Jiachun Lin

Existing re-entrant honeycomb designs face a fundamental trade-off between stiffness enhancement and post-peak stability, stemming from an inability to control local instability propagation. Here we establish a defect-programmed paradigm using mimetic edge dislocation (MED) engineering. Unlike conventional unit-cell modification, this approach translates crystalline dislocation mechanisms into macro-scale geometric-topological defect arrays. These ordered defects prescribe low-strength nucleation cores (Z-shaped regions) and continuous slip lines, inducing a tailored slip-like deformation behavior (SDB). Experiments and simulations show that MED-driven SDB transforms chaotic local collapse into ordered progressive global failure, effectively suppressing post-peak instabilities. By tuning MED arrangements, we achieve an ascending plateau with 47% higher specific energy absorption (SEA) and a dual-plateau stage with 20% higher SEA. Locally reinforcing the Z-shaped regions substantially suppresses the residual stress drops. Increasing the stiffness ratio across slip lines further elevates SEA gains to 98% and 51% for the ascending-plateau and dual-plateau configurations, respectively. This work establishes a programmable defect-engineering methodology that offers a promising approach for addressing the trade-off between stiffness enhancement and post-peak stability in energy-absorbing metamaterials.

Gear wear is a tribological surface damage process caused by contact loading and relative motion between meshing tooth flanks. It involves progressive material removal or transfer, changes tooth-flank topography, and consequently affects contact conditions, mesh stiffness, transmission error, and dynamic response. This paper reviews recent advances in gear wear research, including typical tooth-surface damage, wear prediction under various lubrication conditions, tribodynamic behavior, dynamic effects of wear, and the influence of assembly errors, parameter uncertainties, and gear modification on meshing characteristics. Existing studies have progressed from isolated descriptions of wear to integrated analyses involving lubrication, surface condition, and dynamics. Nevertheless, the long-term bidirectional coupling between wear evolution and tribodynamics remains insufficiently understood, while the effects of assembly errors and multi-source uncertainties have received limited attention. Gear modification studies have also focused mainly on initial transmission performance rather than its sustained role during wear degradation. Future research should therefore establish coupled wear–friction dynamic models and develop wear analysis methods that account for actual assembly conditions and parameter uncertainties.

Gears serve as core transmission components, and their wear evolution directly affects equipment stability and service life under long-duration complex loading. Especially under complex loading and long-term service conditions, the tooth surface topography undergoes continuous evolution. However, traditional wear prediction methods based on physical models or empirical formulas have significant limitations in addressing nonlinear problems involving multiple coupled variables. This study proposes a deep neural network (DNN)-based method for gear wear prediction. Geometric parameters, loading conditions, and surface topography characteristics are integrated as model inputs to enable point-by-point prediction of tooth-profile wear. Experimental results demonstrate that the proposed model achieves excellent predictive performance in the mild-wear regime, with a mean absolute error (MAE) below 2.5 × 10⁻⁴ mm, a root mean square error (RMSE) below 5.0 × 10⁻⁴ mm, and R² values ranging from 0.92 to 0.99. The model also achieves satisfactory prediction accuracy at previously unseen measurement positions and for previously unseen superfinished gear samples. The proposed DNN effectively learns implicit wear-evolution patterns from experimental data and exhibits strong generalization capability, providing a practical approach for gear health monitoring and predictive maintenance.

The mechanical metamaterials have promising applications in aerospace, construction, biomedical and many other fields due to their excellent properties, but the expensive manufacturing cost greatly affects the practical application. Although many existing mechanical metamaterials enable significant tuning of mechanical properties to suit different application scenarios without requiring structural redesign, the adjustment of these properties often depends on the variation of parameters. This results in the need to manufacture different mechanical property structural components separately, making it difficult to achieve large-scale production and reduce manufacturing costs. In this study, a novel mechanical metamaterial was proposed that could adjust the positive and negative Poisson’s ratio, elastic modulus, and compression-torsion properties only by simply changing the unit cell number and the combination method. This asymmetric structural design provides a new perspective for the design of metamaterials with tunable mechanical properties.

The pursuit of advanced auxetic materials with broadband load-adaptive mechanical properties and multifunctional performance has become a critical frontier in structural engineering. The improved star honeycomb and hexagon star composite honeycomb structures proposed in this study were based on the arrangement and combination of concave hexagonal and concave star shaped structures. The response of these structures under both quasi-static and dynamic loading conditions through numerical simulations and theoretical analyses were investigated. The results indicated the honeycomb structures were insensitive to changes in velocity constant crushing speed was at 5 mm s⁻¹, 10 mm s⁻¹, 20 mm s⁻¹, 30 mm s⁻¹, 50 mm s⁻¹, and 100 mm s⁻¹, and exhibited a similar and stable negative Poisson’s ratio phenomenon in the x and y directions. Furthermore, the stiffness and strength of these two honeycomb structures were superior to the traditional hexagon hexagonal and the fix hexagon star honeycomb structures, and revealed excellent energy absorption capabilities. Therefore, the proposed honeycomb structures were expected to play an important role in energy dissipation devices such as shock pad used in mechanical engineering to reduce vibration and noise generated during equipment operation, wind turbine blades, aircraft wings and train frames.

The concave star structure is a typical negative Poisson's ratio structure which reduces the volume of the structure through mechanical compression. Usually, the parameters of the structure such as wall thickness, bevel angle, and size affect the strength, stiffness, and energy absorption characteristics of the structure. This study designs a novel 3D star‐shaped structure with a negative Poisson's ratio. Through finite element method simulation and mechanical experiments, the influence of relevant parameters on the strength, stiffness, and Poisson's ratio of the overall structure is explored, and the deformation mode, load‐bearing capacity, and energy absorption capacity of the structure under quasistatic compression conditions are systematically studied, and structural parameters with good mechanical properties are finally obtained. The unit cell of the proposed structure has higher strength and specific energy absorption, and the proposed star‐shaped structure shows a stable negative Poisson's ratio phenomenon after array combination compared with the traditional star‐shaped structure. The structure designed in this research has both high strength and stiffness, as well as good buffering and high energy‐absorbing potential, to prevent structural fracture under limited compression distance, which may have broad application prospects in the field of protective structures.

The attitude control error of the robot end-effector directly affects the manufacturing accuracy. The study aims to develop a real-time measurement method of the industrial robot end-effector attitude in the field environment for improving the control accuracy of robot attitude.In this paper, an attitude measurement method of robot end-effector based on the inertial technology was proposed. First, an inertial measurement system was designed, and the measurement parameters and installation errors were calibrated. Then the inertia measurement principle of robot end-effector attitude was explored, and the robot end-effector attitude measurement was realized with the fourth-order Runge−Kutta algorithm. In addition, the influence of the data processing algorithm and sampling frequency on the attitude accuracy was analyzed. Finally, a test platform was built to experimentally explore the proposed inertial measurement method.The inertial measured data were compared with the data obtained with the laser tracker. The measurement accuracy of the inertial measurement method reached 0.15°, which met the accuracy requirements of real-time measurements of robot end-effector attitude in the manufacturing field.The method proposed in this paper is convenient and can realize the real-time attitude measurement of industrial robot. The measurement results can compensate the attitude control error of the robot end-effector and improve the attitude control accuracy of the robot.