To address the scientific challenges in laboratory shock testing of large-inertia and large-scale marine structural components, particularly those used in deep-sea naval environments, a pneumatic-hydraulic shock testing apparatus (PHSTA) has been developed. In contrast to conventional drop-weight, pendulum, or explosion-based shock simulators that rely on localized collisions or rigid impacts, the proposed PHSTA employs a pneumatic-hydraulic spring mechanism to provide controllable, repeatable, and tunable shock responses. A comprehensive theoretical framework was developed, including dynamic modeling of the PHSTA, energy storage analysis, and closed-form expressions for equivalent stiffness and acceleration characteristics. The effects of gas precharge pressures p_0 and loading pressures \mathrm{\Delta }p on the stored energy E, shock acceleration, and the system’s dynamic characteristics—in particular the natural frequency and damping ratio—were quantitatively analyzed using AMESim. Experimental validation was conducted on a prototype PHSTA, and the measured shock responses demonstrated the validity of the proposed model, showing good agreement with both the theoretical analysis and simulation results. At p_0=10 MPa, the peak acceleration increased by 24.7% compared to p_0=13 MPa, closely matching the simulated result of 23.3%. The developed PHSTA offers a novel approach for simulating marine shock environments under laboratory conditions and provides a robust platform for evaluating the shock resistance of offshore and naval structural components.

This research enhances the precision and efficacy of abrasive waterjet machining (AWJM) of S275 carbon steel. To this end, a precise predictive framework has been developed using artificial neural networks (ANNs) and response surface models (RSMs). By employing an innovative Vectorized Macrographic Analysis, the cutting geometries are accurately mapped and the correlation between width at various depths and energy dissipation is established. The fit accuracy of the ANN is 99%, while that of the RSM is 90%. Furthermore, a minimum cutting energy threshold of 52.20 kJ/m² has been identified, which represents the optimal efficiency threshold. These developments highlight ANN’s ability to model complex AWJM interactions, improving machining precision and adaptability.

Thanks to the high quantum efficiency, large migration lifetime, excellent X-ray absorption capability, and ease of crystal growth, perovskite scintillators have received widespread attention in recent years and have become highly competitive X-ray detection scintillators. Compared with traditional inorganic scintillators, the tunable structure and diverse chemical composition of perovskite scintillators are unique advantages in optimizing their scintillation performance. Fully understanding the relationship between scintillation characteristics with structure and chemical composition of perovskite scintillators is the key to achieve high-sensitivity detection and high-resolution imaging. The latest progress and future prospect of key performance indicators such as light yield and spatial resolution in perovskite X-ray indirect detection and imaging are reviewed herein. First, the basic principles of X-ray indirect detection and the key performance parameters of X-ray indirect detectors are discussed. Then, the methods to improve the light yield of perovskite scintillators are discussed from the aspects of the characteristics of perovskite materials themselves, the introduction of ion doping to adjust the perovskite structure, and the improvement of scintillation preparation processes. We further discuss how to suppress fluorescence crosstalk to improve the spatial resolution of X-ray imaging from the aspects of material size, scintillation preparation process, and scintillation structure. Finally, we emphasize the challenges that current perovskite scintillators still face and provide prospects for their future development.

Dual-arm robots operating in dynamic, human-centered environments must be reactive, dexterous, and safe while intelligently coordinating both arms to perform task-space-constrained manipulations. To address this challenge, we propose a general framework comprising three interrelated modules for dual-arm robots, which integrates tightly coupled coordinated bimanual manipulation with reactive collision-free motion: i) The multiple task-priority joint control module achieves tightly coupled bimanual coordination by taking advantage of the cooperative dual task space, including the fixed relative pose of the dual-arm end-effectors, the flexible absolute pose control of the end-effectors, and satisfying joint limits. ii) The nominal trajectory module, based on the vector field that combines the target attraction force and the manipulability force, dynamically generates nominal reference trajectory for the end-effectors. iii) The safety filter module, based on control barrier function, locally reshapes the nominal reference trajectory in real time to generate collision-free reference trajectory. Various planning methods are thoroughly validated in cluttered and dynamic simulation scenarios. The proposed method is able to reliably accomplish constrained tasks while existing solutions perform poorly. In real-world experiments, a 14-degree-of-freedom dual-arm robot transports multiple cups by grasping a tray in an environment with random disturbances. Experimental results demonstrate that the robot prevents the cups from sliding off by adaptively adjusting the tray’s tilt angle. Simultaneously, bimanual reactive motion enables the robot to successfully avoid dynamic obstacles while maintaining task execution.

