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Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid

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Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models.

Claus O W Trost¹, Stanislav Zak¹, Sebastian Schaffer^2,3

¹Erich Schmid Institute of Materials Science, Austrian Academy of Sciences, Jahnstrasse 12, 8700 Leoben, Austria.

JOM (Warrendale, Pa. : 1989)

|May 25, 2022

Summary

This summary is machine-generated.

Accurate nanoindentation measurements require precise tip radius characterization. This study introduces a data fusion method using machine learning to estimate tip radii in situ, improving data evaluation for miniaturized materials.

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Area of Science:

Materials Science
Mechanical Engineering
Computational Science

Background:

Miniaturized materials require precise characterization.
Nanoindentation is a key technique for measuring mechanical properties.
Tip wear in nanoindentation affects measurement accuracy.

Purpose of the Study:

To develop a method for in situ tip radius estimation in nanoindentation.
To improve the accuracy of nanoindentation measurements.
To interpret machine learning models for physical indentation phenomena.

Main Methods:

A data fusion approach combining finite element simulations and experimental data.
Utilizing an interpretable multi-fidelity deep learning model.
In situ estimation of tip radii.

Main Results:

The developed method accurately estimates tip radii in situ.
The interpretable deep learning models capture physical indentation phenomena.
Improved data evaluation for nanoindentation measurements.

Conclusions:

The data fusion and deep learning approach offers a robust solution for tip radius characterization.
This method enhances the reliability of nanoindentation for advanced materials.
Accurate tip radius estimation is crucial for precise materials characterization.