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Increasing welding speed can promote the productivity of laser welding. However, humping defects often occur, which limits the application of this strategy. The existing explanations for the humping formation remain vague, and mitigation and suppression methods are limited. In this research, high-speed imaging experiments and numerical simulation of the high-speed laser welding process are performed. Through careful examination, the humping phenomenon is explained. At high welding speed, the high-speed melt flow caused by recoil pressure is hindered by the solidified region in the melt pool, leading to the occurrence of a swelling. The swelling then grows, forming a valley in front of the swelling under the effect of surface tension. The solidification of the valley results in the occurrence of a second swelling. This process repeats and humping defect forms. Marangoni force and viscous force also have influence on this process. In addition, it is found that adding a Tungsten Inert Gas arc behind the laser beam can effectively suppress the humping. [ABSTRACT FROM AUTHOR]

In order to ensure the production quality of high-speed laser welding, it is necessary to simultaneously monitor multiple state properties. Monitoring methods combining vision sensing and deep learning models are popular but most models used can only make predictions on single welding state property. In this contribution, we propose a multi-output model based on a lightweight convolutional neural network (CNN) architecture and introduce the particle swarm optimization (PSO) technique to optimize the loss function of the model, to simultaneously monitor multiple state properties of high-speed laser welding of AISI 304 austenitic stainless steel. High-speed imaging is performed to capture images of the melt pool and the dataset is built. Test results of different models show that the proposed model can achieve monitoring of multiple welding state properties accurately and efficiently. In addition, we make an interpretation and discussion on the prediction of the model through a visualization method, which can help to deepen our understanding of the relationship between the melt pool appearance and welding state. The proposed method can not only be applied to the monitoring of high-speed laser welding but also has the potential to be used in other procedures of welding state monitoring. [ABSTRACT FROM AUTHOR]

Web-core sandwich panels are a typical lightweight structure utilized in a variety of fields, such as naval, aviation, aerospace, etc. Welding is considered as an effective process to join the face panel to the core panel from the face panel side. However, it is difficult to locate the joint position (i.e., the position of core panel) due to the shielding of the face panel. This paper studies a weld position detection method based on X-ray from the face panel side for aluminum web-core sandwich panels used in aviation and naval structures. First, an experimental system was designed for weld position detection, able to quickly acquire the X-ray intensity signal backscattered by the specimen. An effective signal processing method was developed to accurately extract the characteristic value of X-ray intensity signals representing the center of the joint. Secondly, an analytical model was established to calculate and optimize the detection parameters required for detection of the weld position of a given specimen by analyzing the relationship between the backscattered X-ray intensity signal detected by the detector and the parameters of the detection system and specimen during the detection process. Finally, several experiments were carried out on a 6061 aluminum alloy specimen with a thickness of 3 mm. The experimental results demonstrate that the maximum absolute error of the detection was 0.340 mm, which is sufficiently accurate for locating the position of the joint. This paper aims to provide the technical basis for the automatic tracking of weld joints from the face panel side, required for the high-reliability manufacturing of curved sandwich structures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Inconel 617 Ni-based superalloy sheets were joined using a laser-welding process under four different welding positions. • When using high-heat input parameters, the welding position has an obvious effect on the laser weld's quality. • Different welding positions generated different levels of hydrostatic melt pressure on the keyhole surface. Inconel 617 Ni-based superalloy sheets were joined using a laser-welding process. The welding experiments were conducted on four different welding positions (i.e., flat, horizontal, vertical-up, and vertical-down), using two sets of welding parameters with different heat inputs. Then, the geometry, internal defects, mechanical properties, and molten-pool dynamics of the laser joints were evaluated. The results showed that, when using high-heat input parameters, the welding position has an obvious effect on the laser weld quality. The top surface of the flat weld and the root surface of the vertical-down weld are both slightly concave. Moreover, the horizontal weld and vertical-down weld have serious keyhole-induced porosity defects, which lead to relatively worse mechanical properties. The analysis shows that the stability of keyhole differs for different welding positions; keyhole-induced porosity is easily generated in the horizontal and vertical-down positions. Moreover, solid metal blocks escape routes of the bubbles, resulting in more serious porosity for these welding positions. However, when using low-heat input parameters, the welding position has less influence on the laser weld quality because there is less molten metal, which makes the keyhole more stable in different welding positions. [ABSTRACT FROM AUTHOR]