Springback is an unavoidable problem in cold-forming processes and affects the efficiency and quality of the processing of outer sheets for ships. Therefore, effective control and prediction of sheet-forming springback is particularly important in the field of cold-bending processes. To this end, this paper presents research on cold-bending springback prediction based on a study of the multipoint cold-bending process combined with intelligent algorithms, as well as research on the multipoint cold-bending production of ship-hull plates. The forming process of spherical sheets was simulated by a finite element simulation. The amount of springback under different processes was studied, and the forming state and springback state were briefly analyzed. Then an in-depth study of machine learning was carried out, and the sparrow search algorithm (SSA) was introduced based on a back-propagation neural network (BPNN). The purpose of this integration was to prevent the BP neural network model from falling into local optimal solution problems. Then simulation data were obtained with the help of a simulation to build a backpropagation neural network prediction model, which was optimized based on the sparrow search algorithm, and training tests were conducted. Then the prediction results of the model were compared with the simulation data to verify that the prediction accuracy performance of the sparrow-search-algorithm-optimized BPNN model was improved. Finally, the prediction model based on the SSA–BPNN algorithm was compared with the prediction models of different algorithms, and the prediction results showed that SSA–BPNN outperformed other algorithms in prediction accuracy and speed; its prediction error was within 4%, which meets on-site processing requirements. The sparrow-search-algorithm-based optimization of BPNN was confirmed to have strong applicability in springback prediction.

Amid the ongoing decarbonization of the international shipping industry, methanol has emerged as a promising alternative fuel for marine auxiliary boilers owing to its environmental advantages and engineering feasibility. However, its low viscosity and surface tension make the atomization process highly sensitive to operating conditions, posing challenges to stable and efficient burner operation. Existing studies have predominantly focused on engine applications, whereas systematic investigations into the atomization characteristics and operating-parameter matching of primary-air swirl-cup nozzles for marine auxiliary boilers remain limited. To address this gap, the present study combines experimental measurements and numerical simulations to investigate the effects of fuel flow rate, atomizing-cup rotational speed, and primary-air damper opening on spray characteristics. Spray imaging was employed to characterize the spray cone angle and macroscopic morphology, while PIV and PDA were used to measure the outer-flow-field velocity and droplet-size characteristics, respectively. Numerical simulations of liquid-film formation and breakup were performed using a coupled VOF-DPM framework. The predicted spray angle and outer-flow-field velocity showed good agreement with the experimental measurements, with overall deviations within 3–12%. Increasing the atomizing-cup speed generally promoted droplet refinement, while adjustment of the primary-air supply further influenced the droplet-size distribution. Under high-speed operating conditions, the atomized droplet size was generally maintained below 100 μm, and the SMD in the investigated near-field region was approximately 60–80 μm. Based on the multi-load experimental results, primary-air parameter-matching relationships were established for fuel flow rates ranging from 100 to 500 kg/h, providing guidance for maintaining stable atomization performance over a wide operating-load range. This study provides a quantitative basis for the operating-parameter design and stable operation of primary-air swirl-cup nozzles in marine methanol-fired auxiliary boilers and offers useful guidance for their engineering application.

Geometric degeneracy and poor illumination make localization difficult in confined metal compartments. This study examines quality-dependent pose fusion in a four-dimensional position-and-yaw model using known-map LiDAR registration, synthetic visual poses, and noisy velocity and yaw-rate inputs. A geometric-visual quality score controls the combination of iterative closest point (ICP) and normal distributions transform (NDT) pose observations, while modality-specific covariance scales and innovation checks regulate state updates. Across 60 controlled Monte Carlo trials, the full configuration achieves a mean position error of 0.029 m, a between-trial standard deviation of 0.003 m, a mean position root-mean-square error of 0.034 m, and a mean peak error of 0.112 m. Frames in error runs above 0.15 m lasting at least five samples account for 0.00%. Representative processing time is 25.65 ms per frame, excluding synthetic generation. Paired ablations identify LiDAR innovation screening as the main measured accuracy contributor; independent mean-accuracy benefits from covariance and mixing adaptation are not established. Conditional NDT reduces registration work. Sustained common pose-observation corruption raises the mean error from 0.066 m at 6 s to 0.178 m at 24 s, and a fixed reduced configuration remains competitive. A separate field point-cloud study evaluates compact map representation. These findings characterize controlled pose-fusion behavior; sensor-level LiDAR-visual-inertial performance and real-compartment trajectory accuracy require independently referenced validation.

To address the challenges of constrained grid-like compartments, a motion framework integrating adaptive obstacle avoidance planning and active disturbance rejection control is proposed. First, an Adaptive Rapidly exploring Random Tree Star (Adaptive RRT*) algorithm based on multi-source state feedback is developed. Scaled-down model simulations show that, compared to conventional algorithms, its path length (374.28 mm), planning time (0.30 s), and node count (50.83) are reduced by at least 29.5%, 64.7%, and 28.6%, respectively, achieving a 100% planning success rate. Next, a control scheme based on Extended State Observer–Model Predictive Control (ESO-MPC) is designed. Simulations indicate that under nominal conditions, tracking errors are reduced by 5.78–84.35% compared to traditional MPC. Under a 20% link mass perturbation, the scheme effectively eliminates phase lag. Under complex scenarios involving parameter perturbation and a 0.6 N·m step torque disturbance, the tracking error reduction ranges from 25.27% to 87.59%, exhibiting excellent disturbance rejection robustness. Physical experiments conducted on a scaled-down experimental platform further verify that the maximum tracking errors of the manipulator end-effector along the x, y, and z axes under ESO-MPC are 0.88 mm, 0.85 mm, and 0.89 mm, respectively, significantly outperforming the 2.41 mm, 2.39 mm, and 2.47 mm observed with MPC. Finally, obstacle avoidance and trajectory-tracking simulations of an industrial manipulator in a full-scale ship compartment environment validate the engineering feasibility of the proposed framework.