Effective recognition and prediction of spraying patterns for electrohydrodynamic (EHD) process are extremely important for its applications in high quality micro/nanoparticles preparation, chip coating, droplet-reactor design, and high precision printing, etc. In this study, six distinct spray patterns, namely dripping, spindle, cone-jet, rotational jet, atomization, and skew jet-atomization, were classified through experiments. Subsequently, 30,000 images were obtained to train a convolutional neural network (CNN) model for recognizing EHD spraying patterns, which exhibited a remarkable accuracy of 99.80%. The CNN model was used to recognize the patterns across a range of experimental variables. Dimensionless groups were established and the generalized spraying pattern maps were drawn efficiently via the model. Finally, a database consisting of 11,650 experimental data points was constructed to train a deep neural network (DNN) model, aiming to reduce the number of experiments. The DNN model with an accuracy of 95.88% was employed to predict the spraying patterns, by which a rapid but comprehensive analysis of the impact of different conditions was achieved.

中文翻译：

---

基于深度学习的电流体动力系统喷雾模式识别与预测


电流体动力学（EHD）工艺的喷雾模式的有效识别和预测对于其在高质量微/纳米颗粒制备、芯片涂层、液滴反应器设计和高精度打印等方面的应用极其重要。在本研究中，六种不同的喷雾模式通过实验分为滴流、纺锤、锥射流、旋转喷射、雾化和斜射流雾化。随后，获得了 30,000 张图像来训练用于识别 EHD 喷涂模式的卷积神经网络 (CNN) 模型，其准确率高达 99.80%。 CNN 模型用于识别一系列实验变量的模式。建立无量纲群，并通过模型有效绘制广义喷洒模式图。最后，构建了由11,650个实验数据点组成的数据库来训练深度神经网络（DNN）模型，旨在减少实验数量。采用精度为95.88%的DNN模型来预测喷洒模式，实现了对不同条件影响的快速而全面的分析。