Modification of a Defect-Based Fatigue Assessment Model for Al-Si-Cu Cast Alloys

Aigner, Roman; Leitner, Martin; Stoschka, Michael; Hannesschläger, Christian; Wabro, Thomas; Ehart, R. J. A.

doi:10.3390/ma11122546

Cited by 17 publications

(14 citation statements)

References 64 publications

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“…In terms of porosity effect on the cast component fatigue strength various studies have been conducted, thoroughly investigating crack growth behaviour [2][3][4][5] as well as statistical description taking into account the effect of pore size, location and shape [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Therein, fatigue assessment concepts like the √ area-approach of Murakami [22], or threshold-based concepts like the concept of Kitagawa and Takahashi [23], are frequently applied due to their engineering feasible applicability.…”

Section: Introductionmentioning

confidence: 99%

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

Pomberger

Oberreiter

Leitner

et al. 2020

Metals

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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.

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Section: Introductionmentioning

confidence: 99%

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

Pomberger

Oberreiter

Leitner

et al. 2020

Metals

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“…Fatigue is one of the main failure forms of engineering components of aluminum alloys [7,8]. The structural components of Al-Si series alloys work under extremely severe and complex conditions such as high stress [9,10], and the material will be in a state of plastic strain, resulting in low-cycle fatigue failure of the material [11,12]. In order to ensure the safety of components in service, it is necessary to perform a deep investigation of the fatigue characteristics of materials.…”

Section: Introductionmentioning

confidence: 99%

Microstructure and Low-Cycle Fatigue Behavior of Al-9Si-4Cu-0.4Mg-0.3Sc Alloy with Different Casting States

et al. 2020

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The low-cycle fatigue behavior of Al-9Si-4Cu-0.4Mg-0.3Sc alloy with different casting states was investigated by performing low-cycle fatigue tests and by means of observations and analysis with a scanning electron microscope (SEM) and a transmission electron microscope (TEM). It was found that the metal-mold cast and die-cast Al-9Si-4Cu-0.4Mg-0.3Sc alloys exhibited the cyclic stress response of strain hardening under all imposed total strain amplitudes. The cyclic deformation resistance and fatigue life of the metal-mold cast Al-9Si-4Cu-0.4Mg-0.3Sc alloy were lower than those of the die-cast Al-9Si-4Cu-0.4Mg-0.3Sc alloy. The plastic strain and elastic strain amplitudes of the metal-mold cast and die-cast Al-9Si-4Cu-0.4Mg-0.3Sc alloys were linearly related to the number of reversals to failure, which obeyed the Coffin-Manson and Basquin formulas, respectively. The results of TEM observation revealed that at all imposed total strain amplitudes, the cyclic deformation mechanisms of the metal-mold cast and die-cast Al-9Si-4Cu-0.4Mg-0.3Sc alloys were planar slip and wavy slip at the lower and higher strain amplitudes, respectively.

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“…Since a preliminary study in [56] revealed that the area parameter correlates well with the stress field around the inhomogeneity, this parameter is further utilized in this work for the estimation of defect sizes. The defect size measurement methodology invoked in this work is in detail described in [57,58,59]. According to investigations in [60,61], the statistical distribution of defect sizes can be characterized by the limiting extreme value distribution of the Lognormal (LN) distribution, namely the Gumbel or Extreme Value type one distribution (EV) [62,63].…”

Section: Introductionmentioning

confidence: 99%

On the Statistical Size Effect of Cast Aluminium

et al. 2019

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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.

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Modification of a Defect-Based Fatigue Assessment Model for Al-Si-Cu Cast Alloys

Cited by 17 publications

References 64 publications

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

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

Microstructure and Low-Cycle Fatigue Behavior of Al-9Si-4Cu-0.4Mg-0.3Sc Alloy with Different Casting States

On the Statistical Size Effect of Cast Aluminium

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