Evaluation of splashing threshold of impacting droplets based on automated experiments

Splashing of impacting droplets is an important phenomenon which frequently happens both in nature and industry. Thus, many studies have been carried out to model the splashing threshold such as K-Parameter proposed by Mundo et al (1995) and the splashing threshold model based on aerodynamic lift force by Riboux et al. (2014).

However, the judgement of splashing threshold heavily relies on human eyes, which is subjected to human error. Moreover, due to the insufficient amount of data, statistical errors are likely to occur. Therefore, the purpose of our study is to automate a drop-impact experiment for obtaining a large amount of data. Also, we improve the proposed splashing threshold models into a universal model that is robust in various impact conditions by reducing these errors. The automated experimental system is built with with a robot arm, a high-speed camera, and actuators. This system can get a massive amount of high-quality image sequences of impacting droplets. The results obtained in this study is analyzed by AI for evaluating exisiting splashing thresholds.