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• The effect of ply number on the impact response was identified. • The effect of impact damage on the residual axial compression behavior was analyzed. • The proposed two-step FE model predicted accurately the damage behaviors of braided tube. In this study, transverse low-velocity impact response and residual axial compression behavior of braided composite tube with different ply number was investigated by experimental and numerical methods. The transverse low-velocity impact tests with 5.6 J energy were conducted on the composite tubes. The quasi-static axial compression performance of intact and pre-impacted tubes was compared to evaluate the effect of impact damage. A two-step finite element (FE) model was also established to reveal damage mechanisms of braided tube under impact loading and following axial compression. It was found that the wall thickness had a significant influence on the impact response. Obvious structural deformation occurred in 2-ply tube when subjected to impact loading, resulting in a large projected delamination area. In following axial compression process, the delamination failure significantly reduced the local compression stiffness and caused a structural instability, which led to a buckling failure mode. In contrast, increasing bending stiffness of 3 or 4-ply tube suppressed its structural deformation during impact, leading to a confined projected delamination area and therefore the buckling of tube wall was prevented effectively when subjected to axial compression. For all impacted tubes, the transition of failure mode from progressive folding mode in intact tube led to a lower energy absorption capacity per composite ply, especially for 2-ply tube. [ABSTRACT FROM AUTHOR]

Accurately identifying various meso-morphological features and sub-phases within the damaged braided composite fabric is crucial for assessing the mechanical properties of composites under complex loading conditions and different processing parameters. However, obtaining 3D reconstructions that can be quantitatively analyzed is still a challenge, primarily due to the low contrast of the meso-morphological features (yarn tow and matrix crack networks). In this work, a new data enhancement algorithm is proposed to generate a realistic-looking artificial training dataset for enriching the information of the meso-morphological features. Then, the effect of various training networks and training parameters (the size of real and hybrid training dataset, number of epochs) on segmentation performance were examined. Finally, the meso-morphological features were statistically analyzed to accurately evaluate the mechanical properties of the composite material. The work presented here provides an effective tool that enables the quantitative analysis of the dynamic crack evolution process under the axial load of the composite materials. [Display omitted] [ABSTRACT FROM AUTHOR]