Based on the changes of microhardness, tensile strength, and impact resistance caused by the difference of macroscopic morphology and microstructure of welded joints, this paper studied the effect of different ultrasonic power on the properties of welded joints during the welding of homogeneous armor steel. It is experimentally found that the macroscopic morphology of those joints is very different. Compared with conventional welding, ultrasonic welding can increase the weld depth and the width of the heat-affected zone (HAZ) on either side of the weld. However, only the ultrasonic wave at an appropriate power level can increase the weld width. In addition, appropriate ultrasonic power can significantly improve the grain state of the weld. With the increase of ultrasonic power, the grain size in HAZ will decrease. The microhardness of the weld will first increase and then decrease, while the microhardness of the HAZ will increase. This is basically consistent with the changing trend of impact resistance. An ultrasonic wave can also increase the tensile strength of a welded joint up to 802 MPa, 12.4% higher than that in conventional welding. However, a high-power ultrasonic wave will bring down the tensile strength. This study provides guidance for the selection of ultrasonic-assisted regulation power to achieve the different properties of homogeneous armor steel joints.

Due to its extreme service conditions, low-temperature pressure piping often needs post-welding stress measurement and control. Aiming at the phenomenon of local stress concentration in welded 316L pipes, this study used ultrasound to regulate the stress in the welded area at different times during and after the multi-layer welding of the pipeline butt joint for different time lengths. Mechanical properties such as tensile strength and hardness were tested for each comparison group, and the microcrystalline phases of the weld and its surrounding microstructure were analyzed. The transverse and longitudinal surface residual stresses of each comparison group were measured. The influence of high-energy ultrasound on the surface temperature field during and after welding was analyzed. The experimental results show that ultrasonic wave regulation can speed up heat exchange and radiation in the weld zone (WZ), refine the grains in the WZ, heat-affected zone (HAZ) and fusion zone (FZ) to some extent and reduce and homogenize residual stress to a certain degree. In the 120 mm area of the weld center, the residual stress measured after the mid-welding regulation was smaller than that of any other comparison group. This regulation result was the best, followed by that of hot regulation and finally that of offline regulation. The tensile strengths obtained by the mid-welding regulation and post-welding hot regulation of this group were the best, increasing by 17.2% and 24.3%, respectively, compared with the untreated groups.

In heavy-duty shipbuilding, the efficient transfer of ship blocks directly affects the overall construction schedule, while reliable identification of the lifting and dropping states of transport vehicles is essential for updating block locations. However, vehicle vibration, road disturbances, and local sensor anomalies can cause substantial fluctuations in multi-source measurement signals, limiting the recognition accuracy of conventional hard-threshold methods. To address this issue, a knowledge-driven dual-expert machine learning framework with physical gating is proposed. The standard deviation of vehicle speed is used as the physical gating indicator to partition and route data under different operating conditions. A 16-dimensional domain-informed hybrid feature space is then constructed, and LightGBM and CatBoost are employed as the steady-state and dynamic experts, respectively. Experiments on real-world engineering data show that the proposed method achieves a Macro-F1 of 0.9938 and an Accuracy of 0.9972. Measurement-noise enhancement experiments and tests on unseen vehicles with different rated tonnages further demonstrate its robustness and cross-vehicle generalization capability. t-SNE and SHAP analyses reveal distinct feature dependencies between the two experts, which are generally consistent with the physical mechanisms of lifting operations and driving disturbances. The proposed method provides an effective approach for reliable operational-state measurement of heavy-duty engineering vehicles under complex operating conditions.

This paper presents a robust, decoupled approach to camera distortion correction using a rational function model (RFM), designed to address challenges in accuracy and flexibility within precision-critical applications. Camera distortion is a pervasive issue in fields such as medical imaging, robotics, and 3D reconstruction, where high fidelity and geometric accuracy are crucial. Traditional distortion correction methods rely on radial-symmetry-based models, which have limited precision under tangential distortion and require nonlinear optimization. In contrast, general models do not rely on radial symmetry geometry and are theoretically generalizable to various sources of distortion. There exists a gap between the theoretical precision advantage of the Rational Function Model (RFM) and its practical applicability in real-world scenarios. This gap arises from uncertainties regarding the model’s robustness to noise, the impact of sparse sample distributions, and its generalizability out of the training sample range. In this paper, we provide a mathematical interpretation of how RFM is suitable for the distortion correction problem through sensitivity analysis. The precision and robustness of RFM are evaluated through synthetic and real-world experiments, considering distortion level, noise level, and sample distribution. Moreover, a practical and accurate decoupled distortion correction method is proposed using just a single captured image of a chessboard pattern. The correction performance is compared with the current state-of-the-art using camera calibration, and experimental results indicate that more precise distortion correction can enhance the overall accuracy of camera calibration. In summary, this decoupled RFM-based distortion correction approach provides a flexible, high-precision solution for applications requiring minimal calibration steps and reliable geometric accuracy, establishing a foundation for distortion-free imaging and simplified camera models in precision-driven computer vision tasks.