Damage mode classification in CFRP laminates using convolutional autoencoder and convolutional neural network on acoustic emission waveforms

The acoustic emission (AE) technique is a widely used nondestructive method for in-situ health monitoring of composite structures. Unlike metals, failure mechanisms in composite structures are complex, involving multiple damage modes, and each damage mode has a distinct AE signature. This work uses deep learning algorithms called convolutional autoencoder (CAE) and convolutional neural network (CNN) to classify damage modes in carbon fiber-reinforced polymer laminates using AE waveforms. Tensile experiments are carried out on laminates of various stacking sequences, and the acquired raw AE waveforms are transformed into time-frequency planes called spectrograms using short-time Fourier transform. CAE is used for retrieving deep features associated with damage modes in the latent space from these spectrograms. Subsequently, k -means is used to cluster the deep features in the latent space. Each cluster is labeled with a damage mode by inspecting their damage signatures using the scalograms. This labeled data is then used to train the CNN. The CNN, once trained is used on the AE data of pristine and notched quasi-isotropic specimens, and its ability to classify and identify the damage modes is investigated. The trained CNN achieves satisfactory classification accuracy of 96.9% on pristine quasi-isotropic specimen data and 96.4% on notched quasi-isotropic specimen data. When compared to the prediction accuracy of pure damage modes, the prediction accuracy of mixed-mode damage is slightly lower, at 92.5% for pristine and 91.3% for notched quasi-isotropic specimens. This reduction in accuracy is due to the spectrograms of mixed-mode damage containing energy distributed across multiple frequency bands. By classifying the AE waveforms of both pristine and notched quasi-isotropic specimens, the progression of different damage modes is analyzed through the cumulative count of AE waveforms associated with each damage type. These findings enhance the understanding of damage mode evolution in composite structures and contribute to structural health monitoring studies.