In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.

In this paper, the optimization of laser-MAG hybrid welding parameters of 10CrNi3MoV ship steel was developed. Using the Box-Behnken Design (BBD) model in Response Surface Methodology (RSM) and taking laser power, welding speed and welding current as response factors, the design matrix was completed and verified by experiment. The regression model associated with welding parameters was established by measuring the response indices, such as penetration, tensile strength and impact absorption energy. Through the model check, it was found that the accuracy of penetration and tensile strength of the model was high, and the optimized parameters were as follows: laser power (P) = 3700 W, welding speed (V) = 0.8 m/min, wire feeding speed (Vs) = 7 m/min. On the premise of meeting mechanical performance inspection standards, the maximum penetration was 8 mm.

To reduce the residual stress and deformation of the copper alloy sheet after welding, and improve the welding quality of the copper alloy sheet, the finite element method (FEM) research on welding thermal deformation and welding sequence optimization was carried out. First, a finite element model of copper alloy sheet welding was established based on ANSYS, the mechanical property parameters of the model at high temperature were determined, and the thermal–structural coupling calculation was performed on the model. Then, the change trend and magnitude of the residual stress and deformation of the model after welding were analyzed. Finally, different welding sequence schemes were designed, and numerical simulation calculations were carried out. The results of the welding sequence solution show that the change trend of the residual stress after welding of the base metal under different welding sequences is basically the same; repeated heating of the base metal at the same position causes large residual stress; the weldment vertical plate is subjected to opposing forces in the x-axis and y-axis directions at the same time. Among four welding schemes, the welding scheme that alternately welds symmetrically from the start and end positions of the weld seam to the middle position of the plate causes the least welding deformation. Compared with the other three schemes, its deformation reduces by 26.6%, 18.3%, and 19.4%, respectively.

The arc weld seam is a common form in ship medium-small assemblies. In order to reduce the deformation of the welded parts with an arc weld seam, and then improve the welding quality, research on the optimization of welding sequences based on the artificial immune algorithm is carried out in this paper. First, the formation mechanism of welding deformation is analyzed by the thermo-elastic-plastic finite element method; next, the reduction in the welding deformation is taken as the optimization goal, and the welding sequence optimization model for the arc weld seam is constructed under the condition of boundary constraints; then, an immune clonal optimization algorithm based on similar antibody similarity screening and steady-state adjustment is proposed, and its welding sequence optimization ability is improved through antibody screening and median adjustment. Finally, the welding sequence optimization tests are carried out based on the Ansys platform. Numerical tests of a typical arc weld seam show that different welding sequences will cause different welding deformations, which verifies the importance of welding sequence optimization. Furthermore, the numerical test results of four different types of welds in ship medium-small assemblies demonstrated that the use of distributed optimization algorithms for welding sequence optimization can help reduce the amount of welding deformations, and the immune clonal algorithm, based on antibody similarity screening and steady-state adjustment, achieves the optimal combination of the welding sequence. Compared with the other three optimization algorithms, the maximum welding deformation caused by the welding sequence optimized by the proposed immune clonal algorithm is reduced by 3.1%, 4.0%, and 3.4%, respectively, the average maximum welding deformation is reduced by 3.5%, 5.5%, and 4.7%, respectively, and the convergence generation of the optimization algorithm is reduced by 16.8%, 13.1% and 14.5%, respectively, which further verifies the effectiveness and superiority of the proposed immune clonal algorithm in the optimization of welding sequences.

This study addresses critical challenges in manufacturing GRCop-42 Cu alloy (Cu-4Cr-2Nb) components via laser-directed energy deposition (LDED). We systematically establish process–microstructure–property correlation for this alloy, demonstrating that laser power critically governs defect formation and mechanical performance. The alloy exhibited optimal microstructure and properties at a laser power of 2000 W, with a room temperature tensile strength of 319 ± 6.5 MPa and an elongation of 25.42 ± 1.9%. The tensile strength in the high-temperature tensile test at 600 °C was measured at 98 ± 3.1 MPa, with an elongation of 15.83 ± 1.5%. The comprehensive performance reaches the optimal value of the processing window. Through cross-scale characterization techniques, the differences in fracture mechanisms at different temperatures are clarified for the first time: at room temperature, a microporous aggregation-type ductile fracture is observed, with plastic deformation primarily dominated by dislocation slip; in a high-temperature environment, due to the weakening of grain boundary strength, the fracture mode shifts to intergranular fracture, and the deformation mechanism evolves into a synergistic effect of dislocation slip and twinning. The findings of this study not only provide valuable insights into optimizing the LDED process parameters for the GRCop-42 alloy but also shed light on the relationship between its microstructure and mechanical properties under different temperature conditions, offering a solid foundation for the further application of this alloy in complex aerospace components.