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This paper reports the features of a splashing drop on a solid surface andthe temporal evolution, which are extracted through image-sequenceclassification using a highly interpretable feedforward neural network (FNN)with zero hidden layer. The image sequences used for training-validation andtesting of the FNN show the early-stage deformation of milli-sized ethanoldrops that impact a hydrophilic glass substrate with the Weber number rangesbetween 31-474 (splashing threshold about 173). Specific videographingconditions and digital image processing are performed to ensure the highsimilarity among the image sequences. As a result, the trained FNNs achieved atest accuracy higher than 96%. Remarkably, the feature extraction shows thatthe trained FNN identifies the temporal evolution of the ejected secondarydroplets around the aerodynamically lifted lamella and the relatively highcontour of the main body as the features of a splashing drop, while therelatively short and thick lamella as the feature of a nonsplashing drop. Thephysical interpretation for these features and their respective temporalevolution have been identified except for the difference in contour height ofthe main body between splashing and nonsplashing drops. The observationreported in this study is important for the development of a data-drivensimulation for modeling the deformation of a splashing drop during the impacton a solid surface.

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Features of a Splashing Drop on a Solid Surface and the Temporal  Evolution extracted through Image-Sequence Classification using an  Interpretable Feedforward Neural Network