The fatigue strength of cast aluminium alloys is known to be greatly affected by different defect types related to the manufacturing process, particularly microshrinkage pores created during the solidification phase of the casting process. Even if certain classification procedures are given in the standard ASTM E155-15 , the presence of defects is not readily related to the capacity of a component or a structure to meet the requirements of the mechanical technical specifications. The present study aims at establishing a clear link between certain microstructural features and the average fatigue strength. This is possible by looking for the average size of critical defects and using a relevant statistical analysis. More exactly, the Murakami approach based on the statistics of extremes is employed. The main originality of this work lies in the application of this approach to the case of a real structure submitted to high cycle fatigue damage: engine cylinder heads, used in the automotive industry. Indeed, both fatigue tests and microstructural characterizations are carried out on cylindrical specimens and real structures. The specimens are subjected to uniaxial and multiaxial loading conditions . Original fatigue tests, developed by PSA to load in-service critical regions, are carried out on cylinder heads. Systematic analyses of fatigue failure surface are conducted to obtain the statistics of critical defects at the origin of the failures for both specimens and structures. In parallel, critical regions and the associated local loading mode in the structure are characterized by an appropriate high cycle fatigue analysis. The latter, combined with the fatigue test data and the statistical analysis of the critical defects, leads to a discussion about the size effect and an approach is proposed for a relevant fatigue design procedure.
Osmond, P., Le, V. D., Morel, F., Bellett, D., & Saintier, N. (2018). Effect of porosity on the fatigue strength of cast aluminium alloys: From the specimen to the structure. In Procedia Engineering (Vol. 213, pp. 630–643). Elsevier Ltd. https://doi.org/10.1016/j.proeng.2018.02.059