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As the need for miniaturized structural and functional materials hasincreased,the need for precise materials characterizaton has also expanded.Nanoindentation is a popular method that can be used to measure materialmechanical behavior which enables high-throughput experiments and, in somecases, can also provide images of the indented area through scanning. Bothindenting and scanning can cause tip wear that can influence the measurements.Therefore, precise characterization of tip radii is needed to improve dataevaluation. A data fusion method is introduced which uses finite elementsimulations and experimental data to estimate the tip radius in situ in ameaningful way using an interpretable multi-fidelity deep learning approach. Byinterpreting the machine learning models, it is shown that the approaches areable to accurately capture physical indentation phenomena.

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