Electrohydrodynamic jet printing enables the generation of femtoliter-scale droplets, offering advantages such as additive manufacturing, fine patterning, and high process efficiency. With these advantages, electrohydrodynamic jet printing technology holds broad application potential for repairing complex electronic structures at micro- and nanoscales. This technology has entered industrial application. However, precise droplet volume control is required during repair, and conventional methods often fail to achieve both high efficiency and accuracy. In this work, a physics-guided closed-loop droplet volume control framework is proposed by integrating meniscus-based physical modeling with data-driven reinforcement learning. Key process parameters are reduced to physically interpretable equivalent parameters via physics-inspired dimensionality reduction, significantly simplifying the control space and enabling efficient learning. A reinforcement learning strategy is then employed to adaptively regulate these equivalent parameters, with a reward function directly reflecting droplet volume deviation and filling accuracy to ensure alignment with practical manufacturing objectives. The framework is experimentally validated on an industrial E-jet printing platform. Using a 1200 ppi pixel pit substrate as a representative case, the droplet filling rate is improved from 65.6

Dot array deposition through multi-nozzle electrohydrodynamic (EHD) printing is widely used for high efficiency. However, the conventional study mainly focuses on the plain capillary array. The research on the mechanisms and design principles for extractor-electrode EHD nozzle array chip remains scarce. The extractor-electrode EHD chip is beneficial for improving the printhead's coaxial accuracy and integration level. In this work, the crosstalk mechanism and design principles of the extractor-electrode EHD nozzle array chip are revealed and proposed. A model is established by combining the Navier-Stokes equation, charge transportation equation, and phase field equation. The nozzle's conductivity is considered by introducing a charge transportation equation in the nozzle chip. The effect of the electric field on the liquid supply is analyzed. The mechanism of the generated droplet deflection is revealed. The droplet deflection is determined by the electric field of the nozzle chip and the charge ejected with the fluid. The most lateral droplets deflect to the middle of the nozzle chip, which is different from the plain capillary arrays. The effect of high potential electrode, nozzle conductivity, and nozzle structure on the printing feasibility and consistency is investigated. The large high-potential electrode, low nozzle conductivity, large nozzle spacing, and proper size/position of the extractor electrode are preferred for better printing consistency. The design guidelines of the extractor electrode and a new dumb nozzle design are finally proposed to enhance the printing feasibility and consistency. This work could be beneficial for understanding and designing the extractor-electrode EHD nozzle array with high uniformity.

The vibration suppression performance of nonlinear energy sink (NES) exhibits strong amplitude dependence, which limits its applicability when facing dynamic systems with significant amplitude and frequency changes. For cantilevered fluid-conveying pipes, the flutter response is characterized by large amplitudes and marked frequency variations with flow velocity. Consequently, the single NES is only effective within a narrow range of flow velocities. To achieve flutter suppression across a wide range of flow velocities, this paper proposes a flutter control technique for cantilevered pipes based on a distributed array of graded NES units. Through theoretical modeling and numerical analysis, this study systematically elucidates the advantages of the distributed NESs in flutter suppression and the collaborative mechanisms among the components of distributed configuration. Subsequently, a design strategy is proposed to customize the performance of distributed NESs by adjusting the proportion of high stiffness NES units in the distributed configuration. The design parameters of distributed NESs are optimized and analyzed using an improved particle swarm optimization (PSO) algorithm. The results demonstrate that the distributed NESs achieves pronounced and robust flutter suppression across the entire wide range of flow velocities examined. Moreover, the distributed NES system exhibits considerable flexibility with respect to installation location, achieving effective flutter suppression even when placed in the vicinity of the optimal position.

