Detecting co-existing deformation mechanisms in metals is crucial for early damage alerts. Here, authors develop a knowledge-driven unsupervised learning approach that identifies co-existing mechanisms from acoustic emission signals without labelled data.
- Boyuan Gou
- Yan Chen
- Xiangdong Ding
