Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP

This study investigates the classification of damage modes in adhesively bonded carbon fiber-reinforced plastic (CFRP) composites, a critical factor in advancing lightweight automotive design. Adhesive bonding, replacing traditional riveting, improves structural integrity while reducing weight and CO2 emissions. Mechanical testing on CFRP composites was performed, and acoustic emission (AE) signals were collected to evaluate damage mechanisms. A deep autoencoder (DAE) framework was developed to automate damage characterization by reducing AE signal dimensionality through singular value decomposition (SVD) and classifying features using the k-means algorithm. This approach effectively identified three primary damage modes: matrix cracking, interfacial debonding, and fiber breakage. Traditional AE features, such as entropy and amplitude were also classified and validated using spectral analysis. The DAE-based strategy demonstrated superior capability in real-time damage mode differentiation. Fractographic analysis confirmed crack growth in the adhesive layer, leading to interfacial debonding, fiber-matrix separation, and eventual fiber rupture. These findings highlight the DAE framework’s effectiveness in enhancing damage mode characterization, offering valuable insights for optimizing the structural performance of bonded CFRP composites in automotive applications.