Identification of poroid damage in CFRP axle tubes based on modal parameters

In order to mitigate the loss caused by poroid damage during the service of carbon fiber-reinforced polymer (CFRP) axle tubes, this paper proposes a modal parameter-based approach to identify poroid damage. The method focuses on single-hole, double-hole, and triple-hole damage as the objects of study, with fiber Bragg grating sensors for data collecting and strain mode shapes serving as the indicator for damage determination. The damage area of the axle tubes is localized based on the difference in strain mode shapes, and the degree of damage is identified using deep neural networks (DNN). The results indicate that the method of identifying the poroid damage of CFRP axle tubes based on modal parameters is highly accurate, with all damage locations reliably identified, and the maximum relative error in damage degree identification is -12.95%. This study is highly significant for enhancing maintenance efficiency and prolonging the service life of CFRP axle tubes.