Unsupervised Learning of Nanoindentation Data to Infer Microstructural
  Details of Complex Materials
Chen Zhang Clémence Bos Stefan Sandfeld Ruth Schwaiger
In this study, Cu-Cr composites were studied by nanoindentation. Arrays of
indents were placed over large areas of the samples resulting in datasets
consisting of several hundred measurements of Young's modulus and hardness at
varying indentation depths. The unsupervised learning technique, Gaussian
mixture model, was employed to analyze the data, which helped to determine the
number of "mechanical phases" and the respective mechanical properties.
Additionally, a cross-validation approach was introduced to infer whether the
data quantity was adequate and to suggest the amount of data required for
reliable predictions -- one of the often encountered but difficult to resolve
issues in machine learning of materials science problems.