The high frequency, low amplitude wing motion that mosquitoes employ to dry their wings inspires the study of drop release from millimetric, forced cantilevers. Our mimicking system, a 10-mm polytetrafluoroethylene cantilever driven through ±1 mm base amplitude at 85 Hz, displaces drops via three principal ejection modes: normal-to-cantilever ejection, sliding and pinch-off. The selection of system variables such as cantilever stiffness, drop location, drop size and wetting properties modulates the appearance of a particular ejection mode. However, the large number of system features complicate the prediction of modal occurrence, and the transition between complete and partial liquid removal. In this study, we build two predictive models based on ensemble learning that predict the ejection mode, a classification problem, and minimum inertial force required to eject a drop from the cantilever, a regression problem. For ejection mode prediction, we achieve an accuracy of 85% using a bagging classifier. For inertial force prediction, the lowest root mean squared error achieved is 0.037 using an ensemble learning regression model. Results also show that ejection time and cantilever wetting properties are the dominant features for predicting both ejection mode and the minimum inertial force required to eject a drop.

中文翻译：

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通过机器学习对阻尼、阻尼机翼的液滴喷射进行预测建模

蚊子用来干燥翅膀的高频、低振幅的翅膀运动激发了对毫米、强制悬臂的液滴释放的研究。我们的模拟系统是一个 10 毫米的聚四氟乙烯悬臂，以 85 Hz 的频率通过 ±1 毫米的基本振幅驱动，通过三种主要的弹出模式替换液滴：正常到悬臂的弹出、滑动和夹断。悬臂刚度、液滴位置、液滴尺寸和润湿特性等系统变量的选择会调节特定喷射模式的外观。然而，大量的系统特征使模态发生的预测以及完全和部分液体去除之间的过渡变得复杂。在这项研究中，我们建立了两个基于集成学习的预测模型，用于预测弹射模式，一个分类问题，以及从悬臂上弹出液滴所需的最小惯性力，这是一个回归问题。对于弹出模式预测，我们使用装袋分类器实现了 85% 的准确率。对于惯性力预测，使用集成学习回归模型实现的最低均方根误差为 0.037。结果还表明，喷射时间和悬臂润湿特性是预测喷射模式和喷射液滴所需的最小惯性力的主要特征。