# Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP
## 4 Conclusions  
This study focused on characterizing damage modes in adhesively bonded CFRP composites under mechanical loading through the analysis of acoustic emission (AE) signals. A deep autoencoder (DAE)-based framework was introduced to automate the damage characterization process, significantly improving the precision of AE signal analysis. The DAE reduced the dimensionality of the AE signals, extracting latent features via singular value decomposition (SVD). These features were analyzed using the k-means clustering algorithm, which successfully identified three primary damage modes: matrix cracking, interfacial debonding, and fiber breakage. The accuracy of these findings was validated using Hilbert Spectral Analysis (HSA) and Fast Fourier Transform (FFT), further corroborating the effectiveness of the DAE approach.  
A predominant frequency band between 250 kHz and 375 kHz was observed across all specimens. This frequency range reflects the role of the adhesive layer in transmitting the load, with AE signals primarily originating from interfacial crack growth within this layer. The identified damage modes were consistent with fractographic analysis, which revealed crack initiation and growth at the adhesive interface, leading to fiber-matrix separation and adhesive peeling.  
While the study highlighted the effectiveness of both traditional and innovative methods in damage detection, it also underscored the complexity of characterizing CFRP damage. Variations in the frequency content within each damage mode suggest that current methodologies may not fully capture all the damage signatures. This indicates the need for a more integrated approach, combining conventional AE features with advanced analytical techniques to improve damage mode identification.  
The integration of deep learning-based techniques like DAE with traditional acoustic emission methods holds significant potential for enhancing damage characterization in CFRP composites. This hybrid approach will refine the understanding of complex damage modes and improve the accuracy of predictive models, contributing to the optimization of CFRP composites in automotive and other high-performance applications.  
Conceptualization D.K.M.; methodology, C.B., D.K.M. and G.P.; software, D.K.M.; validation, C.B., D.K.M. and G.P.; formal analysis, C.B., D.K.M. and G.P.; investigation, C.B., D.K.M. and G.P.; resources, C.B. and G.P.; data curation, C.B. and D.K.M.; writing--original draft preparation, D.K.M.; writing--review and editing, C.B., D.K.M. and G.P.; visualization, D.K.M.; supervision, G.P. and C.B.; project administration, CC.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
C.B., G.P. and D.K.M. would like to acknowledge the following funding. The project is funded under the National Recovery and Resilience Plan (PNRR), Mission 4 Component C2 Investment 1.1--Call for Proposals for the development of \"Projects of Significant National Interest\" (PRIN)--2 February 2022--D.D. No. 104/2022 PRIN 2022. Award number: 2022}FMPSC--INnovative damage MOnitoring Of Self-HEAling Composites by acoustic emissions in civil and aerospace Applications--IN MOOSHEAD--Grant n. CUP: D53D2300360006--Politecnico di BARI. C.C. would like to acknowledge the following funding. The project is funded under the National Recovery and Resilience Plan (PNRR), Mission 4 Component C2 Investment 1.3--Italian Ministry of University and Research funded by the European Union--NextGenerationEU. Award Number: CNMS named MOST, Concession Decree No. 1033 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP: D93C22000410001, Spoke 11 \"Innovative materials and lightweighting\".  
The data used in the research work is available upon request to the corresponding author.  
The authors declare no conflict of interest.