Detecting deformation mechanisms of metals from acoustic emission signals through knowledge-driven unsupervised learning
Abstract Timely detection of deformation mechanisms in metallic structural materials is essential for early-warning alerts on potential damages and fractures. Acoustic emission (AE) technologies are commonly used for this purpose due to their non-destructive nature. However, traditional methods ofte...
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| Autores principales: | , , , , , , |
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| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Nature Portfolio
2025-07-01
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| Colección: | Nature Communications |
| Acceso en línea: | https://doi.org/10.1038/s41467-025-61707-z |
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