Search

I-section carbon fiber reinforced polymer (I-CFRP) laminates are widely used as hatch and cargo doors of wide-body airliners. However, estimating the loading capacity of I-CFRP laminates has always been empirically dependent since there is no uniform failure criterion for composite materials. This study attempts to reveal the failure characteristic points of I-CFRP laminates from a thermodynamic perspective. First, the test strain data can be modeled as state variables, thus equating the loaded engineering structure to a thermodynamic system. Based on the relation of state variables, matrices (Modes) and Hamiltonians (Characteristic parameters) that characterize the overall stressing state evolution of the specimen can be established. Integrating the Hamiltonians of each part into the whole is similar to the group representation and renormalization of Wilson's phase transition theory. Applying the clustering analysis (CA) criterion in combination with the bifurcation and transition of the mode and characteristic parameter curves reveals the phase transformation loads of the specimen. The accuracy and stability can be verified for the phase transition loads before and after the renormalization transformation. In conclusion, this study reveals the deformation-failure law of I-CFRP laminates from the thermodynamic perspective, which provides a new reference and method for the design of composite laminates. [Display omitted] • Eleven I-CFRP laminates axial compression tests were conducted. • Modeling the behavior of I-CFRP laminates from a thermodynamic perspective. • Propose a matrix representation (Mode) and Hamiltonian (Characteristic parameter) of the I-CFRP laminates' stressing state. • Reveal the phase transition point of I-CFRP laminates: EPB and FS points. [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]

Accurate 3D representations of multiphase layered composite fabric, including distinguish and label the pores, matrices, warp and weft yarns, can assist in understanding and ultimately improving composite material design performance. In this work, a new data enhancement algorithm is proposed to generate realistic-looking artificial learning datasets for expanding the datasets. Then, we demonstrate how supervised, the Swin Transformer approach can help to realize accurate segmentation of low-resolution and poor-contrast fabric datasets by populating databases with real and artificial learning datasets. Most of the pixels were correctly identified and the boundaries of the yarn could be identified clearly, except for some spots and a few merged boundaries. The work presented here provides an effective tool that can be widely used in semantic segmentation for various composite materials (woven composites, fiber-reinforced composites, short-fiber materials, etc.), which enables higher-precision reconstruction of fiber-composites and significant reduction in dataset processing time, and provides a route to generating artificial learning dataset. [ABSTRACT FROM AUTHOR]