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...
Gorde:
| Egile Nagusiak: | , , , , , , |
|---|---|
| Formatua: | Artigo |
| Hizkuntza: | Inglês |
| Argitaratua: |
Nature Portfolio
2025-07-01
|
| Saila: | Nature Communications |
| Sarrera elektronikoa: | https://doi.org/10.1038/s41467-025-61707-z |
| Etiketak: |
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
|