Organic light-emitting diode (OLED) technology is particularly favored for such devices due to its broad color spectrum, design flexibility, low power consumption, and suitability for miniaturization. While conventional OLED fabrication predominantly relies on vapor deposition, inkjet printing has recently emerged as a promising alternative for large-area OLED manufacturing because of its high material utilization and capability for scalable patterning. In this process, light-emitting materials are deposited in liquid form onto the substrate and subsequently dried to form a uniform thin film. However, several challenges remain. In particular, the solvent evaporation rate must be precisely controlled to minimize droplet shrinkage during drying, which can compromise film uniformity and adhesion. Moreover, external parameters such as temperature and humidity significantly influence the drying dynamics, and the choice of solvents and polymers plays a critical role in achieving the desired film quality. To address these challenges, we propose optimizing the ambient pressure to achieve improved curing morphology.

According to the proposed four-layer modified neural network with adaptive learning, an adaptive learning model is designed, and the Play operator weight function update algorithm, the dead-zone operator weight function update algorithm, and the hybrid model program are studied. The hysteresis nonlinearity of a piezoelectric stack actuator at multiple frequencies was tested separately, and the root mean square error (RMSE) of five control methods, including the without control, classic PI and DZ model, and the four-layer modified neural network with an adaptive learning model, were compared through experimental studies. The experimental results show that compared with the without control condition, the RMSE of the classic PI and DZ model is reduced by 67.98% at a frequency of 1 Hz, which can effectively reduce the hysteresis nonlinearity of the piezoelectric stack actuator and has a good hysteresis compensation effect. Compared with the classic PI and DZ model, under the four-layer modified neural network with an adaptive learning model, the RMSE of the piezoelectric stack actuator is reduced by 15.34% at 1 Hz, and the error can still be reduced by 67.75% even at 10 Hz. Indicating that the four-layer modified neural network with an adaptive learning model still has a good hysteresis compensation effect at a wider frequency band.

Magnetoactive soft actuators have attracted considerable attention in the fields of soft robotics, flexible electronics, and biomedicines. Their mechanical study is critical for design and control. However, the theoretical investigations into the deformation mechanisms of these magnetoactive soft structures remain scarce due to geometric and material nonlinearities, as well as the complex coupling of stretch/compress, bending, and shear effects. This work develops a model to study the nonlinear statics and dynamics of hard-magnetic soft (HMS) beams with both ends supported. A novel model is proposed that accounts for both geometric and material nonlinearities, and its validity is verified by comparison with prior finite element results. Through extensive calculations, the effects of actuation strength, magnetization gradient, and the angle (or angular velocity) of the external magnetic field on the static and dynamic responses of the supported HMS beam are studied in detail. The proposed theoretical model and the resulting conclusions are intended to support the design and application of smart soft structures.

Electrohydrodynamic atomization printing is a very promising technology for thin film deposition. Mainly due to the ability to form plumes of highly uniform droplets under electrostatic fields. However, the plume divergence mechanism in the printing process and the parametric influence of deposition uniformity are still not well explained. This study proposes a multiphysics field coupling model based on three-dimensional Lagrangian particles, which is capable of completely and accurately describing the morphology of electrohydrodynamic atomization printing patterns and the deposition characteristics of charged droplet plume. The time-series deposition and force analysis of printing process are discussed. In addition, by analyzing the relationship between the effects of different parameters on the printing process, the mechanisms affecting the pattern morphology and deposition uniformity are revealed. The results show that the radial expansion of the plume continuously increases the inhomogeneity between the deposition center and the edge. The electric force drives the axial movement of the droplets, while the magnitude of the Coulomb force determines the morphology and spreading extent of the plume. There is a maximum value of deposition uniformity over the range of variation of voltage and flow rate and a minimum value of deposition uniformity over the range of variation of viscosity. Increases in solution conductivity and surface tension can enhance deposition uniformity, while the relative dielectric constant has the opposite effect. These studies contribute to a better understanding of the electrohydrodynamic atomization printing process, provide a theoretical basis for parameter selection and solution formulation, and can provide valuable guidance for optimizing the printing process.

