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The on-demand jetting of high-viscosity liquid has significant applications in fields such as electronic packaging and bioprinting. Conventional methods for high-viscosity liquid jetting often employ a needle propelling the liquid rapidly, which demands high precision in the manufacturing and assembly of the needle and nozzle, and can potentially damage biomaterials. In this study, a novel method utilizing jet tube impact for on-demand high-viscosity liquid jetting is proposed, leveraging the inherent inertia of the liquid to generate the pressure pulse necessary for on-demand jetting. This method reduces the precision requirements for the device, enables device simplification, and avoids harm to biomaterials. The feasibility of this approach for on-demand high-viscosity liquid jetting is validated through experiments, and by combining numerical simulations, the jetting mechanism is revealed and primary factors influencing jetting performance are investigated. It is found that the water hammer pressure wave induced by the liquid inertia during the sudden velocity change of the jet tube is the predominant driving force for jetting, and the peak pressure can exceed 1 MPa and the peak jet velocity can exceed 15 m/s. An increase in the jet tube impact velocity and an extension of the acceleration duration at the same impact velocity both lead to an increase in the pressure wave amplitude. In addition, a decrease in the liquid level height shortens the period of the pressure wave. These factors all have an influence on the jetting performance. This study provides a new insight and theoretical foundation for the on-demand high-viscosity liquid jetting. [ABSTRACT FROM AUTHOR]

In recent years, gallium-based liquid metal has emerged as a prominent research material in the realm of flexible electronics, owing to its exceptional electrical conductivity and deformability. The crux of developing flexible electronic devices using liquid metal as a conductive material lies in the patterning of the liquid metal. Inkjet printing technology possesses the capability to efficiently craft functional patterns by printing any inkable material. However, due to the facile oxidation of the gallium-based liquid metal surface in ambient air, leading to the formation of oxide films and the presence of high surface tension, conventional inkjet printing techniques are incapable of directly rendering liquid metal prints. This manuscript introduces a refined and remarkably efficient approach to inkjet printing utilizing the liquid metal. By deftly adjusting the interfacial tension of the liquid metal at the nozzle with ultra-low potentials, falling within the 2-V range, the achievement of on-demand, micron-scale liquid metal microdroplet ejection printing surpassing 3 kHz is brought to fruition. This article delves into unraveling the mechanisms of potential modulation on the interfacial tension of the liquid metal at the nozzle, as well as the fundamental theories pertaining to the formation and migration of liquid metal microdroplets. These insights establish a theoretical and technical foundation for the application of liquid metals in the field of electronic printing. [ABSTRACT FROM AUTHOR]

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]

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. [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]