This review examines the methods used to optimize the process parameters of laser cladding, including traditional optimization algorithms such as single-factor, regression analysis, response surface, and Taguchi, as well as intelligent system optimization algorithms such as neural network models, genetic algorithms, support vector machines, the new non-dominance ranking genetic algorithm II, and particle swarm algorithms. The advantages and disadvantages of various laser cladding process optimization methods are analyzed and summarized. Finally, the development trend of optimization methods in the field of laser cladding is summarized and predicted. It is believed that the result would serve as a foundation for future studies on the preparation of high-quality laser cladding coatings.

In this study, WC reinforced Ni-based composite coatings with Y₂O₃ addition were deposited on Ti-6Al-4V titanium substrate by laser cladding. The phases, microstructure, microhardness and wear resistance of the composite coatings were studied by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, microhardness tester and wear tester. The results showed that good metallurgical bonding was achieved between the composite coatings and substrate. The phases mainly were γ-Ni, TiC, TiB₂, Ni₃B, M₂₃C₆ and WC. Most of the WC was dissolved in small pieces of WC during the laser cladding process. The microhardness of the composite coatings was about 3 times that of the titanium substrate and the wear resistance of the composite coatings had a significant increase.

Seam-tracking technology based on vision sensing is the key technology to realize robot intelligent welding. Because of changes in the weld gap, the diversity of workpiece material and surface conditions, and the interference of welding noises, it is extremely difficult for the robot to realize real-time seam-tracking in gas metal arc welding (GMAW) of large structures. So, a method of position detection and weld gap feature extraction is proposed for GMAW seam-tracking system of fillet weld with variable gap. This method established a highly robust weld feature extraction model based on a low-cost structured-light vision sensor. In this model, using the reflection characteristic of the stripes on fillet weld, a feature extraction method for variable gap weld based on column gray difference operator was proposed. To overcome the interference of welding noises, this model adopted the AND logic operation method between adjacent sampled images. To overcome the interference of uneven and strong reflection conditions, an optimized random sampling consistency (RANSAC) algorithm was proposed and used for weld feature extraction. The optimized RANSAC algorithm used the linear slope of the stripes to optimize the random sampling process of the algorithm. The experimental results showed that the average detection errors of the proposed method in the Y-axis and Z-axis directions are 0.19 mm and 0.23 mm, respectively, at a welding speed of 1000 mm/min. The proposed method could realize seam tracking of the variable gap fillet weld and still had good robustness under the uneven and strong reflection of the workpiece surfaces.

Lack of fusion can often occur during ultra-thin sheets edge welding process, severely destroying joint quality and leading to seal failure. This paper presents a vision-based weld pool monitoring method for detecting a lack of fusion during micro plasma arc welding (MPAW) of ultra-thin sheets edge welds. A passive micro-vision sensor is developed to acquire clear images of the mesoscale weld pool under MPAW conditions, continuously and stably. Then, an image processing algorithm has been proposed to extract the characteristics of weld pool geometry from the acquired images in real time. The relations between the presence of a lack of fusion in edge weld and dynamic changes in weld pool characteristic parameters are investigated. The experimental results indicate that the abrupt changes of extracted weld pool centroid position along the weld length are highly correlated with the occurrences of lack of fusion. By using such weld pool characteristic information, the lack of fusion in MPAW of ultra-thin sheets edge welds can be detected in real time. The proposed in-process monitoring method makes the early warning possible. It also can provide feedback for real-time control and can serve as a basis for intelligent defect identification.

To solve the problems of low teaching programming efficiency and poor flexibility in robot welding of complex box girder structures, a method of seam trajectory recognition based on laser scanning displacement sensing was proposed for automated guidance of a welding torch in the skip welding of a spatially intermittent welding seam. Firstly, a laser scanning displacement sensing system for measuring angles adaptively is developed to detect corner features of complex structures. Secondly, a weld trajectory recognition algorithm based on Euclidean distance discrimination is proposed. The algorithm extracts the shape features by constructing the characteristic triangle of the weld trajectory, and then processes the set of shape features by discrete Fourier analysis to solve the feature vector used to describe the shape. Finally, based on the Euclidean distance between the feature vector of the test sample and the class matching library, the class to which the sample belongs is identified to distinguish the weld trajectory. The experimental results show that the classification accuracy rate of four typical spatial discontinuous welds in complex box girder structure is 100%. The overall processing time for weld trajectory detection and classification does not exceed 65 ms. Based on this method, the field test was completed in the folding special container production line. The results show that the system proposed in this paper can accurately identify discontinuous welds during high-speed metal active gas arc welding (MAG) welding with a welding speed of 1.2 m/min, and guide the welding torch to automatically complete the skip welding, which greatly improves the welding manufacturing efficiency and quality stability in the processing of complex box girder components. This method does not require a time-consuming pre-welding teaching programming and visual inspection system calibration, and provides a new technical approach for highly efficient and flexible welding manufacturing of discontinuous welding seams of complex structures, which is expected to be applied to the welding manufacturing of core components in heavy and large industries such as port cranes, large logistics transportation equipment, and rail transit.