Electrohydrodynamic direct-writing (EDW) is widely applied in the field of micro-nano manufacturing due to its advantage of rapid deposition of line structure. However, due to the difficulty of bending-torsion coupled modeling for EDW with large deformation, the mechanism and effective control strategies of EDW still unclear. This study firstly establishes a three-dimensional geometrically exact model for EDW using Euler angles. It contains differential equations for the mass conservation, the jet geometry, the jet kinematics, the jet dynamics, and the electric field and charge. Euler angles are used to exactly describe the jet’s geometric variation. A theoretical electric field description for non-uniform electric field is introduced. The universality of the model is validated by reducing the governing equations system to a two-dimensional steady-state situation. The geometrically exact model established in this study is useful for revealing the mechanism of EDW and exploring its control strategies.

Flexible electronics, including biosensors and new displays, are revolutionary devices and are facing a major manufacturing hurdle. Metal nanowire is the foundation of flexible electronics. It requires a process that is high precision (<= 500 nm), compatible with diverse functional inks (viscosity in the range of 5-1,0000 mPa s) and efficient for large-scale production. Traditional techniques typically satisfy only one or two of the mentioned three aspects, making a transformative technology desirable. Electrohydrodynamic (EHD) printing emerges as a highly promising solution to this triple challenge. Its unique 'pull' principle allows it to print nanoscale features, handle a wide range of ink materials and achieve high throughput with multinozzle systems. This effectively integrates the key advantages needed for advanced flexible electronics manufacturing. However, to fully realise its potential, several challenges must be addressed. A deeper theoretical understanding of the complex interaction between the metal particle ink properties and the electric field is needed to guide ink formulation. Furthermore, developing precise control strategies for voltage and flow rate is crucial to ensure stable and reliable printing performance. The solutions for the problem of nozzle blockage also need to be found. Moreover, the proposal of post-treatment of deposited metal particle ink line is needed for obtaining excellent nanoscale metal lines. The stability of printing should be improved using focus-assisted EHD (FA-EHD) printing. FA-EHD can stabilise EHD printing from the effect of 0th/1st/2nd-order instability. Overcoming these challenges will make EHD printing a key manufacturing technology that drives the evolution of next-generation, high-performance flexible electronics.

The use of printing to manufacture organic light-emitting diodes (OLEDs) is currently one of the most competitive technologies, especially in the mass production manufacturing of large-size substrates. As human technology develops and advances, the requirement for screen resolution gradually increases, which is mapped to the printing manufacturing process and requires higher spraying accuracy. However, in the online printing process, due to the long time and high frequency of spraying, the nozzle drop offset has the probability of state transfer. At this point, the old printing planning parameters will lead to defects such as scattering, bridging, etc.; and in order to improve efficiency, each pixel pit is printed by a combination of multiple nozzles, which directly increases the difficulty of defect traceability. In addition, it is also necessary to realize the regulation of printing parameters in the shortest possible time, which is a great challenge for the large time-lag control algorithm. In this paper, artificial intelligence is introduced into the printing manufacturing process, and an online patterned printing traceability-regulation algorithm is proposed. The algorithm mainly consists of two modules, defect traceability and parameter regulation, which are used to update the printing planning parameters. The correspondence between our pixels and nozzles is constructed as two-part graph data, and the information extraction and inference of the traceability process is carried out by the proposed network combining vision and graph attention network to realize the traceability; and the PPO-based parameter regulation network is designed to adjust the compensation value of nozzles for each abnormal nozzle, and intelligently regulate the printing planning parameters to solve the problem of regulating the defects of the on-line printing drop point. In this paper, the on-line patterned printing traceability-regulation algorithm is verified in the self-developed NEJ-PRG4.5 printing equipment, and the traceability accuracy reaches 98.7%, which is about 34% higher than the manual traceability.

