Metallic glasses heterogeneous and time sensitive small scale plasticity probed through nanoindentation and machine learning clustering

TL;DR

This study investigates the small-scale plasticity and creep behavior of Zr-based bulk metallic glasses using nanoindentation with varying hold times, revealing heterogeneity and time sensitivity, and employs machine learning clustering to identify deformation mechanisms.

Contribution

It introduces a combined nanoindentation and machine learning approach to analyze heterogeneity and time-dependent plasticity in metallic glasses.

Findings

Plastic behavior is spatially heterogeneous and time-sensitive.

Three distinct clusters of deformation mechanisms were identified.

Statistical analysis links energy distributions to specific deformation processes.

Abstract

Small-scale plasticity and creep behavior of a Zr-based BMG were investigated using nanoindentation. Four load functions, differing only in hold times of 0, 10, 30, and 60 seconds at peak load, were applied. Results indicate spatially heterogeneous and time-sensitive plastic behavior. Machine learning clustering, based on hardness and creep displacement, suggested three clusters. Statistical analysis of plastic energy distributions enabled identification of potential deformation mechanisms within the clusters.