Unsupervised learning of nanoindentation data to infer microstructural details of complex materials
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...
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| Format: | Article |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Materials |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2024.1440608/full |
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| author | Chen Zhang Clémence Bos Stefan Sandfeld Stefan Sandfeld Ruth Schwaiger Ruth Schwaiger |
| author_facet | Chen Zhang Clémence Bos Stefan Sandfeld Stefan Sandfeld Ruth Schwaiger Ruth Schwaiger |
| author_sort | Chen Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9bc6ac921a0443c8b503b3467fa63c18 |
| institution | Kabale University |
| issn | 2296-8016 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj-art-9bc6ac921a0443c8b503b3467fa63c182024-12-04T06:46:10ZengFrontiers Media S.A.Frontiers in Materials2296-80162024-12-011110.3389/fmats.2024.14406081440608Unsupervised learning of nanoindentation data to infer microstructural details of complex materialsChen Zhang0Clémence Bos1Stefan Sandfeld2Stefan Sandfeld3Ruth Schwaiger4Ruth Schwaiger5Institute for Advanced Simulation – Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, Jülich, GermanyInstitute for Applied Materials, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyInstitute for Advanced Simulation – Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, Jülich, GermanyChair of Materials Data Science and Informatics, Faculty of Georesources and Materials Engineering, RWTH Aachen University, Aachen, GermanyInstitute of Energy and Climate Research – Structure and Function of Materials (IEK-2), Forschungszentrum Jülich GmbH, Jülich, GermanyChair of Energy Engineering Materials, Faculty of Georesources and Materials Engineering, RWTH Aachen University, Aachen, GermanyIn 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.https://www.frontiersin.org/articles/10.3389/fmats.2024.1440608/fullunsupervised learningcross-validationGaussian mixture modelCu-Cr compositemechanical propertiesnanoindentation |
| spellingShingle | Chen Zhang Clémence Bos Stefan Sandfeld Stefan Sandfeld Ruth Schwaiger Ruth Schwaiger Unsupervised learning of nanoindentation data to infer microstructural details of complex materials Frontiers in Materials unsupervised learning cross-validation Gaussian mixture model Cu-Cr composite mechanical properties nanoindentation |
| title | Unsupervised learning of nanoindentation data to infer microstructural details of complex materials |
| title_full | Unsupervised learning of nanoindentation data to infer microstructural details of complex materials |
| title_fullStr | Unsupervised learning of nanoindentation data to infer microstructural details of complex materials |
| title_full_unstemmed | Unsupervised learning of nanoindentation data to infer microstructural details of complex materials |
| title_short | Unsupervised learning of nanoindentation data to infer microstructural details of complex materials |
| title_sort | unsupervised learning of nanoindentation data to infer microstructural details of complex materials |
| topic | unsupervised learning cross-validation Gaussian mixture model Cu-Cr composite mechanical properties nanoindentation |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2024.1440608/full |
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