Electrohydrodynamic atomization printing (EHDAP) is a promising additive manufacturing technique and has been widely used in micro/nano-scale thin-film manufacturing. However, due to the multi-stage rheological process of EHDAP and the electrical crosstalk, high-throughput multi-capillary EHDAP for thin-film fabrication is constrained. A parallel-interleaved array design to address high-throughput, crosstalk-free printing was proposed. And a comprehensive array electrohydrodynamic atomization model is proposed as a general tool for the design and optimization. This method innovatively incorporating the space charge field formed by plume flow to enhance the accuracy of simulation results. Combined with experimental validation, design criteria for capillary array spacing are provided. For the first time, a systematic analysis was conducted on the impact of multi-row array capillary distribution patterns on additive manufacturing quality. By adopting a parallel-interleaved array and optimizing nozzle spacing, the impact of electrical crosstalk can be reduced, while enhancing the uniformity and efficiency of additive manufacturing. Rapid fabrication of thin-films (1-3 mu m) with high uniformity (>95%, reached a maximum of 98.98%) achieved on 12-inch wafers (300 & times;300 mm) and 370 & times; 470 mm large-size glass substrates, which lays the foundation for the development of high-quality rapid additive manufacturing technology for micro- and nano-scale thin-films.

Electrohydrodynamic inkjet printing technology can generate femtoliter-scale droplets, which provides significant advantages in additive manufacturing. With these advantages, electrohydrodynamic inkjet printing technology shows broad application prospects in repairing micro/nano-scale complex structures in flexible electronic devices and high-resolution displays. During the repair process, precise control of printed droplet volume is required according to target volume requirements. However, due to the complexity of the printing process, traditional theoretical and simulation methods face challenges in achieving effective volume control. This paper proposes a supervised learning-based electrohydrodynamic droplet volume control method. The algorithm innovatively establishes new strategy samples through historical datasets, which include the deviation between current droplet volume and target volume, current process parameters, and changes in process parameters for the next iteration. Based on a feedforward control strategy, we employ a multilayer perceptron (MLP) supervised algorithm to achieve printing parameter recommendation, significantly improving printing efficiency. Volume control experiments conducted on the established electrohydrodynamic printing platform show that the standard volume filling rate can reach 98%, and the control can be completed within a single control cycle.

Plantar energy harvesters demonstrate significant potential for continuously powering portable electronic devices. However, low output power and poor wearing comfort remain critical challenges hindering the practical deployment of plantar energy harvesters. To effectively address the aforementioned issues, this paper proposes a novel adaptive damping high-power plantar energy harvesting (DPEH) system. A bidirectionally extensible cross-beam array structure is designed, combined with pulley block-belt composite transmission system. It achieves a 17.6-fold stroke amplification, improving the power output of the DPEH significantly. Furthermore, a hybrid long short-term memory-Kolmogorov-Arnold network (LSTM-KAN) framework was developed to dynamically identify movement speeds during walking and running, achieving an accuracy rate of 99.84%. Real-Time control of DPEH electromagnetic damping via STM32 microcontroller, optimizing comfort-power tradeoffs. Experimental results demonstrate that ergonomic comfort improved by 12%. A 75 kg tester wearing the DPEH generated a peak power of 20.81 W and an average power of 2.75 W while running at 7 km/h. This strategy constitutes a new paradigm for self-regulating human-machine collaborative energy systems.

The large-scale droplet evaporation mechanism has a key impact on industrial applications such as spray cooling and printed displays. In this study, we developed a theoretical algorithm based on the concept of a critical shading distance to predict the evaporation rate and lifetime of droplet arrays. Exceptionally, the analysis was extended to an array containing 10 000 droplets. The results reveal that the evaporation lifetime gradually increases from the array edges toward the center, eventually converging to a stable value at the center. Notably, the width of the transition region, where the evaporation lifetime increases, does not continue to expand with the overall size of the array. However, the topological characteristics of the droplet array, such as droplet spacing and contact angle, exert a significant influence on the size of this transition region. Among these factors, increasing the droplet spacing is the only adjustment that effectively enhances the uniformity of evaporation time across the array. Finally, experimental measurements confirm the accuracy of the proposed theory, demonstrating that the evaporation times of corner and center droplets remain essentially unchanged as the array size increases.

