Acoustic Emission-Based Damage Pattern Identification and Residual Strength Prediction of Glass-Fiber Reinforced Polymers

In this paper, the damage mechanisms and residual strength prediction models of unidirectional glass-fiber reinforced polymers are investigated by acoustic emission (AE) technique. The material exhibits three damage modes: matrix cracking, fiber fracture, and interface damage. A novel AE descriptor, amplitude/centroid frequency (ACF), is introduced to differentiate interface damage from other damage modes. Moreover, the clustering analysis results are used as a training set for K-nearest neighbor (KNN) and support vector machine (SVM) methods to realize real-time classification. Prediction of residual strength of materials after pre-fatigue is achieved by introducing AE cumulative counts into two regression analysis prediction models. Additionally, optimization of prediction results can be achieved by a certain kind of signals after clustering. The combination of AE and machine learning can realize real-time residual strength prediction.