@misc{10481/107790,
year = {2025},
month = {12},
url = {https://hdl.handle.net/10481/107790},
abstract = {Delamination will inevitably occur during the fatigue process in composites, and its shape contains substantial
information like the area and center, which is useful for prognostics. However, developing a deep learning-based
shape prognostic model typically requires a comprehensive dataset of delamination images, which is difficult to
obtain in practical scenarios. To deal with the limited data availability, this paper proposes a novel data
augmentation (DA)-aided shape prognostic solution. First, the delamination shape is discretized into certain key
lengths, which are then processed through data augmentation to provide synthetic data. Next, a recursive
prognostic model is built with both original and synthetic data and then used to project future shapes with one
single observed image only. The proposed framework is demonstrated by experimental tests of fatigue delamination growth in composites with ultrasonics C-scan monitoring.},
organization = {European Union’s Horizon Europe – Marie Skłodowska-Curie Actions (Grant 101168725)},
publisher = {Elsevier},
keywords = {Structural Health Monitoring},
keywords = {Composites},
keywords = {Delamination shape prognostics},
title = {Data augmentation-aided fatigue delamination shape prognostics in composites},
doi = {10.1016/j.compstruct.2025.119748},
author = {Yuan, Jing and Wang, Xi Vicent and Wang, Lihui and Chiachío Ruano, Manuel and Chiachío Ruano, Juan and Gao, Jianmin and Li, Tianzhi},
}