Fluid-conveying pipes are widely used across various engineering fields, including aerospace, marine, nuclear and mechanical systems. Establishing a theoretical model that balances high accuracy with computational efficiency is essential for investigating the nonlinear dynamical behavior of such systems. In this study, a new fifth-order Taylor expansion model is proposed to improve the representation of the bending curvature compared to the conventional third-order approximations. By applying the axial inextensibility condition, the kinematic relationship between transverse and axial displacements of the deformed pipe is obtained. Using Hamilton’s principle, the nonlinear governing equation of motion for a cantilevered fluid-conveying pipe is derived within the fifth-order Taylor expansion framework. The resulting partial differential equation is spatially discretized via the Galerkin method and numerically solved using the fourth-order Runge-Kutta algorithm to analyze the nonlinear dynamic responses. Numerical calculations are conducted to compare the computational accuracy and efficiency of the proposed fifth-order Taylor expansion model against both the traditional third-order model and the geometrically exact model. In addition, the influence of two key parameters—mass ratio and gravity parameter—on the dynamical behavior of the pipe is further examined under both high and low flow velocities. Results show that the fifth-order Taylor expansion model offers improved accuracy and wider applicability over the third-order model.

Pipes conveying fluid are important components in modern engineering systems, such as aerospace, marine and nuclear power equipment. Under external excitations, these pipes may exhibit complex nonlinear vibration phenomena. Predicting the long-term nonlinear vibration behavior of such pipes based on limited data has become a key scientific challenge in both academia and industry. To address this issue, a novel hybrid neural network model is proposed, by integrating three core architectures: the convolutional neural network (CNN), long short-term memory network (LSTM), and Transformer model. It is enhanced with attention mechanisms and optimized via a Bayesian approach for hyperparameter tuning. Specifically, the model leverages CNN for extracting spatial feature, LSTM for capturing temporal dependencies, and Transformer for modeling global dynamic characteristics. Two base architectures, i.e. the CNN-Attention-LSTM-Attention and the CNN-Attention-Transformer, were constructed integrated through a multi-expert feature fusion strategy, resulting in the Bayesian-optimized multi-expert feature fusion (BO-MEFF) model. The computational validation and autoregressive predictions were conducted using limited vibration displacement response data from the nonlinear forced vibration of a fluid-conveying pipe. The results demonstrate that the predictions of the proposed model are in close agreement with those from classical numerical simulations, confirming its stability and effectiveness in capturing nonlinear dynamic characteristics under varying excitation conditions. Thus, this study establishes a novel neural-network-based forecasting framework for predicting nonlinear vibrations in the dynamical system of pipes conveying fluid, and is expected to offering computationally efficient and accurate alternative for addressing complex fluid–structure interaction problems in slender structures.

Ultrathin film has been widely used in all kinds of electronic devices. The traditional manufacturing technology is no longer able to meet the current demand for ultrathin film manufacturing, and there is a contradiction between manufacturing efficiency and quality. The electrohydrodynamic atomization printing (EHDAP) has the advantages of high material utilization, low equipment occupancy and high-quality manufacturing, and is an advanced manufacturing technology. This paper proposes an arrayed electrohydrodynamic atomization spraying process based on stable conical jet patterns. Through simulation modeling the transition range for electrohydrodynamic cone-jet atomization spraying modes was determined. The range of dimensionless parameters for stable jet and deposition is provided. Based on thin-film manufacturing experiments, the relationship between droplet cluster size distribution and film thickness is investigated. And the array atomization nozzle and printing system were successfully developed. Experimental results demonstrate that this system enables ultra-thin film manufacturing with high quality. This research provides a viable technical solution for ultrathin film fabrication and advances the development of EHDAP.

