Deep transfer learning-based damage detection of composite structures by fusing monitoring data with physical mechanism

Abstract

Damage detection of carbon fiber reinforced plastics (CFRP) composites is a challenging issue owing to their intrinsic high degree of anisotropy with complex failure modes. Data-driven approaches have great potential in damage detection for CFRP composites. However, the lack of physical interpretability makes data-driven methods highly dependent on the amount of data, which involves significant effort in experiment and is impractical to obtain for all damage conditions. To overcome the above challenges, a robust and generalizable framework for damage detection of CFRP composite structures is proposed by fusing monitoring data with physical mechanisms. In this framework, the numerical method is leveraged to build physical models of the composite structures to generate data under various damage conditions. Then, a deep transfer learning-based model is applied in the fusion of experiment and simulation data, which mitigates the discrepancy between the physical model and real experiment. With the combination of domain adaptation and domain adversarial training in the model, the domain invariant features can be generalized from the source domain (simulation data) to the target domain (experiment data). Therefore, the physical interpretability is supplemented in the data-driven model. To verify the adaptability of the method, different transfer tasks based on the accelerated aging experiments are performed. The results show that the proposed method can reduce the dependence of the data-driven methods on real monitoring data and prevails in all evaluation indicators than other methods. Eventually, this method can reduce the experiment effort of damage detection for composites while holding a great detection accuracy.