Work

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

Year: 2023

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2309.06613

Authors: Zhang, Chen, Bos, Clémence, Sandfeld, Stefan, Schwaiger, Ruth

Institutions: Forschungszentrum Jülich, Karlsruhe Institute of Technology, RWTH Aachen University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4386793418

Cites:

Cited by: 1

Related to: 10