Wei Zhou

1. Large-span and lightweight structures require materials with high load-bearing efficiency and long-term durability, making carbon fiber reinforced polymer (CFRP) materials attractive candidates ...

High-precision 3D reconstruction of large-size weak feature curved surfaces has long been a challenge in industrial manufacturing. Noise interference, occlusions, low overlap rates, and sparse geometric features often cause traditional point cloud registration methods to converge to local optima or fail to meet accuracy requirements. To this end, we propose a novel registration framework that integrates spatial positioning guidance with a multi-scale robust iterative closest point algorithm (MR-ICP). This method first establishes a world coordinate system using a global positioning system and achieves coarse registration of multi-view point clouds by introducing a transition coordinate system, thereby providing precise initial registration positions. Subsequently, the innovative MR-ICP algorithm incorporates a multi-scale extended convex hull to identify effective overlapping regions, while integrating a multi-scale feature fusion weighting mechanism to achieve synergistic optimization of global constraints and local details. Furthermore, we design an adaptive robust loss function, which dynamically adjusts residual weights to suppress noise during fine registration effectively. In Gaussian noise simulation stitching, this method reduces the root mean square error (RMS) by up to 85% compared to the point-to-surface benchmark method. In the experiment, the average error achieved in the reconstruction of the engine hood (1.8×1.2m) reached 0.261mm, significantly outperforming existing advanced algorithms. This provides an efficient and scalable solution for large-scale industrial surface 3D reconstruction.

To address the requirements for defect detection and three-dimensional (3D) visualization requirements of defects in carbon fiber composite cables (CFCC), a 3D reconstruction method integrating cross-sectional and surface electromagnetic tomography is proposed. First, the cross-sectional and surface sensitivity matrices of the CFCC are constructed. By combining algebraic reconstruction techniques with singular value decomposition for dimensionality reduction, two-dimensional (2D) imaging of typical defect modes is reconstructed for both cross-sectional and surface views. On this basis, a priori knowledge of the regular geometry of CFCC and a triple-constraint verification mechanism are introduced to achieve the fusion and inversion of 2D information into 3D defect morphology. It is shown that cross-sectional imaging exhibits relatively superior performance in the correlation coefficient metric (0.516–0.679), facilitating defect localization, whereas surface imaging demonstrates greater prominence in the structural similarity metric (0.949–0.979) for better preservation of defect contours. The proposed 3D reconstruction method enables visualization of defect spatial distribution and effectively eliminates interference from non-defect information through a constrained verification mechanism. Therefore, it overcomes the limitations of single-view imaging. This imaging method provides a viable solution for the rapid detection and 3D visualization of CFCC defects.

More and more wearable flexible electronic devices are being used for bioelectrical signal monitoring and related diseases under different conditions. At present, standard silver, silver chloride gel electrodes and hard electrodes often cause skin irritation due to heat dissipation, adhesion and structure mismatch when worn for a long time, or cause a large number of motion artefacts during motion detection. Here, we propose a mesh structure magnetically responsive 4D printed flexible electronic patch to collect static and dynamic bioelectrical signals for a long time, which is composed of a layer of customisable mesh flexible 4D printing substrate and a stretchable conductive pattern on the substrate. Flexible Substrates The mixed Ecoflex is fused with hexagonal boron nitride (E-hBN) and a mixture of Ecoflex with ferric tetroxide material (E-Fe ₃ O ₄ ) through 3D printing technology to form a 4D flexible mesh substrate that can be controlled by a magnetic field. This configuration provides the required adhesion as needed, as well as excellent heat dissipation, sweat wicking, deformation ability, motion adaptability, and excellent signal-to-noise ratio (SNR). The SNR achieves 23.1 dB under optimal conditions, whilst the sensor area can reach 5 cm × 6 cm. At the same time, it can meet the customised needs of different shapes when inspecting different parts. In addition, by drawing a stretchable conductive pattern on a flexible substrate, we can accurately discriminate specific gestures by monitoring the muscle electrical signals of the forearm muscles.