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

• DOI: 10.1111/ffe.14613
• Publication Date: 2025-02-26T06:14:20Z
• Authors: Xiheng Xu; Xinyu Bi; Zhuohan Li; Yiliang You

ABSTRACTIn 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.