An approach to modeling the S-N behavior of bulk-metallic glasses

Probability and statistics analyses are increasingly being used for reliability and durability assessments for life predictions of advanced material systems. Fatigue-life predictions have historically been based on crack-growth approaches, which are almost exclusively empirically based. Consequently, they often do not adequately reflect long-term operating conditions, which are well beyond laboratory test conditions. These models usually fail to identify the sources of the randomness and the extent of their contributions to the total variability. Using a simple crack-growth model, the variability inherent in the stress vs. fatigue-life (S-N) response for bulk-metallic glasses (BMGs) can be related to some of the key random variables that are readily identified in the models. The identification and quantification of these variables are paramount for predicting fatigue lives and reaching some understanding of the fundamental damage growth mechanisms in BMGs. The effectiveness of the modeling is shown through the analyses of a set of S-N data for BMGs where the variability associated with the material chemistry and structure and specimen preparation is considered.