Key points are not available for this paper at this time.
The present research exploits various physically explainable features for the evaluation of electromechanical impedance (EMI) measurements for sandwich face layer debonding identification using a recently published two-step physics- and machine-learning (ML)-based approach. In the previous work, the ML approach used employs a One-Class Support Vector Machine and a K-Nearest Neighbor model for debonding detection and size estimation, respectively. Feature engineering was used to compensate for the simplifications of the finite element (FE)-based physical model and a simple data calibration step was used to adapt to distribution shifts. This enabled to train both ML models exclusively with FE simulation-based synthetic EMI spectra data. The efficacy of the method was demonstrated on real-world EMI spectra measurements of a circular aluminum sandwich panel by a piezoelectric transducer. The considered face layer debonding damage was idealized and stepwise increased by a milling process. In the present work, we explore opportunities for further optimization of our method with respect to data distillation, where we reduce the computational requirements of both training and inference while at the same time preserving essential information. Furthermore, we investigate the usefulness of a separate approach for debonding detection, formulated as an anomaly detection problem. The data preprocessing and ML models are validated by unseen real-world EMI measurement data and benchmarked with the previously published damage evaluation results, considering both reliability and accuracy. The data and the code used in this study are provided in their entirety to enable reproducibility, enhance comprehensibility, and encourage future research.
Kralovec et al. (Sat,) studied this question.