Inference and Prediction of Nanoindentation Response in FCC Crystals: Methods and Discrete Dislocation Simulation Examples

TL;DR

This paper uses discrete dislocation dynamics simulations and machine learning to predict nanoindentation responses in FCC crystals, linking dislocation density and orientation to surface responses for microstructural analysis.

Contribution

It introduces a novel method combining simulations and machine learning to classify and predict nanoindentation responses based on dislocation density and orientation.

Findings

Simulation results agree qualitatively with experimental data.

Machine learning enables one-to-one mapping between dislocation density and indentation response.

Statistical classification predicts local surface properties non-destructively.

Abstract

Nanoindentation, a common technique for probing the mechanical properties of crystalline materials, exhibits both surface and bulk-dominated responses that are linked to the parent crystal's elasticity and plasticity. For FCC crystals, the nanoindentation response is primarily controlled by the indented-grain's crystalline orientation and dislocation density. However, the indentation properties, such as hardness, have ambiguous significance at the nanoscale due to tip-specific size effects. Through extensive discrete dislocation dynamics simulations of flat-punch nanoindentation on FCC single crystals, we show that a one-to-one correspondence between an indented location and the pre-existing dislocation density is possible with the help of statistical, machine-learning algorithms. We cluster and classify various experimentally plausible ensembles with varying dislocation density and/or crystalline orientation, using nanoindentation force-depth curves or post-indent surface displacement images. We use this classification to statistically predict the nanoindentation response of a given location, through the average response of classified load-displacement curves. In addition, we demonstrate that our simulation results qualitatively agree with experimental data on common FCC single crystals. Ultimately, we propose a novel pathway to uncover the depth dependence of local surface material properties for more complete, non-destructive, microstructural characterization, through nanoindentation.