Abstract
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.
Highlights
For fatigue strength assessment of metallic castings in mechanical engineering the designer has to consider a manufacturing process based on local material properties such as shrinkage pores or surface texture based notches
It is well known that aluminium castings inherit both internal casting defects, shrinkage pores, as well as surface texture related micro and macro notches driven by the surface geometrical structure (SGS), affecting the local fatigue strength
After modification of the concept in order to improve the overall performance, the model was validated by means of the hot isostatic pressing (HIP) P2 cast surface texture (Cast-S) data as well as the remaining two comparably high possibility of observing a mixed (Cast-M) specimens, which have not yet been taken into account for training of the modified neural network
Summary
For fatigue strength assessment of metallic castings in mechanical engineering the designer has to consider a manufacturing process based on local material properties such as shrinkage pores or surface texture based notches. This methodology may be validated even further by additional datasets, which should provide significantly different cast surface textures in order to broaden the applicability of the method towards a wider range of surface roughness values As this areal sand cast surface characterisation method is based on local roughness values, the fatigue designer has to have knowledge about these manufacturing process dependent values. Such as localised information is in general not available for cast surface structures. Statistical characterisation of the sand cast surface texture and subsequently probabilistic evaluation of the manufacturing process related surface fatigue strength as design recommendations of cast components
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