Shujun Chen

The driving and distribution of fluid is important for building integrated and functional microfluidic systems. This study presents a bidirectional micropump based on acoustically oscillating water droplet-shaped microbubbles. The micropump exploits the geometric asymmetry of the microbubbles along the pumping direction to generate asymmetric acoustic microstreaming actuated with acoustic waves, enabling directional fluid transport. Bidirectional pumping is achieved by adjusting the frequency of actuated acoustic waves, which switches the direction of the acoustic microstreaming. The prototypes were fabricated, and experimental tests were conducted. Experimental results show that water droplet-shaped microbubbles generate out-of-plane microstreaming near the base ends when the frequency of the actuated acoustic waves is between 11.85 and 13.11 kHz. As a result of that, the net flow from the base end to the tip of the water droplet-shaped microbubbles is formed by the superposition of the array of acoustic microstreaming. The micropump achieved its peak forward flow of 2658 nl/min at an acoustic frequency of 12.32 kHz and 60 Vpp. A reverse out-of-plane microstreaming is generated at the middle of the microbubbles when the frequency of acoustic waves is within the range of 18.22–19.35 kHz. A reverse flow from the tip to the base end is generated by the superposition of acoustic microstreaming produced by the microbubble array. A peak reverse flow of 1230 nl/min was achieved at 18.55 kHz and 140 Vpp. This study proposes a flexible bidirectional micropump, offering a solution for the dynamic regulation of flow direction in microfluidic systems.

High-frequency pulsed Gas Tungsten Arc Welding (GTAW) break critical limitations in conventional welding by enabling independent control of weld penetration and width which is essential for thin-walled structures and heat-sensitive materials. Our previous studies indicated that high-frequency pulsed GTAW (10kHz-40kHz) exhibits significant advantages over conventional continuous arc welding, particularly in arc constriction, stability and weld quality control. However, the underlying physical mechanisms governing arc behavior under high-frequency pulsing, especially the waveform of pulse input, remained unclear. To reveal these mechanisms, in this study, based on the assumptions of magneto-hydrodynamics and local thermal equilibrium, a transient two-dimensional axisymmetric numerical model of high-frequency multi-pulse GTAW was established to explain how pulse parameters affect arc temperature distribution, electromagnetic forces, and energy transfer. The numerical analysis results revealed that increasing the pulse frequency primarily leads to an increase in the average temperature of the high-temperature region of the arc, a reduction in the overall area of the arc, and has a greater impact on the weld penetration. Increasing the number of pulses in a single cycle mainly constricts the overall arc, increases the area and proportion of the high-temperature region, and has a more prominent impact on the weld width. Finally, welding experiments conducted on Q235 steel show high consistence with the simulation. This mechanistic understanding enables precise design of arc characteristics through dual-parameter control (pulse frequency and pulse number) to meet diverse operational requirements.

Molten pool flow and keyhole status during Variable Polarity Plasma Arc (VPPA) welding directly affect the weld quality and stability. The lack of a clear correlation between them, however, prevents this process approach from being developed further. To investigate the keyhole morphology and liquid metal flow, the experimental examination of fluid flow by the X-ray imaging method and numerical simulation of plasma arc under the effect of the keyhole were carried out. By changing the tungsten electrode setback while keeping all other parameters, it is possible to vary the keyhole status and maintain the consistency of heat input to the base metal. This work establishes a dual-bifurcation flow model to characterize the keyhole molten pool, where the bifurcation point on the keyhole rear wall significantly affects the stability of the keyhole molten pool. The rear wall of the keyhole is divided into three sections from top to bottom, with the arc pressure in the middle section being significantly higher than in the upper and lower sections. As the degree of arc constriction increases—i.e., as arc stiffness or arc force increases—the middle section becomes more vertical. By the calculated distribution of driving forces, the arc pressure has a high possibility of being one of the dominances for the metal flow in keyhole welding of aluminum alloys. Arc pressure is also important for the bifurcation point position, which is closely related to the three welding states: blind keyhole, keyhole, and cutting.

This work aims to elucidate the physical characteristics of the arc and the state of the tungsten electrode under the constraint effect of gas flow. Tungsten electrodes containing the elements Ce, Zr, La, and Th were selected for the study. Through high-speed imaging, spectral diagnostics, and numerical simulations, the distribution patterns of the physical fields of the arc were obtained. Compared to pure tungsten, tungsten electrodes containing trace elements are more resistant to erosion and better maintain their integrity. The differences among electrodes with various trace elements are relatively small. When a constrained gas flow is introduced, the arc contracts, and the arc contraction range is 5.8%–8.6%. The gas flow not only increases the energy density of the arc but also provides cooling effect. Changes in the gas flow rate alter the dominance of these two effects, thereby influencing the arc width. By combining the surface morphology of the tungsten electrode with the results of numerical simulations, it is found that the constrained gas flow can effectively inhibit the diffusion of metal vapor. When the gas flow rate is 1–2 l/min, the concentration of metal vapor can be reduced by 75%; when the gas flow rate exceeds 3 l/min, its concentration will increase again. In addition, the degree of metal vapor diffusion under different gas flow rates may be another factor contributing to the changes in arc width.

