Since the cell was discovered by humans, it has been an important research subject for researchers. The mechanical response of cells to external stimuli and the biomechanical response inside cells are of great significance for maintaining the life activities of cells. These biomechanical behaviors have wide applications in the fields of disease research and micromanipulation. In order to study the mechanical behavior of single cells, various cell mechanics models have been proposed. In addition, the measurement technologies of single cells have been greatly developed. These models, combined with experimental techniques, can effectively explain the biomechanical behavior and reaction mechanism of cells. In this review, we first introduce the basic concept and biomechanical background of cells, then summarize the research progress of internal force models and experimental techniques in the field of cell mechanics and discuss the latest mechanical models and experimental methods. We summarize the application directions of cell mechanics and put forward the future perspectives of a cell mechanics model.

The emerging trend towards precision in robot-assisted microsurgical techniques stems from the increasing need to perform intricate and dexterous tasks within confined spaces. However, micromanipulators face challenges in size miniaturization, operational flexibility, and overall biocompatibility. To overcome these challenges, we propose a miniature pneumatic manipulator with multi-degree-of-freedom (DOF) attitude adjustment capability. With dimensions no larger than 15 mm × 2.1 mm × 2.1 mm, it utilizes pneumatic actuation, making it adaptable to various complex environments. The microgripper section incorporates an innovative three-dimensional interleaved helical structure, allowing for both miniaturization and high clamping force, capable of lifting an object over 1700 times its own weight. This component can be mass-produced through a single-step 3-D printing process. The attitude adjustment mechanism, featuring a five-cavity microtube structure with pitch and yaw rotational DOF, is fabricated using a novel low-cost method to overcome micro-scale manufacturing challenges. The mechanism has ±136° of rotational angle in a single axis. Demonstrations of single-finger continuous microscopic threading and the orderly reorganization of disordered micro-objects validate the micromanipulator's ability to perform dexterous and complex manipulations in constrained spaces, similar to human hand capabilities, highlighting its potential in precision microsurgery and related fields.

To address the challenges of manual inspection dependency, low efficiency, and high costs in evaluating the surface grinding quality of composite materials, this study investigated machine vision-based surface recognition algorithms. We proposed a multi-scale texture fusion analysis algorithm that innovatively integrated luminance analysis with multi-scale texture features through decision-level fusion. Specifically, a modified Rayleigh parameter was developed during luminance analysis to rapidly pre-segment unpolished areas by characterizing surface reflection properties. Furthermore, we enhanced the traditional Otsu algorithm by incorporating global grayscale mean (μ) and standard deviation (σ), overcoming its inherent limitations of exclusive reliance on grayscale histograms and lack of multimodal feature integration. This optimization enables simultaneous detection of specular reflection defects and texture uniformity variations. To improve detection window adaptability across heterogeneous surface regions, we designed a multi-scale texture analysis framework operating at multiple resolutions. Through decision-level fusion of luminance analysis and multi-scale texture evaluation, the proposed algorithm achieved 96% recognition accuracy with >95% reliability, demonstrating robust performance for automated surface grinding quality assessment of composite materials.

The application of lightweight materials in the automotive industry can effectively achieve further weight reduction while maintaining overall structural strength, thereby reducing energy consumption. Currently, friction stir spot welding (FSSW) is the primary method for joining carbon fiber-reinforced polyphenylene sulfide (CF-PPS) with aluminum alloys. This study successfully achieved the connection between 6061-T6 aluminum alloy and CF-PPS using the more operationally convenient friction stir lap welding (FSLW) technique. The primary objective of this study was to explore the potential of expanding the welding technologies available for successfully joining these two dissimilar materials. The joint morphology and strength were analyzed through metallographic observation and tensile testing, and the effects of different welding parameters on the microstructure and mechanical properties of dissimilar joints were studied. The study demonstrated that the successful connection between AA6061-T6 and CF-PPS was primarily attributable to the combined effects of mechanical interlocking and mixture bonding. The joint strength demonstrated a maximum value of 9.41 MPa when the following parameters were set: a rotation speed of 1800 rpm, a welding speed of 40 mm/min, and a plunge depth of 0.2 mm. Although low rotation speed and low welding speed cannot form an effective mechanical interlocking structure for the joint, the failed joints have different causes. When the rotation and welding speeds are fixed, changing the plunge depth cannot change the interlocking structure of the joint. A larger plunge depth will thin the weld and greatly reduce the joint strength.

In conventional CNC machining, the workpiece clamping pose is registered with a preset CAD model under multiple geometric constraints to establish the machining reference frame. The tool path, generated from this model, is subsequently used to produce components of identical geometry. However, this paradigm proves inadequate when a final shape must accommodate morphological variations specific to each individual blank. Manual grinding, as an alternative, is not only inefficient and hazardous but also relies heavily on subjective quality assessment. To address these challenges, we propose an adaptive local-region milling strategy tailored for blanks with similar yet non-identical surface morphologies, enabling the finished geometry to adjust dynamically to each workpiece. Under conditions of under-constrained clamping, visual positioning is first employed to automatically locate the target regions. Line laser scanning is then conducted over the planned area to acquire high-density point clouds. Through segmentation, points lying outside the region to be machined are extracted, from which a theoretical post-machining surface is reconstructed. Milling toolpaths are subsequently planned based on this reconstructed model to compensate for surface variations across different blanks. Experimental validation on a three-axis CNC milling machine demonstrates that the proposed adaptive strategy effectively replaces manual grinding by removing the bulk of the machining allowance from locally variant surfaces. With the reconstructed model serving as the reference, 77.1 percent of the machining errors fall below 0.055 mm. These results confirm that the method yields a smooth and level surface finish, thereby meeting the fundamental requirements for such adaptive machining tasks.