Stochasticity of fatigue failure times in sheared glasses

TL;DR

This paper investigates the distribution and stochastic nature of fatigue failure times in sheared glasses using simulations and models, revealing that failure time variability diminishes with increasing system size, indicating intrinsic stochasticity.

Contribution

It introduces a detailed analysis of failure time distributions and their size dependence, highlighting the intrinsic stochasticity of fatigue failure in glasses.

Findings

Failure time distribution width decreases with system size

Failure times are proportional to their logarithm's standard deviation

Intrinsic stochasticity drives failure time variability

Abstract

Fatigue failure occurs when a solid is subjected to repeated, cyclic loading. Glasses subjected to cyclic to shear deformation have recently been investigated using computer simulations and theoretical models, to characterize and rationalize the dependence of the number of cycles to failure, depending on the properties of the glasses, and the deformation amplitude. The average number of cycles to failure has been observed to diverge as the strain amplitude approaches the so-called fatigue limit from above. In this work, rather than the average times themselves, we investigate by computer simulations the distribution of fatigue failure times, in model glasses subjected to cyclic shear deformation and in an elasto-plastic model. In particular, we observe in atomistic simulations that the standard deviation of the logarithm of failure times are proportional to their mean values, with the proportionality constant decreasing as the system size increases, indicating a sharper distribution of failure times. Using a finite-element-based elasto-plastic model, we observe similar behavior and perform a system-size analysis showing that the ratio of the standard deviation to the mean tends toward zero in the thermodynamic limit. Such distributions, rather than arising solely from the distribution of disorder in the samples that have been subjected to cyclic deformation, appear to arise from the intrinsic stochasticity of the failure process, which we analyze through a stochastic damage accumulation model.