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.

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.

Mechanical vibration in the high-temperature forging production line often causes large forging thermal dimensional measurement error in the detection task, so a vibration point cloud compensation method based on an acceleration sensor is proposed in this study. First, the vibration signal is obtained through the built-in acceleration sensor in the laser camera. After the acceleration of the camera vibration is detected, the displacement of the camera in three directions is solved by secondary integration. Subsequently, the coordinate value of the corresponding point is obtained by the rotation matrix transformation so as to compensate and correct the point cloud deviation caused by the camera vibration. Finally, the forging point cloud is matched using the surface matching algorithm in Halcon. An automatic forging production line for wheel hubs has been built, and the key dimensions of high-temperature forging products have been measured online using the developed method. After the forging point cloud is compensated, the average measurement error of dimensions is reduced from ±0.9 mm to ±0.1 mm, and the standard deviation is reduced from 0.52 mm to 0.056 mm. Using the vibration point cloud compensation method based on the acceleration sensor, as well as using silica aerogel insulation, vibration structural parts, heat insulation and constant temperature, a blue-violet 3D laser camera, and other measures, the dimensional detection accuracy of high-temperature forgings in the forging production line can be improved, and the instability of dimensional detection can be reduced.

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.

The double-sided welding process is widely used in ship construction due to its high welding efficiency and forming quality. In order to further reduce the deformation caused by double-sided welding for medium-small assemblies in ships, the optimization of the double-sided welding sequence based on an artificial immune algorithm is carried out. First, the formation mechanism of welding deformation under the double-sided welding process is analyzed by the inherent strain method; next, the reduction of the welding deformation is taken as the optimization goal, and the welding sequence optimization model for double-sided welding is constructed; then, an immune clonal optimization algorithm based on similar antibody similarity screening and steady-state adjustment is introduced, and the immune optimization process for double-sided welding sequence is designed; finally, double-sided welding sequence optimization tests are carried out for four different types of medium-small assemblies in ships. Numerical test results show that, compared with IGA (Immune genetic algorithm), ICA (Immune clonal algorithm), and GA (Genetic algorithm), the maximum welding deformation caused by the welding sequence optimized by the proposed immune clonal algorithm is reduced by 2.4%, 2.8, and 3.3%, respectively, the average maximum welding deformation is reduced by 2.6%, 2.5, and 3.4%, respectively, and the convergence generation is reduced by 16.2%, 13.4, and 11.2%, respectively, which verifies the strong optimization ability and high optimization efficiency of the immune clonal algorithm introduced in the double-sided welding sequence optimization.