| Autor: |
Chen Zhang, Clémence Bos, Stefan Sandfeld, Ruth Schwaiger |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Frontiers in Materials, Vol 11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2296-8016 |
| DOI: |
10.3389/fmats.2024.1440608 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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