[SY-L3] Symposium L-3

[SY-L3] Nanoindentation in the ultra-nano scale: Microstructure-property relationships using statistical approaches

Hengxu Song^1,2, Ryder Bolin¹, Michael Tzimas¹, ^○Stefanos Papanikolaou^1,2 (1.west virginia university, United States of America, 2.johns hopkins university, United States of America)

Due to the difficulties of tensile/compressive tests at small length scales, nanoindentation is widely used towards unveiling crystalline mechanical properties. However, crystal plasticity limits the understanding of nanoindentation results at depths below 500nm: the Indentation Size Effect (ISE) in these scales leads to very noisy and unclear data, with the measured hardness/stiffness being difficult to ‘translate’ into features of the material microstructure. In this work, we demonstrate two statistical approaches to investigate the ultra-nano regime of FCC metals towards unveiling crystalline properties: First, we notice that indentation together with in-plane tension consist of a phase diagram of the sample elasto-plastic property. The elastic-plastic transition during indentation is naturally continuous for large dislocation densities. In the large dislocation density regime, through the development of scaling functions for an appropriately defined plasticity order parameter, we connect statistically the bulk crystal plasticity transition with nanoindentation. Second, for low dislocation densities, we utilize the noise of the load-depth nanoindentation curves and the post-indent nanoindentation images to statistically connect experiments with 3D discrete dislocation dynamics simulations. We develop a machine-learning approach that can be used for nanoindentation and predict experimentally relevant pre-existing dislocation densities in single crystalline FCC grains.