Characterization of electrical current and liquid droplets deposition area in a capillary electrospray

Si, Yuchen; Yang, Yingjie; Martel, Myra; Zhang, Lifeng; Kirychuk, Shelley; Predicala, Bernardo; Guo, Huiqing

doi:10.1016/j.rineng.2021.100206

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…To accurately extract data from figures, the Web Plot Digitizer tool has been utilized. [19] Data was extracted from 13 journal articles [20][21][22][23][24][25][26][27][28][29][30][31][32] involving EHD systems operating in both spraying and DOD modes. A detailed breakup of the different data characteristics that have been sourced from each of these articles is listed in Table 1.…”

Section: Data Collectionmentioning

confidence: 99%

Machine Learning‐Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Singh,

Rai,

Subramanian

2023

Adv Eng Mater

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Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high‐quality datasets at appropriate volumes. This article extends the suitability of such ML‐enabled approaches to a new technological domain of EHD spraying and drop‐on‐demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree‐based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.

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