TY - JOUR

T1 - Probabilistic Surface Layer Fatigue Strength Assessment of EN AC-46200 Sand Castings

AU - Pomberger, Sebastian

AU - Oberreiter, Matthias

AU - Leitner, Martin

AU - Stoschka, Michael

AU - Thuswaldner, Jörg

N1 - Funding Information: Funding: This research was funded by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development. Funding Information: Acknowledgments: The financial support by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development is gratefully acknowledged. Furthermore, the authors would like to thank the industrial partners BMW AG and Nemak Dillingen GmbH for the excellent mutual scientific cooperation within the CD-laboratory framework. Special thanks go to Michael Simon Pegritz, coworker within the Christian Doppler Laboratory for Manufacturing Process based Component Design, for supporting the developement of the microstructural characterisation. Publisher Copyright: © 2020 by the authors.

PY - 2020/5/9

Y1 - 2020/5/9

N2 - 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 a1 and a2 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 ≤ Sv ≤ 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 Svi and equivalent notch root radius ρi . Summing up, a statistically driven fatigue strength assessment tool of sand cast aluminium surfaces has been developed and features an engineering friendly design method.

AB - 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 a1 and a2 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 ≤ Sv ≤ 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 Svi and equivalent notch root radius ρi . Summing up, a statistically driven fatigue strength assessment tool of sand cast aluminium surfaces has been developed and features an engineering friendly design method.

KW - cast aluminium

KW - fatigue strength assessment

KW - surface layer porosity

KW - Areal roughness parameter

KW - Hot isostatic pressing

KW - extreme value statistics

KW - probabilistic long life fatigue strength

UR - http://www.scopus.com/inward/record.url?scp=85090253618&partnerID=8YFLogxK

U2 - 10.3390/met10050616

DO - 10.3390/met10050616

M3 - Article

VL - 10.2020

SP - 616

JO - Metals : open access journal

JF - Metals : open access journal

SN - 2075-4701

IS - 5

M1 - 616

ER -