Unsupervised Learning of Nanoindentation Data to Infer Microstructural Details of Complex Materials

arXiv (Cornell University)

Abstract

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.

Authors 4

1. Chen Zhang

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulation -Materials Data Science and Informatics (IAS-9) , Forschungszentrum J ülich GmbH , J ülich , Germany

2. Clémence Bos

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Applied Materials , Karlsruhe Institute of Technology (KIT) , Eggenstein-Leopoldshafen , Germany

3. Stefan Sandfeld Aachen Chair of Materials Data Science and Informatics Faculty of Georesources and Materials Engineering

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Chair of Materials Data Science and Informatics , Faculty of Georesources and Materials Engineering , RWTH Aachen University , Aachen , Germany

   Institute for Advanced Simulation -Materials Data Science and Informatics (IAS-9) , Forschungszentrum J ülich GmbH , J ülich , Germany

4. Ruth Schwaiger Aachen Chair of Energy Engineering Materials Faculty of Georesources and Materials Engineering

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Chair of Energy Engineering Materials , Faculty of Georesources and Materials Engineering , RWTH Aachen University , Aachen , Germany

   Institute of Energy and Climate Research -Structure and Function of Materials (IEK-2) , Forschungszentrum J ülich GmbH , J ülich , Germany