  As the need for miniaturized structural and functional materials has
increased,the need for precise materials characterizaton has also expanded.
Nanoindentation is a popular method that can be used to measure material
mechanical behavior which enables high-throughput experiments and, in some
cases, can also provide images of the indented area through scanning. Both
indenting and scanning can cause tip wear that can influence the measurements.
Therefore, precise characterization of tip radii is needed to improve data
evaluation. A data fusion method is introduced which uses finite element
simulations and experimental data to estimate the tip radius in situ in a
meaningful way using an interpretable multi-fidelity deep learning approach. By
interpreting the machine learning models, it is shown that the approaches are
able to accurately capture physical indentation phenomena.
