Abstract
We observe two distinct interevent time patterns in the slip avalanches of compressed bulk metallic glasses (BMGs). Small slip avalanches cluster together in time, but large slip avalanches recur roughly periodically. We compare the timing patterns of BMG slip avalanches with timing patterns of earthquakes and with the predictions of a mean-field model. The time clustering of small avalanches is similar to the known time clustering of earthquake foreshocks and aftershocks.
Highlights
Bulk metallic glasses (BMGs) are amorphous alloys with superior mechanical properties compared to many conventional structural materials; they typically break in abrupt brittle failure [1,2,3]
We extract interevent time patterns between these stress drops that demonstrate the occurrence of quasiperiodic large slips, time-clustered small slips, and foreshocks and aftershocks of both large slips and small slips in BMGs
The interevent time patterns of slip avalanches in BMGs are consistent with the interevent time patterns of a simple mean-field model of plastic deformation [25,26]
Summary
Bulk metallic glasses (BMGs) are amorphous alloys with superior mechanical properties compared to many conventional structural materials; they typically break in abrupt brittle failure [1,2,3]. The stress vs time traces were Wiener filtered to reduce high-frequency noise, and the start times (end times) of slip avalanches were identified with local maxima (minima) in the Wiener-filtered stress vs time traces [7]. This avalanche-detection algorithm uses a velocity threshold of 0 MPa/s. The slip statistics of the filtered time-dependent stress were previously shown to agree with 12 different statistical predictions of a simple mean-field model [7]. Large, specimen-spanning avalanches have long incubation periods, with smooth and sharply peaked stress rate vs time profiles for individual events [7,14]. We show that while the large avalanches recur quasiperiodically, the small avalanches cluster together at short timescales and occur randomly at long timescales
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