Classification and Parameter Selection for Damage Characterization in CFRP Composite Materials Using Acoustic...

Accurate damage characterization in thermoset Carbon Fiber-Reinforced Polymer (CFRP) composites using Acoustic Emission (AE) requires statistically robust and interpretable models. This study employs multinomial logistic regression with forward selection and Type III analysis to identify the minimal set of AE parameters necessary for classifying damage mechanisms (fiber breaks, delamination, matrix cracks) in quasi-isotropic thermoset CFRP laminates under synchronously recorded load conditions. Starting from 18 conventional time- and frequency-domain descriptors, forward selection yielded seven candidate predictors. However, Type III analysis revealed that only four parameters, Load, Initiation Frequency, Amplitude, and Average Frequency, provide unique, statistically significant contributions (p < 0.05). The remaining predictors became redundant once these four were included. Machine learning and deep learning models trained on this minimal feature set achieved validation accuracies up to 98.7% on external specimens. High-frequency components (>1 MHz), as recorded at the sensor location after propagation and sensor convolution, were associated with fiber break events at elevated loads, while delamination events exhibited higher amplitude and lower-frequency content (<200 kHz) compared to matrix crack events. These observed frequency ranges reflect the combined effects of source mechanisms, guided wave dispersion in the 2.4 mm thick laminate, PWAS sensor response, and HDT-based hit segmentation, and are consistent with established AE damage signatures in literature. The results indicate that this four-parameter set is sufficient to classify the labeled AE waveform classes under monotonic tensile loading of quasi-isotropic [45/90/-45/0]_2s laminates, achieving 98.7% agreement with reference labels assigned via waveform morphology and spectral analysis. The proposed approach reduces computational overhead and enhances interpretability for structural health monitoring applications, pending validation across broader material systems and loading scenarios. A limitation of this study is that reference labels were assigned using waveform morphology and spectral analysis, lacking independent physical validation (e.g., microscopy).