Microstructure-sensitive weighted probability approach for modeling surface to bulk transition of high cycle fatigue failures dominated by primary inclusions.

摘要

The mechanical alloying and casting processes used to make polycrystalline metallic materials often introduce undesirable non-metallic inclusions and pores. These are often the dominant sites of fatigue failure origination at the low stress amplitudes that correspond to the high cycle fatigue (HCF) and very high cycle fatigue (VHCF) regimes, in which the number of cycles to crack initiation is more than 106.;HCF and VHCF experiments on some advanced metallic alloys, such as powder metallurgy Ni-base superalloys, titanium alloys, and high-strength steels have shown that the critical inclusions and pores can appear on the surface as well as in the bulk of the specimen. Fatigue lives have been much higher for specimens that fail from a bulk site.;The relative number of bulk initiations increases as the stress amplitude decreases such that just below the traditional HCF limit, fatigue life data appears to be evenly scattered between two datasets corresponding to surface and bulk initiations. This is often referred to as surface to bulk transition in the VHCF regime. Below this transition stress, the likelihood of surface versus bulk initiation significantly impacts the low failure probability estimate of fatigue life. Under these circumstances, a large number of very costly experiments need to be conducted to obtain a statistically representative distribution of fatigue life and to predict the surface versus bulk initiation probability.;In this thesis, we pursue a simulation-based approach whereby microstructure-sensitive finite element simulations are performed within a statistical construct to examine the VHCF life variability and assess the surface initiation probability. The methodology introduced in this thesis lends itself as a cost-effective platform for development of microstructure-property relations to support design of new or modified alloys, or to more efficiently predict the properties of existing alloys.