistant dependent Kitagawa–Takahashi diagram leads to a more accurate fatigue design with a maximal conservative deviation of 5 % to the experimental validation data. Therefore, the introduced fatigue assessment map improves fatigue design considering the statistical size effect of lightweight aluminium cast alloys. View Full-Text
Keywords: cast aluminium; fatigue assessment; shrinkage pores; statistical distribution; extreme value statistics; highly-stressed volume cast aluminium; fatigue assessment; shrinkage pores; statistical distribution; extreme value statistics; highly-stressed volume

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Aigner, R.; Pomberger, S.; Leitner, M.; Stoschka, M. On the Statistical Size Effect of Cast Aluminium. Materials 2019, 12, 1578.

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