TL;DR
This study uses a feedforward neural network to identify key image features, notably the contour height of impacting drops, that distinguish splashing from nonsplashing drops, revealing nonintuitive insights into drop impact dynamics.
Contribution
The paper introduces a novel application of neural network visualization to identify the contour height as a key feature in drop splashing behavior, a previously unreported insight.
Findings
Neural network achieved over 96% classification accuracy.
Contour height of the main drop body is a crucial feature for splashing prediction.
Contour height remains important even when secondary ejected droplets are excluded.
Abstract
This article reports nonintuitive characteristic of a splashing drop on a solid surface discovered through extracting image features using a feedforward neural network (FNN). Ethanol of area-equivalent radius about 1.29 mm was dropped from impact heights ranging from 4 cm to 60 cm (splashing threshold 20 cm) and impacted on a hydrophilic surface. The images captured when half of the drop impacted the surface were labeled according to their outcome, splashing or nonsplashing, and were used to train an FNN. A classification accuracy higher than 96% was achieved. To extract the image features identified by the FNN for classification, the weight matrix of the trained FNN for identifying splashing drops was visualized. Remarkably, the visualization showed that the trained FNN identified the contour height of the main body of the impacting drop as an important characteristic differentiating…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