The development of artificial intelligence (AI)-based industrial data-driven models is the driving force behind the digital transformation of manufacturing processes and the application of smart manufacturing. However, in real-world industrial applications, the intricate interplay among data quality, model accuracy, and generalizability poses significant challenges, hindering the effective deployment and scalability of data-driven models in complex manufacturing environments. To address this challenge, this paper proposes a universal Physics-informed Hybrid Optimization framework for Efficient Neural Intelligence (PHOENIX) in manufacturing, demonstrating its applicability in robotic welding scenarios. This framework systematically integrates physical principles into its input, model structure, and dynamic optimization processes, enabling proactive, real-time detection and predictive of welding instability. It achieves an accuracy of up to 98% for predictions within the next 50 ms and maintains an accuracy of 86% even for forecasts up to 1 s in advance. Through physics-informed data-driven modeling, the framework significantly reduces the dependence on high-cost data while maintaining the performance of the original model. By leveraging cloud-based optimization modules that integrate new data with historical experience, the framework enables autonomous model parameter optimization, ensuring its continuous adaptation to the complex and dynamic demands of industrial scenarios.

Friction stir welding (FSW) is a solid-state joining technique widely used for aluminum alloys in aerospace, automotive, and shipbuilding applications, yet the prediction of fatigue fracture locations within FSW joints remains challenging for structural-life assessment. In this study, we investigate fatigue fracture location prediction in 7075-T651 aluminum alloy FSW joints by applying four machine learning methods—decision tree, logistic regression, a three-layer back-propagation artificial neural network (BP ANN), and a novel Quadratic Classification Neural Network (QCNN)—using maximum stress, stress amplitude, and stress ratio as input features. Evaluated on an experimental test set of eight loading conditions, the QCNN achieved the highest accuracy of 87.5%, outperforming BP ANN (75%), logistic regression (50%), and decision tree (37.5%). Building on QCNN outputs and incorporating relevant material property parameters, we derive a Regional Fracture Prediction Formula (RFPF) based on a Fourier-series quadratic expansion, enabling the rapid estimation of fracture zones under varying loads. These results demonstrate the QCNN’s superior predictive capability and the practical utility of the RFPF framework for the fatigue failure analysis and service-life assessment of FSW structures.

In situ cleaning of the weld area on a substrate or weld beads is performed by adjusting power and the laser scanning speed using a conventional continuous high-power laser typically employed for welding or deposition. This process facilitates the removal of contaminants (e.g., oil residues, metal oxides, and post-weld black ash) and enables efficient planning and execution of the welding process. The influence of varying laser scanning power on the cleaning efficacy of a 6061 aluminum alloy substrate was examined. The findings revealed that, as the laser scanning power increased from 1200 W to 3900 W, the cleaning effectiveness on the aluminum alloy surface initially improved and then diminished. At lower laser scanning power levels, the energy was insufficient to evaporate and disintegrate the surface contaminants, whereas excessively high laser power tended to cause secondary burns and oxidation on the material surface. Additionally, excessively high laser scanning speeds reduced the overlap of the scanning area, thereby compromising the cleaning results. The proposed surface cleaning method, utilizing a standard continuous high-power laser, demonstrated satisfactory cleaning performance through visual inspection, oxide detection, and analysis of porosity defects in the weld beads produced post-cleaning. Pre-laser scanning and cleaning significantly reduced the incidence of porosity and enhanced weld quality. This method eliminates the need for a specialized laser cleaning system powered by short-pulse lasers and can serve as a supplementary approach to conventional cleaning methods or in situations where alternative methods are unavailable. Oscillating laser scanning can also be applied to clean curved weld surfaces, effectively removing contaminants and oxides from the deposited weld surface, which is particularly beneficial for multi-layer, multi-pass welding or additive manufacturing processes.

The introduction of ultrahigh-frequency pulse gas tungsten arc welding has revolutionized the precision of heat input control in welding arcs, presenting new and promising research directions for welding processes. However, due to current limitations in imaging technology, only steady-state images of the arc under 100 kHz pulse current conditions can be captured, making it challenging to display instantaneous dynamics. This has led to a lack of in-depth studies on the arc morphology and characteristics of ultrahigh-frequency adjustable multipulse gas tungsten arc welding (UFMP-GTAW) at this frequency. To bridge this gap, a transient two-dimensional axisymmetric numerical simulation model for 100 kHz UFMP-GTAW was established in this paper, based on magnetohydrodynamics and the assumption of local thermal equilibrium. The model used Maxwell’s equations to simulate the physical behavior of the arc. A 100 kHz UFMP-GTAW arc model was generated and analyzed using simulation technology. The results show that the macroscopic morphology of the 100 kHz UFMP-GTAW arc obtained through simulation remains highly stable within a single-pulse cycle, which is consistent with the actual steady-state arc images. The high-intensity energy regions inside the arc exhibit regular droplet-like flows that migrate from the cathode to the anode. Variations in the current, temperature, and current density exhibit asynchronous characteristics. The peak and average values of parameters such as the arc temperature and current density have increased significantly. This method’s unique current waveform design and ultrahigh-frequency mechanism enable it to achieve a high-energy state of arc plasma at a lower average current level, which provides a reference for the precise control of welding quality and improves the welding energy utilization efficiency.