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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.03328 |
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| _version_ | 1866916920193712128 |
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| author | Pomes, S. Suzuki, T. Enokizono, T. Adachi, N. Wakeda, M. Ohmura, T. |
| author_facet | Pomes, S. Suzuki, T. Enokizono, T. Adachi, N. Wakeda, M. Ohmura, T. |
| contents | Small-scale plasticity and creep behavior of a Zr-based BMG were investigated using nanoindentation. Four load functions, differing only in hold times of 0, 10, 30, and 60 seconds at peak load, were applied. Results indicate spatially heterogeneous and time-sensitive plastic behavior. Machine learning clustering, based on hardness and creep displacement, suggested three clusters. Statistical analysis of plastic energy distributions enabled identification of potential deformation mechanisms within the clusters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03328 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Metallic glasses heterogeneous and time sensitive small scale plasticity probed through nanoindentation and machine learning clustering Pomes, S. Suzuki, T. Enokizono, T. Adachi, N. Wakeda, M. Ohmura, T. Materials Science Small-scale plasticity and creep behavior of a Zr-based BMG were investigated using nanoindentation. Four load functions, differing only in hold times of 0, 10, 30, and 60 seconds at peak load, were applied. Results indicate spatially heterogeneous and time-sensitive plastic behavior. Machine learning clustering, based on hardness and creep displacement, suggested three clusters. Statistical analysis of plastic energy distributions enabled identification of potential deformation mechanisms within the clusters. |
| title | Metallic glasses heterogeneous and time sensitive small scale plasticity probed through nanoindentation and machine learning clustering |
| topic | Materials Science |
| url | https://arxiv.org/abs/2508.03328 |