@Article{cmc.2025.067907,
AUTHOR = {Mengke Ding, Jun Li, Dongyue Gao, Guotai Zhou, Borui Wang, Zhanjun Wu},
TITLE = {Fatigue Life Prediction of Composite Materials Based on BO-CNN-BiLSTM Model and Ultrasonic Guided Waves},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {85},
YEAR = {2025},
NUMBER = {1},
PAGES = {597--612},
URL = {http://www.techscience.com/cmc/v85n1/63576},
ISSN = {1546-2226},
ABSTRACT = {Throughout the composite structure’s lifespan, it is subject to a range of environmental factors, including loads, vibrations, and conditions involving heat and humidity. These factors have the potential to compromise the integrity of the structure. The estimation of the fatigue life of composite materials is imperative for ensuring the structural integrity of these materials. In this study, a methodology is proposed for predicting the fatigue life of composites that integrates ultrasonic guided waves and machine learning modeling. The method first screens the ultrasonic guided wave signal features that are significantly affected by fatigue damage. Subsequently, a covariance analysis is conducted to reduce the redundancy of the feature matrix. Furthermore, one-hot encoding is employed to incorporate boundary conditions as features, and the resulting data undergoes preprocessing to form a sample library. A composite fatigue life prediction model has been developed, employing the aforementioned sample library as the input source and utilizing remaining life as the output metric. The model synthesizes the strengths of convolutional neural networks (CNNs) and bidirectional long short-term memory networks (BiLSTMs) while leveraging Bayesian optimization (BO) to enhance the optimization of hyperparameters. The experimental results demonstrate that the proposed BO-CNN-BiLSTM model exhibits superior performance in terms of prediction accuracy and reliability in the damage regression task when compared to both the BiLSTM and CNN-BiLSTM models.},
DOI = {10.32604/cmc.2025.067907}
}