Machine Learning-Assisted Estimation of Interfacial Properties from Acoustic Emission Features During Microdroplet Pull-Out Tests

Evaluation of fiber–matrix interfacial properties is essential for understanding composite performance and exploring the feasibility of real-time diagnostic approaches. In this study, the interfacial behavior between glass fiber and epoxy resin was examined using acoustic emission (AE) features obtained during microdroplet pull-out tests. Four AE features (amplitude, energy, rise time, and Fast Fourier transform peak frequency) were used as input variables to Random Forest models for both regression and classification tasks, targeting interfacial shear strength estimation and failure mode identification (interfacial debonding vs. fiber fracture). In regression analysis, energy and amplitude showed stronger associations with interfacial shear strength, although overall regression performance remained limited. In classification analysis, amplitude alone provided the most stable discrimination between fiber fracture and interfacial debonding, while combining multiple features offered only a marginal additional benefit due to feature redundancy. These results suggest that intensity-related AE parameters are closely associated with interfacial debonding behavior and failure modes. Overall, this exploratory study indicates that AE-based machine learning can serve as a supplementary tool for indirect and trend-level assessment of fiber–matrix interfacial behavior, with potential relevance to real-time monitoring applications.