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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61707-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|