Machine Learning Assisted Spraying Pattern Recognition for Electrohydrodynamic Atomization System

In this work, two machine learning (ML) models for recognizing electrohydrodynamic (EHD) spraying patterns are developed to guide the process operation to achieve stable cone-jet mode directly. To this end, the EHD spraying patterns are first divided into three categories, namely, dripping, stable cone-jet, and unstable cone-jet. A database consisting of 86 140 EHD spraying patterns data are used to build the recognition models. The artificial neural network (ANN) and support vector machine (SVM) models are trained through the collected data to give excellent performance in prediction of EHD spraying patterns. With the testing set data, the accuracy of the EHD spraying patterns recognition is 99.611% for the ANN model, and the result is 99.867% for the SVM model. The performance of these two models are further evaluated by comparing the recognition rates of the EHD spraying patterns with experimental data from 22 literature references, and the results show that the SVM model gives a better performance. Lastly, the SVM model is employed to predict the full picture of EHD spraying patterns. Four pattern maps are drawn with the assistance of the SVM model to reveal the effect of volumetric flow rate (Q), tip-to-collector distance (L), polymer concentration (C), nozzle inner diameter (D-in), and applied voltage (V).

Automated summary

In this study, two machine learning models were developed to recognize electrohydrodynamic spraying patterns and guide process operations. The models achieved high accuracy in prediction, with the SVM model performing better in practical applications.