Lian-Hua Ma

This paper proposes a three-dimensionally tunable concave–chiral re-entrant auxetic lattice (CCRAL) that couples re-entrant deformation with chiral torsion to achieve multidirectional auxetic responses and tunable mechanical performance. The proposed architecture enables different but coordinated deformation mechanisms to be activated under different loading directions. Under y -direction compression, the auxetic response is mainly governed by bending and rotation of the re-entrant ligaments, whereas under z -direction compression, coordinated chiral torsional rotation and folding dominate the transverse deformation. Quasi-static compression experiments and finite-element simulations show good agreement and validate the deformation characteristics of the proposed structure. The CCRAL maintains negative Poisson’s ratio responses under both loading directions, with the Poisson’s ratio reaching approximately −1.2 under z -direction compression. Parametric analyses further demonstrate that the concavity angle, connecting-rod length, and cell width effectively regulate the stiffness, load-bearing capacity, auxetic response, and specific energy absorption. Within the investigated parameter range, the specific energy absorption reaches approximately 70 J kg ⁻¹ at a nominal strain of 0.7. These results demonstrate that coupling concave and chiral deformation mechanisms provides an effective approach for balancing multidirectional auxeticity, load-bearing capacity, and energy absorption, offering a directly fabricable and geometrically tunable platform for lightweight protective and energy-absorbing applications.

Carbon fiber reinforced polymer (CFRP)/aluminum honeycomb sandwich structures are widely utilized in aerospace applications due to their high specific stiffness and strength. However, the mechanical performance and reliability of these structures under cryogenic environments present significant challenges. In this study, the compressive damage evolution of composite sandwich structures was investigated using an in-situ fiber-optic acoustic emission (AE) system. Experimental tests were performed at 293 K, 193 K, 93 K, and under room temperature (RT) conditions following cryogenic pretreatment. Then, a refined damage mode identification framework, integrating image processing with deep learning algorithms, was established to elucidate the cryogenic damage mechanisms. The results reveal that the compressive ultimate load of the sandwich structures rises as ambient temperature decreases. The cryogenic environment triggers matrix embrittlement, facilitating a more gradual and uniform damage evolution mode. Deep cryogenic pretreatment enhances the room-temperature load-bearing capacity of structures via microstructure regulation, while maintaining unchanged damage failure modes. Furthermore, a modified convolutional neural network (CNN) model embedded with multi-scale feature fusion and efficient channel attention (ECA) achieves a classification accuracy exceeding 96% under all temperatures conditions. With the decrease in temperature, mixed mode damage characteristics become increasingly prominent, and matrix cracking and interfacial debonding dominate the failure mechanisms. This study deepens the fundamental understanding of the cryogenic damage behavior of sandwich structures and provides a reliable theoretical and technical framework for damage evaluation in cryogenic engineering applications.

Electromagnetic tomography has emerged as a promising technique for defect detection in carbon fiber reinforced polymer (CFRP) cables, owing to its non‐contact and visual imaging capabilities. To address the challenges of the ill‐posedness and computational efficiency in defect imaging of CFRP cables, the Bound‐Constrained Split Bregman L1 method is proposed. The integration of L1 regularization with the Split Bregman solution method effectively mitigates the impact of measurement noise and enhances the spatial resolution of the reconstructed images. Defect reconstruction images of CFRP cables exhibit numerous artifacts due to their low electrical conductivity. Therefore, introducing a constrained lower bound consistent with physical conditions reduces imaging errors from edge artifacts. Numerical simulation results demonstrate that the proposed Bound‐Constrained Split Bregman L1 method outperforms conventional algorithms, including the Linear Back Projection, Landweber iterative, Tikhonov regularization, Iterative Shrinkage‐Thresholding algorithm, Alternating Direction Method of Multipliers, and Split Bregman L1 ‐regularized algorithms, and exhibits superior noise resistance and imaging stability. Specifically, the proposed Bound‐Constrained Split Bregman L1 method attained a mean Imaging Error (IE) of 0.182 and a Structural Similarity Index Measure (SSIM) of 0.948. This represents a 26%–60% decrease in IE and a 1.5%–6.6% increase in SSIM relative to all other methods evaluated. The method's superior defect detection in the experiment highlights its potential as a promising tool for electromagnetic tomography of CFRP cables.

Woven composites exhibit complex mechanical behavior due to intricate microstructural architecture, necessitating efficient multiscale modeling strategies. This study proposes a Direct FE ² ‐based computational framework to capture the mechanical response and progressive damage evolution of carbon fiber woven composites across multiple scales. The model incorporates damage mechanisms within the matrix, fibers, and interfaces, with validation against full‐field Direct Numerical Simulation (DNS) results. Comparisons confirm excellent agreement in load–displacement behavior, deformation fields, and damage evolution. A parametric investigation examines the effects of macroscopic mesh size and Representative Volume Element (RVE) dimensions on computational cost versus predictive accuracy trade‐offs. While finer meshes and larger RVEs enhance accuracy, they increase computational expense significantly. Through optimal parameter tuning, an efficient and accurate modeling strategy is achieved. This study demonstrates that the Direct FE ² method serves as a robust, scalable, and computationally efficient alternative to full‐field simulations. It thus provides a practical and reliable computational tool for multiscale analysis of mechanical behavior and progressive damage in woven composites.

With the extensive application of 3D-printed composites across multiple industries, the investigation into their structural reliability under complex loading conditions has become a critical research focus. This study comprehensively employs acoustic emission (AE) monitoring, digital image correlation (DIC) measurement, and micro-computed tomography (Micro-CT) visualization techniques to explore the progressive damage behavior of 3D-printed sandwich-structured composites reinforced with continuous carbon fiber sheets under three-point bending. Mechanical tests show that increasing the fiber content of face sheets from 10% to 20% enhances average bending strength by 56%, while low fiber content compromises stiffness and load-bearing capacity. AE analysis categorizes damage modes into matrix cracking (150 kHz) using k-means clustering algorithms. DIC measurement reveals significant structural deformation processes during damage progression. The AE-DIC-Micro-CT combination demonstrates an initial undamaged state, followed by damage initiation and propagation in the subsequent stages. This integrated approach provides an effective method for damage assessment, guiding the design and reliability improvement of 3D-printed composites.