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ORIGINAL RESEARCH article

Front. Mater.
Sec. Structural Materials
Volume 11 - 2024 | doi: 10.3389/fmats.2024.1440608

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

Provisionally accepted
Chen Zhang Chen Zhang 1Clémence Bos Clémence Bos 2Stefan Sandfeld Stefan Sandfeld 1Ruth Schwaiger Ruth Schwaiger 3*
  • 1 Institute for Advanced Simulation, Julich Research Center, Helmholtz Association of German Research Centers (HZ), Jülich, Germany
  • 2 Institute for Applied Materials, Faculty of Mechanical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  • 3 Institute of Energy and Climate Research, Julich Research Center, Helmholtz Association of German Research Centres (HZ), Jülich, Germany

The final, formatted version of the article will be published soon.

    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.

    Keywords: unsupervised learning, Cross-validation, Gaussian mixture model, CuCr composite, Mechanical Properties, nanoindentation

    Received: 29 May 2024; Accepted: 30 Oct 2024.

    Copyright: © 2024 Zhang, Bos, Sandfeld and Schwaiger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ruth Schwaiger, Institute of Energy and Climate Research, Julich Research Center, Helmholtz Association of German Research Centres (HZ), Jülich, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.