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Article

A Probabilistic Fatigue Strength Assessment in AlSi-Cast Material by a Layer-Based Approach

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 Mechanical Engineering, Montanuniversität Leoben, Franz-Josef-Straße 18, 8700 Leoben, Austria
3
Institute of Structural Durability and Railway Technology, Graz University of Technology, Inffeldgasse 25/D, 8010 Graz, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Ricardo Branco
Metals 2022, 12(5), 784; https://doi.org/10.3390/met12050784
Received: 8 March 2022 / Revised: 25 April 2022 / Accepted: 26 April 2022 / Published: 30 April 2022
An advanced lightweight design in cast aluminium alloys features complexly shaped geometries with strongly varying local casting process conditions. This affects the local microstructure in terms of porosity grade and secondary dendrite arm spacing distribution. Moreover, complex service loads imply changing local load stress vectors within these components, evoking a wide range of highly stressed volumes within different microstructural properties per load sequence. To superimpose the effects of bulk and surface fatigue strength in relation to the operating load sequence for the aluminium alloy EN AC 46200, a layer-based fatigue assessment concept is applied in this paper considering a non-homogeneous distribution of defects within the investigated samples. The bulk fatigue property is now obtained by a probabilistic evaluation of computed tomography results per investigated layer. Moreover, the effect of clustering defects of computed tomography is studied according to recommendations from the literature, leading to a significant impact in sponge-like porosity layers. The highly stressed volume fatigue model is applied to computed tomography results. The validation procedure leads to a scattering of mean fatigue life from −2.6% to 12.9% for the investigated layers, inheriting strongly varying local casting process conditions. View Full-Text
Keywords: aluminium casting; local fatigue assessment; shrinkage porosity; probability distribution; extreme value statistics; computed tomography aluminium casting; local fatigue assessment; shrinkage porosity; probability distribution; extreme value statistics; computed tomography
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MDPI and ACS Style

Oberreiter, M.; Fladischer, S.; Stoschka, M.; Leitner, M. A Probabilistic Fatigue Strength Assessment in AlSi-Cast Material by a Layer-Based Approach. Metals 2022, 12, 784. https://doi.org/10.3390/met12050784

AMA Style

Oberreiter M, Fladischer S, Stoschka M, Leitner M. A Probabilistic Fatigue Strength Assessment in AlSi-Cast Material by a Layer-Based Approach. Metals. 2022; 12(5):784. https://doi.org/10.3390/met12050784

Chicago/Turabian Style

Oberreiter, Matthias, Stefan Fladischer, Michael Stoschka, and Martin Leitner. 2022. "A Probabilistic Fatigue Strength Assessment in AlSi-Cast Material by a Layer-Based Approach" Metals 12, no. 5: 784. https://doi.org/10.3390/met12050784

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