Morphing aircraft represent a critical direction in the development of future aerospace transportation systems. Morphing mechanisms have become a prominent research focus. Developing synthesis methods for such mechanisms holds significant value for guiding future design and application efforts. This paper proposes a multi-constraint dimensional synthesis and filtering method for the 5-SS/SPS morphing linkage of aircraft. First, the moving and fixed solution curves of the linkage are derived, and principles for selecting appropriate solution curves are built. A defect determination method is proposed, incorporating the derivation of the Jacobian matrix, position analysis, and determination of motion continuity to eliminate defective linkages. Secondly, this paper also creatively presents linkage synthesis method satisfying multiple geometric constraints, and derives mathematical expressions satisfying coplanar, cospherical and concyclic geometric constraints of fixed or moving platform, and obtains the discriminants based on the condition that the linear equation set has a solution. A solution region synthesis method is introduced to visualize the synthesized linkages, and the synthesis process under various constraints is presented. Finally, illustrative examples are conducted to validate the correctness of the derived formulas and the effectiveness of the proposed synthesis method, demonstrating the application potential of the morphing linkage in morphing aircraft. The proposed method provides a valuable reference for the synthesis of aircraft morphing linkages under various constraints.

This paper investigates the suppression of background noise in ultrasonic array imaging by applying the total focusing method and baseline subtraction, focusing on coarse-grained materials that exhibit significant levels of structural noise. Addressing the challenge of identifying small defects due to low signal-to-noise ratios (SNRs) in the measured array data, we have proposed an efficient methodology that can be applied to enhance the detectability of a defect within a specified region of interest (ROI). The proposed methodology requires the original full matrix capture data solely, and it generates the reconstructed baseline (i.e., estimated grain noise) data using a multi-step long short-term memory model. This model predicts time traces corresponding to the ROI based on historical signals of the same data set. The root mean square value and peak noise amplitude of the reconstructed grain image are used to evaluate the noise prediction performance of the proposed approach. The simulation and experimental study results demonstrate that our proposed approach for reconstructing the grain-scattered data can notably enhance the defect SNR when combined with a straightforward baseline subtraction method. Moreover, the effect of the probe position (with respect to the defect) on the noise suppression capability of the prediction model is shown to be small.

The air conditioning manufacturing industry is characterized by discrete manufacturing features including multiple processes, a wide variety of products, small batch sizes and rapid production cycles. Traditional production lines have been rendered insufficient to meet the rapidly evolving market demands concerning flexibility, efficiency, quality and resource management. To address this challenge, an intelligent production line and operational model has been proposed and validated for air conditioning manufacturing, based on the concept of data-driven, system-integrated, and intelligently-scheduled operations. First, three core hypotheses were formulated based on theoretical considerations. An integrated technical framework was subsequently established, incorporating a cyber-physical system architecture, core assembly processes, four sub-production line systems and an intelligent maintenance platform. Key innovations were implemented in technologies including radio frequency identification traceability, artificial intelligence visual inspection, automated equipment integration, Internet of Things sensing networks, as well as an integrated air-ground coordinated transportation system. Through comparative studies with traditional air conditioner production lines, the intelligent production line was shown to significantly outperform traditional systems in production capacity: daily output increased by 57.6%, cycle time was reduced by 57.6%, workforce requirements decreased by 57.4% and unit per person per hour improved to 3.8 times the original level. Additionally, lighting energy consumption was reduced by an average of 60% and the system achieved substantial improvements in efficiency across six dimensions. The established intelligent air conditioner production line model not only effectively validated the research hypotheses and addressed critical limitations of traditional production lines but also provided theoretical support and technical pathways for the intelligent transformation of the discrete manufacturing industry, demonstrating considerable engineering application value and promotion potential.

In the realm of composite materials, the machining accuracy of Carbon Fiber Reinforced Polymer (CFRP) circular cell honeycombs plays a critical role in determining the performance of sandwich components. However, due to the discontinuous nature and relatively weak stiffness characteristics of this material, achieving precise control over the complicate surface profile accuracy becomes a tough task, often accompanied by the problem of low processing efficiency. This study proposes a novel topological hierarchy-based toolpath strategy specifically designed for the grinding process, combined with compensating CFRP circular cell honeycomb surfaces. The proposed methodology involves sequentially processing each circular cell, wherein those cells are treated as discrete units arranged within a two-dimensional plane. To enhance the surface profile accuracy of the workpiece while maintaining high processing efficiency, a compensatory machining approach has been employed. Based on this approach, algorithms for the extraction of the discontinuous compensatory machining regions have been subsequently developed. Consequently, an optimized toolpath and a serialized machining process has been established, enabling the efficient and high accuracy grinding of the CFRP circular cell honeycombs. Experimental verifications demonstrate that the proposed method achieves a significant improvement in machining efficiency and successfully confines the surface profile error within a tolerance of 20 μm. Additionally, the peak-to-valley on cross-section lines after compensatory machining decreases from 0.20 to 0.05 mm. Overall, this innovative method presents a practical solution for the fabrication of large-scale honeycomb sandwich components, thereby making significant contributions to the field of advanced composite material machining.