Unsupervised Learning of Nanoindentation Data to Infer Microstructural Details of Complex Materials
This work addresses a common but challenging issue in materials science for researchers by providing a method to analyze complex material data and estimate required data quantities, though it is incremental as it applies an existing technique to a specific domain.
The study tackled the problem of inferring microstructural details from nanoindentation data on Cu-Cr composites by using a Gaussian mixture model for unsupervised learning, resulting in the determination of mechanical phases and properties, with cross-validation to assess data adequacy.
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.