A new approach to characterizing and modeling the high cycle fatigue properties of cast materials based on self-heating measurements under cyclic loadings

The present paper proposes, a new model for high cycle fatigue of metallic cast materials (i.e., containing initial casting micro-flaws). This model is developed in a probabilistic two-scale framework taking into account the presence of a population of initial micro-flaws by an indirect approach. The proposed model not only accounts for the failure of samples under high cycle loadings, but also for the thermal effects during cycling in a unified theoretical framework. Thus, an ad hoc identification procedure, essentially based on self-heating tests under cyclic loadings, is proposed. The performance of the proposed model is estimated by comparing experimental and theoretical results in the case of classical fatigue tests on a cast copper–aluminum alloy. The results are very promising.

► We used temperature measurements to predict the fatigue properties of a cast alloy.
► A probabilistic two-scale model is proposed.
► The proposed model accounts for the fatigue failure of samples and for the thermal effects.
► The fatigue curves predicted exhibit a very good correlation to the fatigue tests.