Electrohydrodynamic jet printing is a highly promising technology for the fabrication of three-dimensional micro/nanoscopic structures, but the advancement of this technology is hindered by the insufficient understanding of many aspects of its mechanisms. Here we conduct a numerical investigation on high-frequency (∼1 kHz) pulsating electrohydrodynamic jet at low electric Bond numbers (${Bo}_e$ = 0.15–0.7). By analyzing the entire jetting process using the voltage distribution, electric charge density, and flow field obtained from the numerical results, we overcome the limitations of experimental approach and demonstrate the influences of electric voltage ($\Phi$ ), nozzle-to-substrate distance ($H$ ), and liquid surface tension coefficient ($\gamma$ ) on the dynamic behaviors and durations of the three jetting stages: (1) cone formation, (2) jetting, and (3) meniscus oscillation. Furthermore, as a measure of the relative significance of the electric force to the surface tension force, the impacts of ${Bo}_e$ on the jetting process are also examined. Results show that some critical aspects of the pulsating jetting process are closely related to ${Bo}_e$ : (1) the transitional values of ${Bo}_e$ between the four observed jetting regimes on the variations of $\Phi$ , $H$ , and $\gamma$ apply to all three parameters; (2) the nondimensionalized Taylor cone length scales with ${Bo}_e$ according to a power law; (3) the jetting processes that have similar ${Bo}_e$ collapse onto a universal profile. These new findings of pulsating electrohydrodynamic jet provide a useful supplement to the currently inadequate comprehension of the complicated electrohydrodynamic jet printing process.

Electrohydrodynamic (EHD) jet printing is a highly effective technique for micro/nanoscale three-dimensional manufacturing. However, due to the complicated electrohydrodynamic mechanisms behind liquid deformation and jet emission, the printing process with remarkable droplet consistency and excellent controllability is still under investigation. In this work, a numerical analysis is conducted on EHD jet printing under constant and step change of electric voltages. We first examine constant-voltage-based pulsating EHD jet printing and explain the impacts of voltage on the regimes, deposited droplet volumes, and durations of the three key printing stages, namely, cone formation, jetting, and jet/meniscus retraction and oscillation. After that, we carry out a comprehensive investigation on EHD jet printing under various step changes of voltages while focusing on the jet behaviors at the voltage switch and after detaching from the Taylor cone. With the assistance of the electric field distribution, interface charge density, velocity fields, and very clear liquid motion images obtained from the numerical data, we fully inspect the pulsed printing processes and elucidate the influences of the pulse time, bias voltage, and peak voltage on the printing behaviors, durations of the three printing stages, and deposited droplet volumes. Finally, based on the obtained results, we make a comparison of the printing outcomes between these two techniques. The findings discovered in this work can be used for advancing the understanding and controlling methods of this complicated but very useful manufacturing technology.

This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results. The features based on wavelet packet decomposition are derived from the original signals. Then the intrinsic geometric features extracted by WKECA are fed into the support vector machine (SVM) classifier to recognize different operating conditions of bearings, and we obtain the overall accuracy (97%) for the experimental samples. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

Rolling bearings as vital components are present ubiquitously in manufacturing machines and systems, timely fault diagnosis is of great importance in avoiding serious catastrophe, and a variety of methods have been developed to detect faults of rolling bearings. In this study, we introduce labels to adjust the distance of classes and propose a method called supervised kernel entropy component analysis for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis which attempts to preserve the Renyi entropy of the data set after dimension reduction and presents a good performance for nonlinear feature extraction. Simulation and experiments are carried out to verify the method. The intrinsic geometric features derived from original vibration signals are extracted, and then classified by support vector machine. The results prove the feasibility and effectivity of the supervised kernel entropy component analysis in comparison with other similar methods, demonstrating its potentiality for fault diagnosis of rolling bearings.