# Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP
## 1 Introduction
Recent studies have advanced the understanding of damage progression in adhesively bonded composite joints using AE techniques. [PERSON] et al. demonstrated the effectiveness of AE in the real-time monitoring of CFRP joints, employing wavelet decomposition and hierarchical clustering to categorize damage mechanisms [27]. [PERSON] et al. combined AE with self-sensing carbon nanotube networks to monitor glass fiber/epoxy composites, using k-means and principal component analysis to identify various failure modes [28]. [PERSON] et al. applied AE to study single-lap composite joints with adhesive defects, revealing correlations between AE parameters and damage processes [29]. [PERSON] et al. utilized AE to assess pre- and post-damage growth in single adhesive lap joints, evaluating strain energy release rates and sound amplitude dissipation [30]. These studies collectively highlight AE's capability to provide real-time insights into damage initiation, propagation, and classification in composite structures, offering valuable tools for structural health monitoring. AE descriptor-based methods still require significant human intervention, complicating damage characterization in CFRP composites [31; 32]. Various AE descriptors, includingpeak amplitude, frequency, cumulative acoustic energy, and counts, have been employed to assess damage modes [33]. However, the effectiveness of these descriptors can be limited by factors such as material properties, sample dimensions, and sensor characteristics, as highlighted by [PERSON] et al. in their study, where they demonstrated how these variables influence the accuracy of damage assessment using AE signals [34]. Recent advancements have focused on enhancing damage characterization through innovative techniques like Laplacian scores for feature selection and k-means clustering, which help establish relationships between AE parameters [35]. Additionally, approaches such as principal component analysis (PCA) combined with frequency response function (FRF) have demonstrated potential for improving damage sensitivity and clustering outcomes in plate-like structures [36]. However, a clear research gap exists in automating the extraction of damage-sensitive features from AE signals. Automating this process would enhance the efficiency and accuracy of differentiating damage modes, enabling more precise damage detection with minimal human intervention. These advancements emphasize the need for refined methods that can fully leverage AE signals for real-time, efficient monitoring of structural integrity in complex composite materials. This study introduces a framework that combines a deep autoencoder with AE techniques to automate the differentiation of damage modes in bonded CFRP composites. The proposed approach integrates deep learning with conventional AE methods, enhancing the monitoring of damage evolution. Mechanical tests on CFRP specimens in a joggled lap shear configuration are analyzed using this framework, which includes k-means clustering, Hilbert spectrum analysis (HHT), and Fast Fourier Transform (FFT). A comparative analysis highlights the limitations of the conventional descriptor-based approach versus the new framework.