Inkjet printing technology for fabricating organic light-emitting diode display panels offers advantages such as high material utilization and the capability for large-area manufacturing. When printing display panels with varying resolutions, ejecting droplets of different sizes from the nozzle is often necessary to balance print quality and efficiency. However, due to nozzle size limitations, the volume range of stable droplets produced by a single-pulse driving waveform is relatively narrow, with the maximum volume being less than twice the minimum volume. Therefore, approaches based on superposition of multi-pulse waveforms have attracted attention, but existing studies only implement manual design of waveforms based on experimental laws and rarely involve automatic regulation of multi-pulse waveform parameters, which is not favorable for industrial applications. Based on combining a meniscus vibration model with industrial ejection data, this paper extracts control strategies from historical data using deep reinforcement learning, and recommends initial waveform parameters through a fuzzy system. Then, the multi-pulse waveform parameters are automatically adjusted in real-time based on the observed droplet volume to fuse the droplets at the nozzle, enabling a wider range of droplet volume closed-loop control. Experiments on industrial inkjet printing equipment implemented intelligent closed-loop regulation of multi-pulse driving waveforms, successfully controlling droplets of different sizes such as 2, 4, and 8 picoliter with an error accuracy of less than ±4%. This approach applies artificial intelligence algorithms to inkjet printing engineering and intelligently adjusts the multi-pulse waveform parameters to enhance the controllable range of droplet volumes.

Fluid-conveying pipes have been widely used in diverse engineering fields, particularly in aerospace systems, nuclear power plants, oil transportation infrastructure, and biomedical devices. The recent advancements in 3D printing and materials science have increased research interest in the stability and vibration characteristics of slender pipes fabricated from hard magnetic soft (HMS) materials for magnetic control applications. Although several theoretical investigations have been conducted on magnetically controlled cantilevered fluid-conveying pipes, the understanding of their dynamical behavior in vascular environments remains incomplete. In this study, we investigate the buckling and dynamical behaviors of an HMS pipe under the combined effects of an applied magnetic field and nonlinear distributed spring constraints. By solving the nonlinear governing equation, natural frequencies, critical flow velocities, buckling displacements, and dynamic responses of the HMS pipe conveying fluid are obtained. The analysis reveals that the addition of distributed spring constraints leads to a substantial reduction in both buckling and dynamic displacements of the pipe system. Under constant magnetic field conditions, the pipe exhibits static deformation characteristics even when exposed to flow velocities exceeding the critical threshold for buckling instability. When subjected to an alternating magnetic field, the pipe system exhibits periodic oscillatory behavior across a wide range of flow velocities. This periodic response is characterized by displacement variations that show direct correlation with changes in the magnetic declination angle. Notably, nonlinear resonance phenomena associated with the first-mode natural frequency can occur even when the flow velocity is below the threshold for buckling instability. These results demonstrate that both magnetic field strength and declination angle offer a possible means for adjusting the stability, buckling behavior, and dynamic response of an HMS pipe.

The application of advanced technologies such as virtual reality (VR) and augmented reality (AR) is driving the innovation of ultra-high-resolution display panels, such as Micro-Organic Light-Emitting Diodes (Micro-OLEDs). Micro-OLEDs typically consist of multilayer structures, and some researchers have opted for electrofluidic inkjet printing technology over the traditional vapor deposition process. This preference is due to its advantages in achieving high resolution, enabling additive manufacturing. However, when printing multilayer structures, the deposition charge from the bottom layer, along with electric field crosstalk, can cause printing defects. This paper introduces two innovative modules into the conventional printing process: a deep reinforcement learning framework for dynamic height adjustment and a 'run-to-run' data control strategy. The Soft Actor-Critic (SAC) deep reinforcement learning algorithm is employed to develop a strategy for regulating process parameters and the height of the printed structure. This approach allows for the precise control of the multilayer structure height, compensating for the impact of accumulated charges in complex electric fields. Using the electrofluidic printing platform, a three-layer structure was printed on pixel pits with HIL, HTL, and EML inks. This printing process yielded OLED devices with 1200 ppi resolution, adjustable volume, and a stable structure. The uniformity of the printed layer height achieved 96.3%. Furthermore, Micro-OLEDs devices with a resolution of 3600 ppi were successfully fabricated, meeting the resolution requirements for most current display panels.