In the laser powder bed fusion additive manufacturing of metals, extreme thermal conditions create many highly dynamic physical phenomena, such as vaporization and recoil, Marangoni convection, and protrusion and keyhole instability. Collectively, however, the full set of phenomena is too complicated for practical applications and, in reality, the melting modes are used as a guideline for printing. With an increasing local material temperature beyond the boiling point, the mode can change from conduction to keyhole. These mode designations ignore laser-matter interaction details but in many cases are adequate to determine the approximate microstructures, and hence the properties of the build. To date no consistent, common, and coherent definitions have been agreed upon because of historic limitations in melt pool and vapor depression morphology measurements. In this review, process-based definitions of different melting modes are distinguished from those based on postmortem evidence. The latter are derived mainly from the transverse cross sections of the fusion zone, whereas the former come directly from time-resolved x-ray imaging of melt pool and vapor depression morphologies. These process-based definitions are more strict and physically sound, and they offer new guidelines for laser additive manufacturing practices and create new research directions. The significance of the keyhole, which substantially enhances the laser energy absorption by the melt pool, is highlighted. Recent studies strongly suggest that stable-keyhole laser melting enables efficient, sustainable, and robust additive manufacturing. The realization of this scenario demands the development of multiphysics models, signal translations from morphology to other feasible signals, and in-process metrology across platforms and scales.

Automatic joint detection is of vital importance for the teaching of robots before welding and the seam tracking during welding. For narrow butt joints, the traditional structured light method may be ineffective, and many existing detection methods designed for narrow butt joints can only detect their 2D position. However, for butt joints with narrow gaps and 3D trajectories, their 3D position and orientation of the workpiece surface are required. In this paper, a vision based detection method for narrow butt joints is proposed. A crosshair laser is projected onto the workpiece surface and an auxiliary light source is used to illuminate the workpiece surface continuously. Then, images with an appropriate grayscale distribution are grabbed with the auto exposure function of the camera. The 3D position of the joint and the normal vector of the workpiece surface are calculated by the combination of the 2D and 3D information in the images. In addition, the detection method is applied in a robotic seam tracking system for GTAW (gas tungsten arc welding). Different filtering methods are used to smooth the detection results, and compared with the moving average method, the Kalman filter can reduce the dithering of the robot and improve the tracking accuracy significantly.

Multi-layer/multi-pass welding (MLMPW) technology is widely used in the energy industry to join thick components. During automatic welding using robots or other actuators, it is very important to recognize the actual weld pass position using visual methods, which can then be used not only to perform reasonable path planning for actuators, but also to correct any deviations between the welding torch and the weld pass position in real time. However, due to the small geometrical differences between adjacent weld passes, existing weld position recognition technologies such as structured light methods are not suitable for weld position detection in MLMPW. This paper proposes a novel method for weld position detection, which fuses various kinds of information in MLMPW. First, a synchronous acquisition method is developed to obtain various kinds of visual information when directional light and structured light sources are on, respectively. Then, interferences are eliminated by fusing adjacent images. Finally, the information from directional and structured light images is fused to obtain the 3D positions of the weld passes. Experiment results show that each process can be done in 30 ms and the deviation is less than 0.6 mm. The proposed method can be used for automatic path planning and seam tracking in the robotic MLMPW process as well as electron beam freeform fabrication process.

Fe-based amorphous alloys with excellent mechanical properties are suitable for preparing wear resistant coatings by laser cladding. In this study, a novel Fe-based amorphous coating was prepared by laser cladding on 3Cr13 stainless steel substrates. The influence of scanning speeds on the microstructures and properties of the coatings was investigated. The microstructure compositions and phases were analyzed by scanning electron microscope, electron probe microanalyzer, and x-ray diffraction respectively. Results showed that the microstructures of the coatings changed significantly with the increase of scanning speeds. For a scanning speed of 6 mm/s, the cladding layer was a mixture of amorphous and crystalline regions. For a scanning speed of 8 mm/s, the cladding layer was mainly composed of block grain structures. For a scanning speed of 10 mm/s, the cladding layer was composed entirely of dendrites. Different dilution rates at the bonding zones were the main reasons for the microstructure change for different claddings. For all three scanning speeds, the coatings had higher hardness and wear resistance when compared with the substrate; as the scanning speed increased, the hardness and wear resistance of the coatings gradually decreased due to the change in microstructure.

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.